Using epigenomics data to predict geneexpression in lung cancerJeffery Li1dagger Travers Ching23dagger Sijia Huang23 Lana X Garmire23
From 10th International Symposium on Bioinformatics Research and Applications (ISBRA-14)Zhangjiajie China 28-30 June 2014
Abstract
Background Epigenetic alterations are known to correlate with changes in gene expression among variousdiseases including cancers However quantitative models that accurately predict the up or down regulation ofgene expression are currently lacking
Methods A new machine learning-based method of gene expression prediction is developed in the context oflung cancer This method uses the Illumina Infinium HumanMethylation450K Beadchip CpG methylation array datafrom paired lung cancer and adjacent normal tissues in The Cancer Genome Atlas (TCGA) and histone modificationmarker CHIP-Seq data from the ENCODE project to predict the differential expression of RNA-Seq data in TCGAlung cancers It considers a comprehensive list of 1424 features spanning the four categories of CpG methylationhistone H3 methylation modification nucleotide composition and conservation Various feature selection andclassification methods are compared to select the best model over 10-fold cross-validation in the training data set
Results A best model comprising 67 features is chosen by ReliefF based feature selection and random forestclassification method with AUC = 0864 from the 10-fold cross-validation of the training set and AUC = 0836 from thetesting set The selected features cover all four data types with histone H3 methylation modification (32 features) andCpG methylation (15 features) being most abundant Among the dropping-off tests of individual data-type basedfeatures removal of CpG methylation feature leads to the most reduction in model performance In the best model19 selected features are from the promoter regions (TSS200 and TSS1500) highest among all locations relative totranscripts Sequential dropping-off of CpG methylation features relative to different regions on the protein codingtranscripts shows that promoter regions contribute most significantly to the accurate prediction of gene expression
Conclusions By considering a comprehensive list of epigenomic and genomic features we have constructed anaccurate model to predict transcriptomic differential expression exemplified in lung cancer
BackgroundEpigenetics is a rapidly expanding biological field recentlyAberrant epigenetic modifications are associated withmany different diseases including cancers and neurodeve-lopmental disorders [1] Much work has demonstrated thatepigenetic regulation plays an important role in geneexpression among other mechanisms such as transcriptionfactor regulation Advances in high throughput methods
such as methylation arrays CHIP-Sequencing gene expres-sion microarray and RNA-Sequencing have enabledresearchers to better understand the relationship betweenepigenetic modification and gene expression at the genomescale Coupling with the progress in experimental metho-dology we have witnessed a wealthy growth of bioinfor-matics tools to analyze the epigenetics patterns [2-4]DNA methylation and histone modification are two
major mechanisms of epigenetic regulation The mostwidely researched type of DNA methylation in human isthe cytosine methylation of CpG islands and their asso-ciated regions such as CpG shores [5] CpG methylationoccurs genome-wide in regions related to protein coding
Correspondence lgarmirecchawaiiedudagger Contributed equally2Molecular Biosciences and Bioengineering Graduate Program University ofHawaii at Manoa Honolulu HI 96822 USAFull list of author information is available at the end of the article
Li et al BMC Bioinformatics 2015 16(Suppl 5)S10httpwwwbiomedcentralcom1471-210516S5S10
copy 2015 Li et al licensee BioMed Central Ltd This is an Open Access article distributed under the terms of the Creative CommonsAttribution License (httpcreativecommonsorglicensesby40) which permits unrestricted use distribution and reproduction inany medium provided the original work is properly cited The Creative Commons Public Domain Dedication waiver (httpcreativecommonsorgpublicdomainzero10) applies to the data made available in this article unless otherwise stated
genes (promoters exons UTRs etc) as well as in certainintergenic regions It has been shown that CpG methyla-tion tends to occur in promoters located upstream of thetranscription starting site [6] and increased methylation(hypermethylation) in the promoter is negatively asso-ciated with the gene expression level[1] On the otherhand CpG methylation in gene bodies seems to be posi-tively associated with gene expression [1] In cancers cellsmassive global loss of DNA methylation (hypomethyla-tion) has been observed and such hypomethylation in pro-moters can activate aberrant expression of oncogenes [7]Much new information has been gained through therecently developed methods such as Illumina InfiniumHumanMethylation450 arrays that enable the detection ofCpG methylation throughout the different locations asso-ciated with over 99 of protein coding genesHistone modification is another type of important epi-
genetic modification [1] Histones are the core ofnucleosomes that DNA sequences wrap around All his-tones are subject to some level of methylation or acety-lation which would either open up or close the localchromatin structures to enable or repress gene expres-sion Among them Histones 3 (H3) has various kinds ofmethylation and they serve as well-studied markers forgene expression status For example Histone 3 Lysine 4tri-methylation (H3K4Me3) in the promoter region is anindicator of active gene transcription and Histone 3Lysine 36 tri-methylation (H3k36me3) is associated withtranscription elongation Oppositely Histone 3 Lysine27 tri-methylation (H3k27me3) may repress geneexpression Even more complicated histone modifica-tion markers interact with DNA methylation and theconsequent patterns of gene expression are the com-bined effects of their interactions The genomic assayssuch as CHIP-sequencing have enabled the generationof large amount of histone modification dataAlthough integrative analyses on gene expression and
epigenetics regulation abound throughout the literature[89] it is our observation that quantitative models whichuse epigenetic information to accurately predict the up ordown regulation of gene expression are currently lackingA frequent question that a biologist would ask whenmethylation data are available but the gene expressiondata are missing is how the epigenetic changes of a genemay affect the expression of this gene to be either up ordown regulated This report is aimed to fill in this gapand provide the users with a model that allows them toestimate the consequence of epigenetic modification ongene expression when the data for the latter are not avail-able Towards this goal we have built a classification pre-dictor for gene expression using the machine learningapproach This model examines a large set of CpG methy-lation data histone modification data and genome dataand accurately predicts differential expression of RNA-Seq
transcriptome by taking advantage of the publicly availabledata from the TCGA Project (lung cancer) and theENCODE project
MethodsData setsSeveral types of high throughput data were used to extractfeatures or classification responses These include the CpGmethylation array data from 50 paired cancer and adjacentnormal tissues three types of histone marker CHIP-Seqdata from cancer and normal cell lines genomic nucleo-tide sequence and conservation data and RNA-Seqexpression data from samples that have coupled methyla-tion data
Data processingMethylation dataThe Cancer Genome Atlas (TCGA) Methylation data fromIlluminarsquos Infinium HumanMethylation450 Beadchip (Illu-mina 450k) were used to extract CpG methylation relatedfeatures according to their annotation file The genomiccoordinates of CpG their exons and coding regions wereobtained from the Illumina annotation file Since the anno-tation file only provided information of transcripts exonsand coding DNA sequences (CDS) we re-annotated theprotein coding genes using the Illumina iGenomes hg19Refseq annotation in order to extract more comprehensiveinformation from other regions of the transcripts allintrons (with special categories for the first and last intron)as well as first and last exons untranslated regions in the 5rsquoand 3rsquo direction (5rsquo UTR and 3rsquo UTR respectively) and aldquosingle exonrdquo or ldquosingle intronrdquo designation for transcriptsthat only had a single exon or single intronHistone dataThree sets of histone marker CHIP-Seq data H3k4me3H3k27me3 and H3k36me3 were considered from twocell lines A549 cell line (02 EtOH treatment) fromthe lung carcinoma tissue and SAEC normal lungepithelial cell line (no treatment) Raw CHIP-Seq datawere downloaded from the Broad InstituteBernsteinLab at the Massachusetts General HospitalHarvardMedical School and the University of Washington incollaboration with the ENCODE project via the UCSCgenome browser at httpgenomeucscedu [1011] Theraw reads were processed in-house to ensure consis-tency of all normalization procedures Raw data werefirst aligned to hg19 using bowtie2 [12] followed byremoval of duplicated reads using the Samtools toolkit(specifically the ldquormduprdquo tool) [13] The aligned readswere intersected with the relevant segments of the tran-script as annotated in the previous section using theBedtools toolkit (specifically the ldquomulticovrdquo tool) [14] Acustom R script was used to normalize the data overtotal number of reads after removing PCR duplicates
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Human genome dataNucleotide composition data were extracted from hg19genome FASTA files downloaded from the UCSC gen-ome browser Conservation scores across three classesof species vertebrates primates and placental animalswere also considered PhastCons46Way scores weredownloaded from the UCSC genome browser [1115]Conservation scores were then intersected with the rele-vant segments of the transcripts using a custom Perlscript in order to extract conservation featuresRNA-seq dataRNA-Seq gene expression data from lung cancer sampleswith coupled CpG methylation data were downloadedfrom TCGA Research Network httpcancergenomenihgov Lung adenocarcinoma and lung squamous cell carci-noma data were combined for this project as they are twosubtypes of non-small cell lung cancer Differential expres-sion analysis was done with the DESeq2 package in R [16]In cases where multiple transcripts are mapped to thesame Refseq ID the geometric mean of the differentialexpression results was used to represent the gene levelexpression In the case that any of these read counts waszero the counts from all transcripts were artificiallyincreased by one in order to calculate the geometric meanfollowed by final subtraction of one The expression ofa gene was then classified as binary outcomes either up-re-gulated or down-regulated once it passed two thresholds1) having an adjusted p value lt 05 after Holmrsquos multiplehypothesis test [17] and 2) having an absolute value of log2fold change greater than 1 As a result 2874 genes wereselected as ldquodifferentially expressedrdquo genes
Feature extractionThe extracted features are categorized into four majorsub-groups All features were considered on a segment-wise basis (see Data Processing) unless noted otherwiseCpG Methylation featuresDifferential expression of the methylated CpG sites wasprocessed using the limma library in R Specifically thefunction toptable was used to determine the log foldchange (logFC) between the cancer and normal tissues aswell as the average methylation (avgMval) of each CpGsite across the two types of tissue [18] A positive logFCindicates hypermethylation whereas a negative logFCindicates hypomethylation Additional segment-basedfeatures were also considered These include the numberof hypermethylated (numHyper) and hypomethylatedprobes (numHypo) on a segment of a given transcriptFor example first_exon_numHyper refers to the numberof hypermethylated probes on the first exon Two othertypes of features are the average of logFC and avgMval ofall CpG probes on a segment of the transcript eg theaverage logFC of all probes on the first exon of a giventranscript (first_exon_avglogFC)
Special effort was paid to compute distances of CpGprobes to exon-exon junctions Given that one or moreCpG sites may exist on the individual exon segments of atranscript (including the first and last exons) transcript-level maximum minimum and average distances of anyhyperhypo-methylated probe to the nearest 5rsquo or 3rsquoexon-exon junction were computed (maxHypoTo5 min-HypoTo5 avgHypoTo5 maxHypoTo3 minHypoTo3avgHypoTo3 maxHyperTo5 minHyperTo5 avgHy-perTo5 maxHyperTo3 minHyperTo3 and avgHyperTo3)Histone marker modification featuresAfter the alignment of raw histone marker data (seeData Processing) the aligned histone marker reads wereintersected with the segments of each transcript usingthe multicov function from the BEDTools package [19]The histone reads were then normalized per 1000 bplength of each segment per 1 million aligned readlibrary Similar to the CpG methylation features the his-tone marker modification features were extracted on asegment-by-segment basis Initials are used to representthe individual cell lines where the features come fromA for the A549 cell line and S for the SAEC cell lineFollowing the initial is a number representing the speci-fic histone H3 methylation marker 4 for H3k4me3 27for H3k27me3 and 36 for H3k36me3 As a result fea-tures are named as segment_cell type and histone modi-fication type (eg first_exon_A4) In order to comparehistone modification between the cancer and non-cancercell types the differences of the reads between themwere divided by the average of the two (eg a featurenamed first_exon_A4_minus_S4_divavg)Nucleotide featuresIn each segment of the transcript four different types ofnucleotide features were extracted single nucleotidecomposition dinucleotide composition trinucleotidecomposition and the length of each segment Nucleo-tide sequences of Hg19 reference genome were pro-cessed using the Biostrings library in R [20]Conservation featuresConservation score per segment was calculated as thearithmetic mean of the conservation score per nucleo-tide in that segment Three separate sets of conservationscores with different comparative species were extractedfrom UCSC genome browser - vertebrate primate orplacental Thus features such as first_exon_vertebrateemerge from this set
Feature selectionThree feature selection methods were considered Cor-relation Feature Selection (CFS) [21] Gain Ratio [22]and ReliefF [23]CFS is based on mutual information a non-linear
measure of correlation CFS selects an approximatelyoptimal set of features to maximize the relevance and
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minimize redundancy Relevance is the correlation of afeature to the class (up-regulated or down-regulatedgene expression) measured by mutual informationwhereas redundancy is the correlation between two fea-tures Redundancy between selected features is mini-mized to keep the number of selected feature smallThe Gain Ratio is an improved method of Information
Gain (IG) Both feature selection methods employ adecision tree in their respective algorithms The GainRatio by name is a ratio of IG but it overcomes thebias of IG which favors features with more dataReliefF is an improved feature selection method from
Relief Relief uses the Manhattan distance of its nearesthit and miss from a random instance to continuouslyupdate a weight vector which is then used to calculate arelevance score Features above a certain relevancethreshold are considered ldquoselectedrdquo [24] ReliefF improveson Relief in several ways including two improvementsparticularly important for this report First ReliefFextends Relief to be able to handle incomplete or partialdata Second ReliefF searches for k near-hits and near-misses and takes their averages as opposed to one near-est hit or miss from Relief k =10 was sufficient to obtainsatisfactory results [23]CFS is the only method that has a built-in system for
selecting the number of features Gain Ratio and ReliefFboth work as ranker systems meaning every input has amatching respective ranked output In order to ensurefairness between feature selection methods we matchedthe numbers of selected features from Gain Ratio andReliefF to be the same as determined by CFS
Model evaluationThe data were split into training and testing sets Thetraining set constituted 80 of the up-regulated anddown-regulated genes and the testing set constituted theremaining 20 genes The training data set underwent10-fold cross validation on various combinations of fea-ture selection and classification methods in order toobtain the best modelAfter determining the best model two sets of drop-off
tests were conducted The first set of tests considered theeffect of data types including nucleotide compositionhistone markers and methylation data on the perfor-mances of sub-models The second set of drop-off testsconsidered the effects of different segments on tran-scripts including gene body exons introns UTRsTSS1500 and TSS200 on the methylation CpG methyla-tion data based sub-models For each drop-off test a setof features was removed from the original input featuresprior to the feature selection and classification Subse-quently the same ReliefF feature selection and RF classifi-cation for the drop-off tests were performed as describedin the previous Feature Selection section
SoftwareWeka 3 data mining software [25] was used for featureselection classifier training and evaluation Various Rpackages were used including Corrplot for generationof the correlation matrix [26] and ROCR for ROCcurves [27] The classification model is available athttpsgithubcomlanagarmireepiPredictor
ResultsSummary of input data and featuresFour types of input data were used to extract the fea-tures including the Illumina 450K CpG methylationarray data from cancer and normal tissues three typesof histone H3 marker CHIP-Seq data from cancer andnormal cell lines genomic nucleotide sequence and con-servation data and RNA-Seq gene expression data fromsamples with coupled CpG methylation data In totalwe calculated 1424 features and summarized the fea-tures by column These features can be divided into twocategories (Table 1) (1) data type based features includ-ing average CpG methylation average methylation logfold change number of hyperhypo-methylated probesmono-nucleotide di-nucleotide and tri-nucleotide com-position histone H3 methylation CHIP-Seq reads andPhastcon conservation scores (2) segment based CpGmethylation features from Illumina 450K BeadChipannotations upstream of the transcription start site(TSS) 1500 TSS200 5rsquo and 3rsquo UTRs exonintron bodyfirst and last exonintron single exonintron and fulltranscript (Figure 1 and Table 1)
Model selection and evaluationThe model uses 2298 gene data points in the training setwith an additional 576 genes kept in the testing set Threedifferent feature selection methods were evaluated in com-bination with five classification methods using 10-foldcross-validation on the training data set (Figure 2) Thethree feature selection methods are correlation-based fea-ture selection (CFS) ReliefF and Gain Ratio In mostcases with combined classification methods except forGaussian SVM ReliefF gives the best AUCs among thethree feature selection methods Among the five classifica-tion methods that we considered namely Gaussian SVMlinear SVM Logistic Regression Naiumlve Bayes and RandomForest the two non-linear methods (Gaussian SVM andRandom Forest) show superior performances to the otherlinear classifiers (Logistic Regression linear SVM andNaiumlve Bayes) This indicates that interactions exist amongthe selected features However the differences are not verybig suggesting that the decision boundary is close to lin-ear Given that the model based on ReliefF feature selec-tion and Random Forest classification gives the best AUCof 0864 it is selected as the best model for the rest of theproject Similarly a ReliefF and Random Forest based
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Table 1 The list of all features considered prior to feature selectionAverageM value(Methylation)
model has the best predictive performance on the 20holdout data set with an AUC of 0836
Analysis of selected featuresA total of 67 features are selected by the best modelspanning all four types of genomic and epigenomic data
We first explored the relationship among the selectedfeatures Using hierarchical clustering on absolute corre-lation values between features (Figure 3A) we found thatthe selected features tend to cluster by the data type asexpected For example the conservation features in thecoding regions (CDS) are grouped together and so are
Figure 1 Segments associated with protein coding genes Features considered to predict differential gene expression are depicted on asegment-by-segment basis Segments are determined based on the annotations of Illumina Infinium Human Methylation 450K Beadchip Arraywith augmentations on segments located in gene bodies From 5rsquo to 3rsquo end of the protein coding genes listed are transcription starting sites(TSS) upstream up to 1500 bp (TSS 1500) and 200 bp (TSS 200) first exon which may include 5rsquo UTR first intron exon body last intron and lastexon which may include 3rsquo UTR A full transcript region is determined as the UTRs and coding region together
Figure 2 Performance comparison of models with various feature selection and classification methods The Areas Under the Curve (AUC)of ROC are used as the metric to compare the performance of models with different combinations of feature selection (CFS Gain Ratios andReliefF) and classification (Gaussian SVM Linear SVM Logistic regression Naiumlve Bayes and Random Forest) on the training data with 10 foldcross-validation The model with ReliefF based feature selection and Random Forest classification is selected as the best model
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most methylation features As expected the CpG islandswithin the promoter are very important for the predictionof gene expression as demonstrated by the three selectedand highly correlated features CG composition featuresTSS200_GC TSS200_CG and TSS200_CGGThe largest group of selected features is the histone
modification features (32 features) followed by the methy-lation features (15 features) (Additional File 1) Theselected features underscore the importance of histoneepigenetic modification in the regulation of gene expres-sion Likewise the importance of methylation features isevident especially for the featured arising from TSS 5rsquoUTR and first exons Interestingly several methylation fea-tures (TSS1500_avgMval first_exon_avgMval andUTR5_avgMval) are clustered with histone modificationfeatures suggesting collinearity between these two types offeatures as shown by others [2829]On the other hand when features are categorized by
location relative to the transcripts (Additional File 1) theTSS200 has the most number of features (13 features)and TSS1500 has 6 features selected for this regionTogether the promoter comprises 28 of all the selectedfeatures This confirms the previously well-known impor-tance of the promoter region for the epigenetic regulationof gene expression [3031] Additionally CDS has the sec-ond highest number of features being selected highlight-ing its significance in regulating gene expression [30]
We also calculated the correlation of each feature togene expression and plotted the top 15 features mostrelevant to gene expression prediction (Figure 3B) Noneof the features have correlations higher than 045 sug-gesting that no single feature is a dominant predictorfor gene expression These features are either histonemodification (11 features) or methylation features (4 fea-tures) consistent with the previous observation on thesignificance of these two types of features
Evaluation of features by data typeTo determine the contribution of different types of fea-tures to gene expression we tested the performance ofmodels when a subset of features from the same datatype were dropped We present the results of four mea-sures of model performance AUC accuracy F-measureand Matthewrsquos correlation coefficient (MCC) (Figure 4)Dropping any individual feature set of nucleotide compo-sition histone modification or CpG methylation did notseem to have a large effect on the model performanceindicating that there is redundancy between feature setsThe sub-model performance for the dropping-off of asingle feature set from the full model is in the followingorder nucleotide composition removal gt histone modifi-cation removal gt CpG methylation removal Thus drop-ping methylation features had the largest effect amongindividual feature set as the AUC decreases from 0864
Figure 3 Top fifteen features from the best model (a) The clustering results on the absolute values of Pearsonrsquos correlation coefficients from67 selected features by the best model The names of different type of features are labeled by different colors Note the length of a segment islisted out separately (b) List of top fifteen features selected by ReliefF feature selection and sorted by their correlation to the classification ofdifferential gene expression
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in the full model to 0832 in the training set as well asfrom 0836 to 0810 in the testing set Likewise MCCupon single feature set drop-off shows the largest pro-portional change among the four performance measuresand decreases from 056 to 049 on the training set and051 to 045 on the testing setWe also compared the effect of removing both nucleo-
tide and histone features on model performance as com-pared to removing either of them alone As expectedremoving both nucleotide and histone features gives thelowest AUCs lowest accuracies and lowest F-measures in
both training and testing sets However it leads to higherMCC than removing just histones does in the testing setThis suggests that there might be some overfitting withregards to the nucleotide feature set which accounts forthe majority (83) of features prior to feature selection
Evaluation of CpG methylation features by locationsrelative to transcriptsGiven that removing methylation features causes themost reduction of model performance among the singlefeature set drop-off (Figure 5) we next asked the
Figure 4 Evaluation of features generated from various data types (a-b) Effects of feature set drop-off on ROC curves from the 10-foldcross-validation training set (a) and testing set (b) (c) Effects of feature set drop-off on other four metrics AUC Accuracy F-measure and MCCin the training set and testing set
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question of the relative importance of each methylationfeature categorized by genomic location We performeddrop-off tests by sequentially removing features in eachgenomic location category We first removed the featuresfrom the first exons and first introns (as they are close to
the TSS) then from gene bodies including exons intronsand UTRs and lastly from TSS1500 region such that onlyTSS200 features were kept At each step we re-performedfeature selection and model construction using theremaining methylation features
Figure 5 Evaluation of methylation features by segment (a-b) Effects of segment-based methylation feature set sequential drop-off on ROCcurves from the 10-fold cross-validation training set (a) and testing set (b) (c) Effects of segment-based methylation feature set sequential drop-off on other four metrics AUC Accuracy F-measure and MCC in the training set and testing set
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All categories of genomic locations provide relevantuseful information that contributes to better predictionof gene expression as each of the sequential feature setdrop-off decreased the performance of the model in bothtraining and testing sets Compared to the removal offirst exon and intron regions and removal of the UTRsand the rest of the gene body removal of TSS1500 leadsto the largest reduction in all four metrics confirmingthe importance of the promoter region in regulating geneexpression Even when only TSS200 features are consid-ered an AUC of 0638 and 0636 are obtained in the test-ing and training sets respectively suggesting that CpGmethylation status in TSS200 is still somewhat predictiveof gene expression However a more accurate predictionusing methylation features should arise from all locationsassociated with the transcripts
DiscussionThe need to build predictive models of gene expressionfrom epigenomics dataAlthough currently integrative analyses between geneexpression and epigenetic modification exist we havefound that quantitative models using epigenetic informa-tion to accurately predict the up or down regulation ofgene expression are currently lacking There are oftencases where researchers can only obtain reliable epige-netics data but not gene expression data For example ifthe samples are archived and processed by FFPE (Forma-lin-fixed paraffin-embedded) one can still perform epige-nomics measurements but not the gene expressionexperiments due to the degradation of mRNA in the sam-ples More importantly a predictive method such as ourscan efficiently facilitate the bench scientists to narrowdown the candidate lists and conduct gene expression vali-dation especially when the epigenetics information is theonly data handy
Selected features and their relevance to gene expressionAll four types of data (CpG methylation histone H3modification nucleotide sequence and conservation)exist in the 67 features that are selected by the bestmodel indicating that all of them contribute to the accu-rate prediction of gene expression Moreover selectedfeatures of the same data type tend to cluster together onthe correlation matrix among the features suggestingthat the relationship within the same data type is closerthan the relationship between different data types Asexpected histone modification and CpG methylation fea-tures are the largest two groups among the four types ofdata signifying their importance to predict gene expres-sion Since nonlinear classification methods performslightly better than linear classification methods it sug-gests that interactions do exist between different types ofdata This is supported by numerous literatures that
enzymes responsible for CpG methylation also interactwith histone modification events [3233]Besides the value of predicting gene expression our
models also provide insights into the relative importanceof different epigenomicsgenome data as well as thegenomic locations We found that CpG methylation fea-tures have more predictive values for differential geneexpression compared to the three types of histone H3modification data Although other kinds of histone mod-ification data can also be obtained to increase the pre-dictive values of histone modification data it is muchmore costly to obtain them relative to the CpG methyla-tion data (the cost of CHIP-Seq on each of the histonemodification marker is similar to an entire CpG methy-lation array) Therefore practically speaking when thebudget is a constraining factor we suggest that assayson CpG methylation should be considered with priorityin predicting differential gene expression Moreover theresults of our models demonstrate that all genomic loca-tions relative to each transcript including promotersexons and gene bodies provide useful information topredict gene expression alternation Although the CpGmethylation signals from the promoters region are moreimportant the methylation signals from other regionssuch as exons introns and UTRs are indicative ofchanges in the gene expression as wellWorth noticing a lot of features that are extracted on
methylation and histone modification are naturallybased on the annotations from Illumina 450K array plat-form for DNA methylation There may be bias on thenumber of features that are hand coded in the modelTo address potential issue we changed TSS200_GC toTSS150_GC in our model and obtained an AUC = 861(compared to 0864) for cross fold validation on thetraining set and an AUC = 834 (compared to 0836) forthe testing set Therefore we think the bias due to rely-ing on the nomenclatures from Illuminarsquos annotation issmall
Limitations and future directionsWe should point out that our current model does notinclude all histone modification data but only threewidely used methylation markers on histone H3(H3K4Me3 H3K27Me3 and H3K36Me3) Moreover thehistone H3 data are drawn from ENCODE cell linessince the TCGA samples do not have such data Theheterogeneity of the sample resources could affect theaccuracy of the model When more histone marker datacoupled with DNA methylation and RNA-Seq databecome publicly available for lung cancer we caninclude them to achieve a better model In the ideal set-ting we would like to build a predictive model that hasmultiple types of epigenomics data obtained from thesame samples Another potential concern is overfitting
Li et al BMC Bioinformatics 2015 16(Suppl 5)S10httpwwwbiomedcentralcom1471-210516S5S10
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in the classification model However we split the datasetinto training and independent testing subsets and showthe model performs comparably well on the holdouttesting subset We believe that the model can be repli-cated if we can identify paired RNA-Seq and methyla-tion data In fact we had originally built this model ona private data set which also achieved an AUC of morethan 080 Additionally Figure 4 and 5 both indicatethat our approach does not suffer a significant over-fit-ting problem using the TCGA data and show the domi-nant efforts of histone modification and CpGmethylation which yield an updown gene expressionprediction with an AUCgt080 Currently the model useslung cancer data and it will be interesting find outmore general epigenetic predictors for differential geneexpression in other cancers as well Lastly we shouldpoint out that regulation of gene expression is complexincluding other mechanisms mediated by transcriptionfactors microRNA non-coding RNAs etc The fact thatAUCs hover between 080-090 ranges could be well dueto the fact that features from these other mechanismsare not considered in the current epigenetics model Toincrease the accuracy a more complex model that takesinto account of all these events should be constructed
ConclusionsA new model based on epigenomics data is proposed topredict transcriptome-level differential gene expressionin lung cancers Dropping-off feature sets by data typeshows that CpG methylation features are most impor-tant for the prediction Furthermore methylation fea-tures on all genomic regions relative to protein codinggenes contribute to the differential gene expressionwithin which promoter regions are most important
Additional material
Additional file 1 Table S1 Selected 67 features in the best modelsorted by category and their frequency
Competing interestsThe authors declare that they have no competing interests
Authorsrsquo contributionsLXG envisioned the project and supervised the work TC initiated theproject JL and TC designed and implemented the project SJ assisted theproject JL TC and LXG wrote the manuscript All authors have read revisedand approved the final manuscript
AcknowledgementsThe authors would like to thank Dr Jayson Masaki for reviewing themanuscript
DeclarationsPublication charges for this article were funded by NIHNIGMS P20 COBREGM103457 NIHNIEHS K01 ES025434-01 and Hawaii Community Foundation
This article has been published as part of BMC Bioinformatics Volume 16Supplement 5 2015 Selected articles from the 10th InternationalSymposium on Bioinformatics Research and Applications (ISBRA-14)Bioinformatics The full contents of the supplement are available online athttpwwwbiomedcentralcombmcbioinformaticssupplements16S5
Authorsrsquo details1Department of Biomedical Engineering Johns Hopkins University BaltimoreMD 21218 USA 2Molecular Biosciences and Bioengineering GraduateProgram University of Hawaii at Manoa Honolulu HI 96822 USA3Epidemiology Program University of Hawaii Cancer Center Honolulu HI96813 USA
Published 18 March 2015
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10 An integrated encyclopedia of DNA elements in the human genomeNature 2012 489(7414)57-74
11 Karolchik D Hinrichs AS Furey TS Roskin KM Sugnet CW Haussler DKent WJ The UCSC Table Browser data retrieval tool Nucleic acidsresearch 2004 32 Database D493-496
12 Langmead B Salzberg SL Fast gapped-read alignment with Bowtie 2Nature methods 2012 9(4)357-359
13 Li H Handsaker B Wysoker A Fennell T Ruan J Homer N Marth GAbecasis G Durbin R Genome Project Data Processing S The SequenceAlignmentMap format and SAMtools Bioinformatics 200925(16)2078-2079
14 Quinlan AR Hall IM BEDTools a flexible suite of utilities for comparinggenomic features Bioinformatics 2010 26(6)841-842
15 Siepel A Bejerano G Pedersen JS Hinrichs AS Hou M Rosenbloom KClawson H Spieth J Hillier LW Richards S Evolutionarily conservedelements in vertebrate insect worm and yeast genomes Genomeresearch 2005 15(8)1034-1050
16 Love MI Huber W Anders S Moderated estimation of fold change anddispersion for RNA-Seq data with DESeq2 bioRxiv 2014
17 Holm S A simple sequentially rejective multiple test procedureScandinavian journal of statistics 1979 65-70
18 Smyth GK Limma linear models for microarray data Bioinformatics andcomputational biology solutions using R and Bioconductor Springer 2005397-420
19 Quinlan AR Hall IM BEDTools a flexible suite of utilities for comparinggenomic features Bioinformatics 2010 26(6)841-842
20 Pages H Aboyoun P Gentleman R DebRoy S String objects representingbiological sequences and matching algorithms R package version 2009 2(2)
21 Hall MA Smith LA Feature Selection for Machine Learning Comparing aCorrelation-Based Filter Approach to the Wrapper FLAIRS Conference1999 1999 235-239
Li et al BMC Bioinformatics 2015 16(Suppl 5)S10httpwwwbiomedcentralcom1471-210516S5S10
Page 11 of 12
22 Quinlan JR Induction of decision trees Machine learning 1986 1(1)81-10623 Kononenko I Šimec E Robnik-Šikonja M Overcoming the Myopia of
Inductive Learning Algorithms with RELIEFF Applied Intelligence 19977(1)39-55
24 Kira K Rendell LA The feature selection problem Traditional methodsand a new algorithm AAAI 1992 1992 129-134
25 Hall M Frank E Holmes G Pfahringer B Reutemann P Witten IH TheWEKA data mining software an update ACM SIGKDD ExplorationsNewsletter 2009 11(1)10-18
26 Wei T Corrplot visualization of a correlation matrix R package version 02-0 Available on Comprehensive R Archive Network website 2010 [httpCRANR-project orgpackage=corrplot] (accessed 2010)
27 Sing T Sander O Beerenwinkel N Lengauer T ROCR visualizing classifierperformance in R Bioinformatics 2005 21(20)3940-3941
28 Fuks F Hurd PJ Wolf D Nan X Bird AP Kouzarides T The methyl-CpG-binding protein MeCP2 links DNA methylation to histone methylationJournal of Biological Chemistry 2003 278(6)4035-4040
29 Cedar H Bergman Y Linking DNA methylation and histone modificationpatterns and paradigms Nat Rev Genet 2009 10(5)295-304
30 Jones PA Functions of DNA methylation islands start sites gene bodiesand beyond Nat Rev Genet 2012 13(7)484-492
31 Maunakea AK Nagarajan RP Bilenky M Ballinger TJ DrsquoSouza C Fouse SDJohnson BE Hong C Nielsen C Zhao Y Conserved role of intragenic DNAmethylation in regulating alternative promoters Nature 2010466(7303)253-257
32 Bowen NJ Palmer MB Wade PA Chromosomal regulation by MeCP2structural and enzymatic considerations Cellular and molecular lifesciences CMLS 2004 61(17)2163-2167
33 Ooi SK Qiu C Bernstein E Li K Jia D Yang Z Erdjument-Bromage HTempst P Lin SP Allis CD et al DNMT3L connects unmethylated lysine 4of histone H3 to de novo methylation of DNA Nature 2007448(7154)714-717
doi1011861471-2105-16-S5-S10Cite this article as Li et al Using epigenomics data to predict geneexpression in lung cancer BMC Bioinformatics 2015 16(Suppl 5)S10
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Li et al BMC Bioinformatics 2015 16(Suppl 5)S10httpwwwbiomedcentralcom1471-210516S5S10
Page 12 of 12
Abstract
Background
Methods
Results
Conclusions
Background
Methods
Data sets
Data processing
Methylation data
Histone data
Human genome data
RNA-seq data
Feature extraction
CpG Methylation features
Histone marker modification features
Nucleotide features
Conservation features
Feature selection
Model evaluation
Software
Results
Summary of input data and features
Model selection and evaluation
Analysis of selected features
Evaluation of features by data type
Evaluation of CpG methylation features by locations relative to transcripts
Discussion
The need to build predictive models of gene expression from epigenomics data
Selected features and their relevance to gene expression
Limitations and future directions
Conclusions
Competing interests
Authorsrsquo contributions
Acknowledgements
Declarations
Authorsrsquo details
References
genes (promoters exons UTRs etc) as well as in certainintergenic regions It has been shown that CpG methyla-tion tends to occur in promoters located upstream of thetranscription starting site [6] and increased methylation(hypermethylation) in the promoter is negatively asso-ciated with the gene expression level[1] On the otherhand CpG methylation in gene bodies seems to be posi-tively associated with gene expression [1] In cancers cellsmassive global loss of DNA methylation (hypomethyla-tion) has been observed and such hypomethylation in pro-moters can activate aberrant expression of oncogenes [7]Much new information has been gained through therecently developed methods such as Illumina InfiniumHumanMethylation450 arrays that enable the detection ofCpG methylation throughout the different locations asso-ciated with over 99 of protein coding genesHistone modification is another type of important epi-
genetic modification [1] Histones are the core ofnucleosomes that DNA sequences wrap around All his-tones are subject to some level of methylation or acety-lation which would either open up or close the localchromatin structures to enable or repress gene expres-sion Among them Histones 3 (H3) has various kinds ofmethylation and they serve as well-studied markers forgene expression status For example Histone 3 Lysine 4tri-methylation (H3K4Me3) in the promoter region is anindicator of active gene transcription and Histone 3Lysine 36 tri-methylation (H3k36me3) is associated withtranscription elongation Oppositely Histone 3 Lysine27 tri-methylation (H3k27me3) may repress geneexpression Even more complicated histone modifica-tion markers interact with DNA methylation and theconsequent patterns of gene expression are the com-bined effects of their interactions The genomic assayssuch as CHIP-sequencing have enabled the generationof large amount of histone modification dataAlthough integrative analyses on gene expression and
epigenetics regulation abound throughout the literature[89] it is our observation that quantitative models whichuse epigenetic information to accurately predict the up ordown regulation of gene expression are currently lackingA frequent question that a biologist would ask whenmethylation data are available but the gene expressiondata are missing is how the epigenetic changes of a genemay affect the expression of this gene to be either up ordown regulated This report is aimed to fill in this gapand provide the users with a model that allows them toestimate the consequence of epigenetic modification ongene expression when the data for the latter are not avail-able Towards this goal we have built a classification pre-dictor for gene expression using the machine learningapproach This model examines a large set of CpG methy-lation data histone modification data and genome dataand accurately predicts differential expression of RNA-Seq
transcriptome by taking advantage of the publicly availabledata from the TCGA Project (lung cancer) and theENCODE project
MethodsData setsSeveral types of high throughput data were used to extractfeatures or classification responses These include the CpGmethylation array data from 50 paired cancer and adjacentnormal tissues three types of histone marker CHIP-Seqdata from cancer and normal cell lines genomic nucleo-tide sequence and conservation data and RNA-Seqexpression data from samples that have coupled methyla-tion data
Data processingMethylation dataThe Cancer Genome Atlas (TCGA) Methylation data fromIlluminarsquos Infinium HumanMethylation450 Beadchip (Illu-mina 450k) were used to extract CpG methylation relatedfeatures according to their annotation file The genomiccoordinates of CpG their exons and coding regions wereobtained from the Illumina annotation file Since the anno-tation file only provided information of transcripts exonsand coding DNA sequences (CDS) we re-annotated theprotein coding genes using the Illumina iGenomes hg19Refseq annotation in order to extract more comprehensiveinformation from other regions of the transcripts allintrons (with special categories for the first and last intron)as well as first and last exons untranslated regions in the 5rsquoand 3rsquo direction (5rsquo UTR and 3rsquo UTR respectively) and aldquosingle exonrdquo or ldquosingle intronrdquo designation for transcriptsthat only had a single exon or single intronHistone dataThree sets of histone marker CHIP-Seq data H3k4me3H3k27me3 and H3k36me3 were considered from twocell lines A549 cell line (02 EtOH treatment) fromthe lung carcinoma tissue and SAEC normal lungepithelial cell line (no treatment) Raw CHIP-Seq datawere downloaded from the Broad InstituteBernsteinLab at the Massachusetts General HospitalHarvardMedical School and the University of Washington incollaboration with the ENCODE project via the UCSCgenome browser at httpgenomeucscedu [1011] Theraw reads were processed in-house to ensure consis-tency of all normalization procedures Raw data werefirst aligned to hg19 using bowtie2 [12] followed byremoval of duplicated reads using the Samtools toolkit(specifically the ldquormduprdquo tool) [13] The aligned readswere intersected with the relevant segments of the tran-script as annotated in the previous section using theBedtools toolkit (specifically the ldquomulticovrdquo tool) [14] Acustom R script was used to normalize the data overtotal number of reads after removing PCR duplicates
Li et al BMC Bioinformatics 2015 16(Suppl 5)S10httpwwwbiomedcentralcom1471-210516S5S10
Page 2 of 12
Human genome dataNucleotide composition data were extracted from hg19genome FASTA files downloaded from the UCSC gen-ome browser Conservation scores across three classesof species vertebrates primates and placental animalswere also considered PhastCons46Way scores weredownloaded from the UCSC genome browser [1115]Conservation scores were then intersected with the rele-vant segments of the transcripts using a custom Perlscript in order to extract conservation featuresRNA-seq dataRNA-Seq gene expression data from lung cancer sampleswith coupled CpG methylation data were downloadedfrom TCGA Research Network httpcancergenomenihgov Lung adenocarcinoma and lung squamous cell carci-noma data were combined for this project as they are twosubtypes of non-small cell lung cancer Differential expres-sion analysis was done with the DESeq2 package in R [16]In cases where multiple transcripts are mapped to thesame Refseq ID the geometric mean of the differentialexpression results was used to represent the gene levelexpression In the case that any of these read counts waszero the counts from all transcripts were artificiallyincreased by one in order to calculate the geometric meanfollowed by final subtraction of one The expression ofa gene was then classified as binary outcomes either up-re-gulated or down-regulated once it passed two thresholds1) having an adjusted p value lt 05 after Holmrsquos multiplehypothesis test [17] and 2) having an absolute value of log2fold change greater than 1 As a result 2874 genes wereselected as ldquodifferentially expressedrdquo genes
Feature extractionThe extracted features are categorized into four majorsub-groups All features were considered on a segment-wise basis (see Data Processing) unless noted otherwiseCpG Methylation featuresDifferential expression of the methylated CpG sites wasprocessed using the limma library in R Specifically thefunction toptable was used to determine the log foldchange (logFC) between the cancer and normal tissues aswell as the average methylation (avgMval) of each CpGsite across the two types of tissue [18] A positive logFCindicates hypermethylation whereas a negative logFCindicates hypomethylation Additional segment-basedfeatures were also considered These include the numberof hypermethylated (numHyper) and hypomethylatedprobes (numHypo) on a segment of a given transcriptFor example first_exon_numHyper refers to the numberof hypermethylated probes on the first exon Two othertypes of features are the average of logFC and avgMval ofall CpG probes on a segment of the transcript eg theaverage logFC of all probes on the first exon of a giventranscript (first_exon_avglogFC)
Special effort was paid to compute distances of CpGprobes to exon-exon junctions Given that one or moreCpG sites may exist on the individual exon segments of atranscript (including the first and last exons) transcript-level maximum minimum and average distances of anyhyperhypo-methylated probe to the nearest 5rsquo or 3rsquoexon-exon junction were computed (maxHypoTo5 min-HypoTo5 avgHypoTo5 maxHypoTo3 minHypoTo3avgHypoTo3 maxHyperTo5 minHyperTo5 avgHy-perTo5 maxHyperTo3 minHyperTo3 and avgHyperTo3)Histone marker modification featuresAfter the alignment of raw histone marker data (seeData Processing) the aligned histone marker reads wereintersected with the segments of each transcript usingthe multicov function from the BEDTools package [19]The histone reads were then normalized per 1000 bplength of each segment per 1 million aligned readlibrary Similar to the CpG methylation features the his-tone marker modification features were extracted on asegment-by-segment basis Initials are used to representthe individual cell lines where the features come fromA for the A549 cell line and S for the SAEC cell lineFollowing the initial is a number representing the speci-fic histone H3 methylation marker 4 for H3k4me3 27for H3k27me3 and 36 for H3k36me3 As a result fea-tures are named as segment_cell type and histone modi-fication type (eg first_exon_A4) In order to comparehistone modification between the cancer and non-cancercell types the differences of the reads between themwere divided by the average of the two (eg a featurenamed first_exon_A4_minus_S4_divavg)Nucleotide featuresIn each segment of the transcript four different types ofnucleotide features were extracted single nucleotidecomposition dinucleotide composition trinucleotidecomposition and the length of each segment Nucleo-tide sequences of Hg19 reference genome were pro-cessed using the Biostrings library in R [20]Conservation featuresConservation score per segment was calculated as thearithmetic mean of the conservation score per nucleo-tide in that segment Three separate sets of conservationscores with different comparative species were extractedfrom UCSC genome browser - vertebrate primate orplacental Thus features such as first_exon_vertebrateemerge from this set
Feature selectionThree feature selection methods were considered Cor-relation Feature Selection (CFS) [21] Gain Ratio [22]and ReliefF [23]CFS is based on mutual information a non-linear
measure of correlation CFS selects an approximatelyoptimal set of features to maximize the relevance and
Li et al BMC Bioinformatics 2015 16(Suppl 5)S10httpwwwbiomedcentralcom1471-210516S5S10
Page 3 of 12
minimize redundancy Relevance is the correlation of afeature to the class (up-regulated or down-regulatedgene expression) measured by mutual informationwhereas redundancy is the correlation between two fea-tures Redundancy between selected features is mini-mized to keep the number of selected feature smallThe Gain Ratio is an improved method of Information
Gain (IG) Both feature selection methods employ adecision tree in their respective algorithms The GainRatio by name is a ratio of IG but it overcomes thebias of IG which favors features with more dataReliefF is an improved feature selection method from
Relief Relief uses the Manhattan distance of its nearesthit and miss from a random instance to continuouslyupdate a weight vector which is then used to calculate arelevance score Features above a certain relevancethreshold are considered ldquoselectedrdquo [24] ReliefF improveson Relief in several ways including two improvementsparticularly important for this report First ReliefFextends Relief to be able to handle incomplete or partialdata Second ReliefF searches for k near-hits and near-misses and takes their averages as opposed to one near-est hit or miss from Relief k =10 was sufficient to obtainsatisfactory results [23]CFS is the only method that has a built-in system for
selecting the number of features Gain Ratio and ReliefFboth work as ranker systems meaning every input has amatching respective ranked output In order to ensurefairness between feature selection methods we matchedthe numbers of selected features from Gain Ratio andReliefF to be the same as determined by CFS
Model evaluationThe data were split into training and testing sets Thetraining set constituted 80 of the up-regulated anddown-regulated genes and the testing set constituted theremaining 20 genes The training data set underwent10-fold cross validation on various combinations of fea-ture selection and classification methods in order toobtain the best modelAfter determining the best model two sets of drop-off
tests were conducted The first set of tests considered theeffect of data types including nucleotide compositionhistone markers and methylation data on the perfor-mances of sub-models The second set of drop-off testsconsidered the effects of different segments on tran-scripts including gene body exons introns UTRsTSS1500 and TSS200 on the methylation CpG methyla-tion data based sub-models For each drop-off test a setof features was removed from the original input featuresprior to the feature selection and classification Subse-quently the same ReliefF feature selection and RF classifi-cation for the drop-off tests were performed as describedin the previous Feature Selection section
SoftwareWeka 3 data mining software [25] was used for featureselection classifier training and evaluation Various Rpackages were used including Corrplot for generationof the correlation matrix [26] and ROCR for ROCcurves [27] The classification model is available athttpsgithubcomlanagarmireepiPredictor
ResultsSummary of input data and featuresFour types of input data were used to extract the fea-tures including the Illumina 450K CpG methylationarray data from cancer and normal tissues three typesof histone H3 marker CHIP-Seq data from cancer andnormal cell lines genomic nucleotide sequence and con-servation data and RNA-Seq gene expression data fromsamples with coupled CpG methylation data In totalwe calculated 1424 features and summarized the fea-tures by column These features can be divided into twocategories (Table 1) (1) data type based features includ-ing average CpG methylation average methylation logfold change number of hyperhypo-methylated probesmono-nucleotide di-nucleotide and tri-nucleotide com-position histone H3 methylation CHIP-Seq reads andPhastcon conservation scores (2) segment based CpGmethylation features from Illumina 450K BeadChipannotations upstream of the transcription start site(TSS) 1500 TSS200 5rsquo and 3rsquo UTRs exonintron bodyfirst and last exonintron single exonintron and fulltranscript (Figure 1 and Table 1)
Model selection and evaluationThe model uses 2298 gene data points in the training setwith an additional 576 genes kept in the testing set Threedifferent feature selection methods were evaluated in com-bination with five classification methods using 10-foldcross-validation on the training data set (Figure 2) Thethree feature selection methods are correlation-based fea-ture selection (CFS) ReliefF and Gain Ratio In mostcases with combined classification methods except forGaussian SVM ReliefF gives the best AUCs among thethree feature selection methods Among the five classifica-tion methods that we considered namely Gaussian SVMlinear SVM Logistic Regression Naiumlve Bayes and RandomForest the two non-linear methods (Gaussian SVM andRandom Forest) show superior performances to the otherlinear classifiers (Logistic Regression linear SVM andNaiumlve Bayes) This indicates that interactions exist amongthe selected features However the differences are not verybig suggesting that the decision boundary is close to lin-ear Given that the model based on ReliefF feature selec-tion and Random Forest classification gives the best AUCof 0864 it is selected as the best model for the rest of theproject Similarly a ReliefF and Random Forest based
Li et al BMC Bioinformatics 2015 16(Suppl 5)S10httpwwwbiomedcentralcom1471-210516S5S10
Page 4 of 12
Table 1 The list of all features considered prior to feature selectionAverageM value(Methylation)
model has the best predictive performance on the 20holdout data set with an AUC of 0836
Analysis of selected featuresA total of 67 features are selected by the best modelspanning all four types of genomic and epigenomic data
We first explored the relationship among the selectedfeatures Using hierarchical clustering on absolute corre-lation values between features (Figure 3A) we found thatthe selected features tend to cluster by the data type asexpected For example the conservation features in thecoding regions (CDS) are grouped together and so are
Figure 1 Segments associated with protein coding genes Features considered to predict differential gene expression are depicted on asegment-by-segment basis Segments are determined based on the annotations of Illumina Infinium Human Methylation 450K Beadchip Arraywith augmentations on segments located in gene bodies From 5rsquo to 3rsquo end of the protein coding genes listed are transcription starting sites(TSS) upstream up to 1500 bp (TSS 1500) and 200 bp (TSS 200) first exon which may include 5rsquo UTR first intron exon body last intron and lastexon which may include 3rsquo UTR A full transcript region is determined as the UTRs and coding region together
Figure 2 Performance comparison of models with various feature selection and classification methods The Areas Under the Curve (AUC)of ROC are used as the metric to compare the performance of models with different combinations of feature selection (CFS Gain Ratios andReliefF) and classification (Gaussian SVM Linear SVM Logistic regression Naiumlve Bayes and Random Forest) on the training data with 10 foldcross-validation The model with ReliefF based feature selection and Random Forest classification is selected as the best model
Li et al BMC Bioinformatics 2015 16(Suppl 5)S10httpwwwbiomedcentralcom1471-210516S5S10
Page 6 of 12
most methylation features As expected the CpG islandswithin the promoter are very important for the predictionof gene expression as demonstrated by the three selectedand highly correlated features CG composition featuresTSS200_GC TSS200_CG and TSS200_CGGThe largest group of selected features is the histone
modification features (32 features) followed by the methy-lation features (15 features) (Additional File 1) Theselected features underscore the importance of histoneepigenetic modification in the regulation of gene expres-sion Likewise the importance of methylation features isevident especially for the featured arising from TSS 5rsquoUTR and first exons Interestingly several methylation fea-tures (TSS1500_avgMval first_exon_avgMval andUTR5_avgMval) are clustered with histone modificationfeatures suggesting collinearity between these two types offeatures as shown by others [2829]On the other hand when features are categorized by
location relative to the transcripts (Additional File 1) theTSS200 has the most number of features (13 features)and TSS1500 has 6 features selected for this regionTogether the promoter comprises 28 of all the selectedfeatures This confirms the previously well-known impor-tance of the promoter region for the epigenetic regulationof gene expression [3031] Additionally CDS has the sec-ond highest number of features being selected highlight-ing its significance in regulating gene expression [30]
We also calculated the correlation of each feature togene expression and plotted the top 15 features mostrelevant to gene expression prediction (Figure 3B) Noneof the features have correlations higher than 045 sug-gesting that no single feature is a dominant predictorfor gene expression These features are either histonemodification (11 features) or methylation features (4 fea-tures) consistent with the previous observation on thesignificance of these two types of features
Evaluation of features by data typeTo determine the contribution of different types of fea-tures to gene expression we tested the performance ofmodels when a subset of features from the same datatype were dropped We present the results of four mea-sures of model performance AUC accuracy F-measureand Matthewrsquos correlation coefficient (MCC) (Figure 4)Dropping any individual feature set of nucleotide compo-sition histone modification or CpG methylation did notseem to have a large effect on the model performanceindicating that there is redundancy between feature setsThe sub-model performance for the dropping-off of asingle feature set from the full model is in the followingorder nucleotide composition removal gt histone modifi-cation removal gt CpG methylation removal Thus drop-ping methylation features had the largest effect amongindividual feature set as the AUC decreases from 0864
Figure 3 Top fifteen features from the best model (a) The clustering results on the absolute values of Pearsonrsquos correlation coefficients from67 selected features by the best model The names of different type of features are labeled by different colors Note the length of a segment islisted out separately (b) List of top fifteen features selected by ReliefF feature selection and sorted by their correlation to the classification ofdifferential gene expression
Li et al BMC Bioinformatics 2015 16(Suppl 5)S10httpwwwbiomedcentralcom1471-210516S5S10
Page 7 of 12
in the full model to 0832 in the training set as well asfrom 0836 to 0810 in the testing set Likewise MCCupon single feature set drop-off shows the largest pro-portional change among the four performance measuresand decreases from 056 to 049 on the training set and051 to 045 on the testing setWe also compared the effect of removing both nucleo-
tide and histone features on model performance as com-pared to removing either of them alone As expectedremoving both nucleotide and histone features gives thelowest AUCs lowest accuracies and lowest F-measures in
both training and testing sets However it leads to higherMCC than removing just histones does in the testing setThis suggests that there might be some overfitting withregards to the nucleotide feature set which accounts forthe majority (83) of features prior to feature selection
Evaluation of CpG methylation features by locationsrelative to transcriptsGiven that removing methylation features causes themost reduction of model performance among the singlefeature set drop-off (Figure 5) we next asked the
Figure 4 Evaluation of features generated from various data types (a-b) Effects of feature set drop-off on ROC curves from the 10-foldcross-validation training set (a) and testing set (b) (c) Effects of feature set drop-off on other four metrics AUC Accuracy F-measure and MCCin the training set and testing set
Li et al BMC Bioinformatics 2015 16(Suppl 5)S10httpwwwbiomedcentralcom1471-210516S5S10
Page 8 of 12
question of the relative importance of each methylationfeature categorized by genomic location We performeddrop-off tests by sequentially removing features in eachgenomic location category We first removed the featuresfrom the first exons and first introns (as they are close to
the TSS) then from gene bodies including exons intronsand UTRs and lastly from TSS1500 region such that onlyTSS200 features were kept At each step we re-performedfeature selection and model construction using theremaining methylation features
Figure 5 Evaluation of methylation features by segment (a-b) Effects of segment-based methylation feature set sequential drop-off on ROCcurves from the 10-fold cross-validation training set (a) and testing set (b) (c) Effects of segment-based methylation feature set sequential drop-off on other four metrics AUC Accuracy F-measure and MCC in the training set and testing set
Li et al BMC Bioinformatics 2015 16(Suppl 5)S10httpwwwbiomedcentralcom1471-210516S5S10
Page 9 of 12
All categories of genomic locations provide relevantuseful information that contributes to better predictionof gene expression as each of the sequential feature setdrop-off decreased the performance of the model in bothtraining and testing sets Compared to the removal offirst exon and intron regions and removal of the UTRsand the rest of the gene body removal of TSS1500 leadsto the largest reduction in all four metrics confirmingthe importance of the promoter region in regulating geneexpression Even when only TSS200 features are consid-ered an AUC of 0638 and 0636 are obtained in the test-ing and training sets respectively suggesting that CpGmethylation status in TSS200 is still somewhat predictiveof gene expression However a more accurate predictionusing methylation features should arise from all locationsassociated with the transcripts
DiscussionThe need to build predictive models of gene expressionfrom epigenomics dataAlthough currently integrative analyses between geneexpression and epigenetic modification exist we havefound that quantitative models using epigenetic informa-tion to accurately predict the up or down regulation ofgene expression are currently lacking There are oftencases where researchers can only obtain reliable epige-netics data but not gene expression data For example ifthe samples are archived and processed by FFPE (Forma-lin-fixed paraffin-embedded) one can still perform epige-nomics measurements but not the gene expressionexperiments due to the degradation of mRNA in the sam-ples More importantly a predictive method such as ourscan efficiently facilitate the bench scientists to narrowdown the candidate lists and conduct gene expression vali-dation especially when the epigenetics information is theonly data handy
Selected features and their relevance to gene expressionAll four types of data (CpG methylation histone H3modification nucleotide sequence and conservation)exist in the 67 features that are selected by the bestmodel indicating that all of them contribute to the accu-rate prediction of gene expression Moreover selectedfeatures of the same data type tend to cluster together onthe correlation matrix among the features suggestingthat the relationship within the same data type is closerthan the relationship between different data types Asexpected histone modification and CpG methylation fea-tures are the largest two groups among the four types ofdata signifying their importance to predict gene expres-sion Since nonlinear classification methods performslightly better than linear classification methods it sug-gests that interactions do exist between different types ofdata This is supported by numerous literatures that
enzymes responsible for CpG methylation also interactwith histone modification events [3233]Besides the value of predicting gene expression our
models also provide insights into the relative importanceof different epigenomicsgenome data as well as thegenomic locations We found that CpG methylation fea-tures have more predictive values for differential geneexpression compared to the three types of histone H3modification data Although other kinds of histone mod-ification data can also be obtained to increase the pre-dictive values of histone modification data it is muchmore costly to obtain them relative to the CpG methyla-tion data (the cost of CHIP-Seq on each of the histonemodification marker is similar to an entire CpG methy-lation array) Therefore practically speaking when thebudget is a constraining factor we suggest that assayson CpG methylation should be considered with priorityin predicting differential gene expression Moreover theresults of our models demonstrate that all genomic loca-tions relative to each transcript including promotersexons and gene bodies provide useful information topredict gene expression alternation Although the CpGmethylation signals from the promoters region are moreimportant the methylation signals from other regionssuch as exons introns and UTRs are indicative ofchanges in the gene expression as wellWorth noticing a lot of features that are extracted on
methylation and histone modification are naturallybased on the annotations from Illumina 450K array plat-form for DNA methylation There may be bias on thenumber of features that are hand coded in the modelTo address potential issue we changed TSS200_GC toTSS150_GC in our model and obtained an AUC = 861(compared to 0864) for cross fold validation on thetraining set and an AUC = 834 (compared to 0836) forthe testing set Therefore we think the bias due to rely-ing on the nomenclatures from Illuminarsquos annotation issmall
Limitations and future directionsWe should point out that our current model does notinclude all histone modification data but only threewidely used methylation markers on histone H3(H3K4Me3 H3K27Me3 and H3K36Me3) Moreover thehistone H3 data are drawn from ENCODE cell linessince the TCGA samples do not have such data Theheterogeneity of the sample resources could affect theaccuracy of the model When more histone marker datacoupled with DNA methylation and RNA-Seq databecome publicly available for lung cancer we caninclude them to achieve a better model In the ideal set-ting we would like to build a predictive model that hasmultiple types of epigenomics data obtained from thesame samples Another potential concern is overfitting
Li et al BMC Bioinformatics 2015 16(Suppl 5)S10httpwwwbiomedcentralcom1471-210516S5S10
Page 10 of 12
in the classification model However we split the datasetinto training and independent testing subsets and showthe model performs comparably well on the holdouttesting subset We believe that the model can be repli-cated if we can identify paired RNA-Seq and methyla-tion data In fact we had originally built this model ona private data set which also achieved an AUC of morethan 080 Additionally Figure 4 and 5 both indicatethat our approach does not suffer a significant over-fit-ting problem using the TCGA data and show the domi-nant efforts of histone modification and CpGmethylation which yield an updown gene expressionprediction with an AUCgt080 Currently the model useslung cancer data and it will be interesting find outmore general epigenetic predictors for differential geneexpression in other cancers as well Lastly we shouldpoint out that regulation of gene expression is complexincluding other mechanisms mediated by transcriptionfactors microRNA non-coding RNAs etc The fact thatAUCs hover between 080-090 ranges could be well dueto the fact that features from these other mechanismsare not considered in the current epigenetics model Toincrease the accuracy a more complex model that takesinto account of all these events should be constructed
ConclusionsA new model based on epigenomics data is proposed topredict transcriptome-level differential gene expressionin lung cancers Dropping-off feature sets by data typeshows that CpG methylation features are most impor-tant for the prediction Furthermore methylation fea-tures on all genomic regions relative to protein codinggenes contribute to the differential gene expressionwithin which promoter regions are most important
Additional material
Additional file 1 Table S1 Selected 67 features in the best modelsorted by category and their frequency
Competing interestsThe authors declare that they have no competing interests
Authorsrsquo contributionsLXG envisioned the project and supervised the work TC initiated theproject JL and TC designed and implemented the project SJ assisted theproject JL TC and LXG wrote the manuscript All authors have read revisedand approved the final manuscript
AcknowledgementsThe authors would like to thank Dr Jayson Masaki for reviewing themanuscript
DeclarationsPublication charges for this article were funded by NIHNIGMS P20 COBREGM103457 NIHNIEHS K01 ES025434-01 and Hawaii Community Foundation
This article has been published as part of BMC Bioinformatics Volume 16Supplement 5 2015 Selected articles from the 10th InternationalSymposium on Bioinformatics Research and Applications (ISBRA-14)Bioinformatics The full contents of the supplement are available online athttpwwwbiomedcentralcombmcbioinformaticssupplements16S5
Authorsrsquo details1Department of Biomedical Engineering Johns Hopkins University BaltimoreMD 21218 USA 2Molecular Biosciences and Bioengineering GraduateProgram University of Hawaii at Manoa Honolulu HI 96822 USA3Epidemiology Program University of Hawaii Cancer Center Honolulu HI96813 USA
Published 18 March 2015
References1 Portela A Esteller M Epigenetic modifications and human disease Nature
biotechnology 2010 28(10)1057-10682 Bock C Lengauer T Computational epigenetics Bioinformatics 2008
24(1)1-103 Laird PW Principles and challenges of genomewide DNA methylation
analysis Nature reviews Genetics 2010 11(3)191-2034 Lim SJ Tan TW Tong JC Computational Epigenetics the new scientific
paradigm Bioinformation 2010 4(7)331-3375 Gardiner-Garden M Frommer M CpG islands in vertebrate genomes
Journal of molecular biology 1987 196(2)261-2826 Daura-Oller E Cabre M Montero MA Paternain JL Romeu A Specific gene
hypomethylation and cancer New insights into coding region featuretrends Bioinformation 2009 3(8)340
7 Wild L Flanagan JM Genome-wide hypomethylation in cancer may be apassive consequence of transformation Biochimica et biophysica acta2010 1806(1)50-57
8 Figueroa ME Chen SC Andersson AK Phillips LA Li Y Sotzen J Kundu MDowning JR Melnick A Mullighan CG Integrated genetic and epigeneticanalysis of childhood acute lymphoblastic leukemia The Journal ofclinical investigation 2013 123(7)3099-3111
9 Rhee JK Kim K Chae H Evans J Yan P Zhang BT Gray J Spellman PHuang TH Nephew KP et al Integrated analysis of genome-wide DNAmethylation and gene expression profiles in molecular subtypes ofbreast cancer Nucleic acids research 2013 41(18)8464-8474
10 An integrated encyclopedia of DNA elements in the human genomeNature 2012 489(7414)57-74
11 Karolchik D Hinrichs AS Furey TS Roskin KM Sugnet CW Haussler DKent WJ The UCSC Table Browser data retrieval tool Nucleic acidsresearch 2004 32 Database D493-496
12 Langmead B Salzberg SL Fast gapped-read alignment with Bowtie 2Nature methods 2012 9(4)357-359
13 Li H Handsaker B Wysoker A Fennell T Ruan J Homer N Marth GAbecasis G Durbin R Genome Project Data Processing S The SequenceAlignmentMap format and SAMtools Bioinformatics 200925(16)2078-2079
14 Quinlan AR Hall IM BEDTools a flexible suite of utilities for comparinggenomic features Bioinformatics 2010 26(6)841-842
15 Siepel A Bejerano G Pedersen JS Hinrichs AS Hou M Rosenbloom KClawson H Spieth J Hillier LW Richards S Evolutionarily conservedelements in vertebrate insect worm and yeast genomes Genomeresearch 2005 15(8)1034-1050
16 Love MI Huber W Anders S Moderated estimation of fold change anddispersion for RNA-Seq data with DESeq2 bioRxiv 2014
17 Holm S A simple sequentially rejective multiple test procedureScandinavian journal of statistics 1979 65-70
18 Smyth GK Limma linear models for microarray data Bioinformatics andcomputational biology solutions using R and Bioconductor Springer 2005397-420
19 Quinlan AR Hall IM BEDTools a flexible suite of utilities for comparinggenomic features Bioinformatics 2010 26(6)841-842
20 Pages H Aboyoun P Gentleman R DebRoy S String objects representingbiological sequences and matching algorithms R package version 2009 2(2)
21 Hall MA Smith LA Feature Selection for Machine Learning Comparing aCorrelation-Based Filter Approach to the Wrapper FLAIRS Conference1999 1999 235-239
Li et al BMC Bioinformatics 2015 16(Suppl 5)S10httpwwwbiomedcentralcom1471-210516S5S10
Page 11 of 12
22 Quinlan JR Induction of decision trees Machine learning 1986 1(1)81-10623 Kononenko I Šimec E Robnik-Šikonja M Overcoming the Myopia of
Inductive Learning Algorithms with RELIEFF Applied Intelligence 19977(1)39-55
24 Kira K Rendell LA The feature selection problem Traditional methodsand a new algorithm AAAI 1992 1992 129-134
25 Hall M Frank E Holmes G Pfahringer B Reutemann P Witten IH TheWEKA data mining software an update ACM SIGKDD ExplorationsNewsletter 2009 11(1)10-18
26 Wei T Corrplot visualization of a correlation matrix R package version 02-0 Available on Comprehensive R Archive Network website 2010 [httpCRANR-project orgpackage=corrplot] (accessed 2010)
27 Sing T Sander O Beerenwinkel N Lengauer T ROCR visualizing classifierperformance in R Bioinformatics 2005 21(20)3940-3941
28 Fuks F Hurd PJ Wolf D Nan X Bird AP Kouzarides T The methyl-CpG-binding protein MeCP2 links DNA methylation to histone methylationJournal of Biological Chemistry 2003 278(6)4035-4040
29 Cedar H Bergman Y Linking DNA methylation and histone modificationpatterns and paradigms Nat Rev Genet 2009 10(5)295-304
30 Jones PA Functions of DNA methylation islands start sites gene bodiesand beyond Nat Rev Genet 2012 13(7)484-492
31 Maunakea AK Nagarajan RP Bilenky M Ballinger TJ DrsquoSouza C Fouse SDJohnson BE Hong C Nielsen C Zhao Y Conserved role of intragenic DNAmethylation in regulating alternative promoters Nature 2010466(7303)253-257
32 Bowen NJ Palmer MB Wade PA Chromosomal regulation by MeCP2structural and enzymatic considerations Cellular and molecular lifesciences CMLS 2004 61(17)2163-2167
33 Ooi SK Qiu C Bernstein E Li K Jia D Yang Z Erdjument-Bromage HTempst P Lin SP Allis CD et al DNMT3L connects unmethylated lysine 4of histone H3 to de novo methylation of DNA Nature 2007448(7154)714-717
doi1011861471-2105-16-S5-S10Cite this article as Li et al Using epigenomics data to predict geneexpression in lung cancer BMC Bioinformatics 2015 16(Suppl 5)S10
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Li et al BMC Bioinformatics 2015 16(Suppl 5)S10httpwwwbiomedcentralcom1471-210516S5S10
Page 12 of 12
Abstract
Background
Methods
Results
Conclusions
Background
Methods
Data sets
Data processing
Methylation data
Histone data
Human genome data
RNA-seq data
Feature extraction
CpG Methylation features
Histone marker modification features
Nucleotide features
Conservation features
Feature selection
Model evaluation
Software
Results
Summary of input data and features
Model selection and evaluation
Analysis of selected features
Evaluation of features by data type
Evaluation of CpG methylation features by locations relative to transcripts
Discussion
The need to build predictive models of gene expression from epigenomics data
Selected features and their relevance to gene expression
Limitations and future directions
Conclusions
Competing interests
Authorsrsquo contributions
Acknowledgements
Declarations
Authorsrsquo details
References
Human genome dataNucleotide composition data were extracted from hg19genome FASTA files downloaded from the UCSC gen-ome browser Conservation scores across three classesof species vertebrates primates and placental animalswere also considered PhastCons46Way scores weredownloaded from the UCSC genome browser [1115]Conservation scores were then intersected with the rele-vant segments of the transcripts using a custom Perlscript in order to extract conservation featuresRNA-seq dataRNA-Seq gene expression data from lung cancer sampleswith coupled CpG methylation data were downloadedfrom TCGA Research Network httpcancergenomenihgov Lung adenocarcinoma and lung squamous cell carci-noma data were combined for this project as they are twosubtypes of non-small cell lung cancer Differential expres-sion analysis was done with the DESeq2 package in R [16]In cases where multiple transcripts are mapped to thesame Refseq ID the geometric mean of the differentialexpression results was used to represent the gene levelexpression In the case that any of these read counts waszero the counts from all transcripts were artificiallyincreased by one in order to calculate the geometric meanfollowed by final subtraction of one The expression ofa gene was then classified as binary outcomes either up-re-gulated or down-regulated once it passed two thresholds1) having an adjusted p value lt 05 after Holmrsquos multiplehypothesis test [17] and 2) having an absolute value of log2fold change greater than 1 As a result 2874 genes wereselected as ldquodifferentially expressedrdquo genes
Feature extractionThe extracted features are categorized into four majorsub-groups All features were considered on a segment-wise basis (see Data Processing) unless noted otherwiseCpG Methylation featuresDifferential expression of the methylated CpG sites wasprocessed using the limma library in R Specifically thefunction toptable was used to determine the log foldchange (logFC) between the cancer and normal tissues aswell as the average methylation (avgMval) of each CpGsite across the two types of tissue [18] A positive logFCindicates hypermethylation whereas a negative logFCindicates hypomethylation Additional segment-basedfeatures were also considered These include the numberof hypermethylated (numHyper) and hypomethylatedprobes (numHypo) on a segment of a given transcriptFor example first_exon_numHyper refers to the numberof hypermethylated probes on the first exon Two othertypes of features are the average of logFC and avgMval ofall CpG probes on a segment of the transcript eg theaverage logFC of all probes on the first exon of a giventranscript (first_exon_avglogFC)
Special effort was paid to compute distances of CpGprobes to exon-exon junctions Given that one or moreCpG sites may exist on the individual exon segments of atranscript (including the first and last exons) transcript-level maximum minimum and average distances of anyhyperhypo-methylated probe to the nearest 5rsquo or 3rsquoexon-exon junction were computed (maxHypoTo5 min-HypoTo5 avgHypoTo5 maxHypoTo3 minHypoTo3avgHypoTo3 maxHyperTo5 minHyperTo5 avgHy-perTo5 maxHyperTo3 minHyperTo3 and avgHyperTo3)Histone marker modification featuresAfter the alignment of raw histone marker data (seeData Processing) the aligned histone marker reads wereintersected with the segments of each transcript usingthe multicov function from the BEDTools package [19]The histone reads were then normalized per 1000 bplength of each segment per 1 million aligned readlibrary Similar to the CpG methylation features the his-tone marker modification features were extracted on asegment-by-segment basis Initials are used to representthe individual cell lines where the features come fromA for the A549 cell line and S for the SAEC cell lineFollowing the initial is a number representing the speci-fic histone H3 methylation marker 4 for H3k4me3 27for H3k27me3 and 36 for H3k36me3 As a result fea-tures are named as segment_cell type and histone modi-fication type (eg first_exon_A4) In order to comparehistone modification between the cancer and non-cancercell types the differences of the reads between themwere divided by the average of the two (eg a featurenamed first_exon_A4_minus_S4_divavg)Nucleotide featuresIn each segment of the transcript four different types ofnucleotide features were extracted single nucleotidecomposition dinucleotide composition trinucleotidecomposition and the length of each segment Nucleo-tide sequences of Hg19 reference genome were pro-cessed using the Biostrings library in R [20]Conservation featuresConservation score per segment was calculated as thearithmetic mean of the conservation score per nucleo-tide in that segment Three separate sets of conservationscores with different comparative species were extractedfrom UCSC genome browser - vertebrate primate orplacental Thus features such as first_exon_vertebrateemerge from this set
Feature selectionThree feature selection methods were considered Cor-relation Feature Selection (CFS) [21] Gain Ratio [22]and ReliefF [23]CFS is based on mutual information a non-linear
measure of correlation CFS selects an approximatelyoptimal set of features to maximize the relevance and
Li et al BMC Bioinformatics 2015 16(Suppl 5)S10httpwwwbiomedcentralcom1471-210516S5S10
Page 3 of 12
minimize redundancy Relevance is the correlation of afeature to the class (up-regulated or down-regulatedgene expression) measured by mutual informationwhereas redundancy is the correlation between two fea-tures Redundancy between selected features is mini-mized to keep the number of selected feature smallThe Gain Ratio is an improved method of Information
Gain (IG) Both feature selection methods employ adecision tree in their respective algorithms The GainRatio by name is a ratio of IG but it overcomes thebias of IG which favors features with more dataReliefF is an improved feature selection method from
Relief Relief uses the Manhattan distance of its nearesthit and miss from a random instance to continuouslyupdate a weight vector which is then used to calculate arelevance score Features above a certain relevancethreshold are considered ldquoselectedrdquo [24] ReliefF improveson Relief in several ways including two improvementsparticularly important for this report First ReliefFextends Relief to be able to handle incomplete or partialdata Second ReliefF searches for k near-hits and near-misses and takes their averages as opposed to one near-est hit or miss from Relief k =10 was sufficient to obtainsatisfactory results [23]CFS is the only method that has a built-in system for
selecting the number of features Gain Ratio and ReliefFboth work as ranker systems meaning every input has amatching respective ranked output In order to ensurefairness between feature selection methods we matchedthe numbers of selected features from Gain Ratio andReliefF to be the same as determined by CFS
Model evaluationThe data were split into training and testing sets Thetraining set constituted 80 of the up-regulated anddown-regulated genes and the testing set constituted theremaining 20 genes The training data set underwent10-fold cross validation on various combinations of fea-ture selection and classification methods in order toobtain the best modelAfter determining the best model two sets of drop-off
tests were conducted The first set of tests considered theeffect of data types including nucleotide compositionhistone markers and methylation data on the perfor-mances of sub-models The second set of drop-off testsconsidered the effects of different segments on tran-scripts including gene body exons introns UTRsTSS1500 and TSS200 on the methylation CpG methyla-tion data based sub-models For each drop-off test a setof features was removed from the original input featuresprior to the feature selection and classification Subse-quently the same ReliefF feature selection and RF classifi-cation for the drop-off tests were performed as describedin the previous Feature Selection section
SoftwareWeka 3 data mining software [25] was used for featureselection classifier training and evaluation Various Rpackages were used including Corrplot for generationof the correlation matrix [26] and ROCR for ROCcurves [27] The classification model is available athttpsgithubcomlanagarmireepiPredictor
ResultsSummary of input data and featuresFour types of input data were used to extract the fea-tures including the Illumina 450K CpG methylationarray data from cancer and normal tissues three typesof histone H3 marker CHIP-Seq data from cancer andnormal cell lines genomic nucleotide sequence and con-servation data and RNA-Seq gene expression data fromsamples with coupled CpG methylation data In totalwe calculated 1424 features and summarized the fea-tures by column These features can be divided into twocategories (Table 1) (1) data type based features includ-ing average CpG methylation average methylation logfold change number of hyperhypo-methylated probesmono-nucleotide di-nucleotide and tri-nucleotide com-position histone H3 methylation CHIP-Seq reads andPhastcon conservation scores (2) segment based CpGmethylation features from Illumina 450K BeadChipannotations upstream of the transcription start site(TSS) 1500 TSS200 5rsquo and 3rsquo UTRs exonintron bodyfirst and last exonintron single exonintron and fulltranscript (Figure 1 and Table 1)
Model selection and evaluationThe model uses 2298 gene data points in the training setwith an additional 576 genes kept in the testing set Threedifferent feature selection methods were evaluated in com-bination with five classification methods using 10-foldcross-validation on the training data set (Figure 2) Thethree feature selection methods are correlation-based fea-ture selection (CFS) ReliefF and Gain Ratio In mostcases with combined classification methods except forGaussian SVM ReliefF gives the best AUCs among thethree feature selection methods Among the five classifica-tion methods that we considered namely Gaussian SVMlinear SVM Logistic Regression Naiumlve Bayes and RandomForest the two non-linear methods (Gaussian SVM andRandom Forest) show superior performances to the otherlinear classifiers (Logistic Regression linear SVM andNaiumlve Bayes) This indicates that interactions exist amongthe selected features However the differences are not verybig suggesting that the decision boundary is close to lin-ear Given that the model based on ReliefF feature selec-tion and Random Forest classification gives the best AUCof 0864 it is selected as the best model for the rest of theproject Similarly a ReliefF and Random Forest based
Li et al BMC Bioinformatics 2015 16(Suppl 5)S10httpwwwbiomedcentralcom1471-210516S5S10
Page 4 of 12
Table 1 The list of all features considered prior to feature selectionAverageM value(Methylation)
model has the best predictive performance on the 20holdout data set with an AUC of 0836
Analysis of selected featuresA total of 67 features are selected by the best modelspanning all four types of genomic and epigenomic data
We first explored the relationship among the selectedfeatures Using hierarchical clustering on absolute corre-lation values between features (Figure 3A) we found thatthe selected features tend to cluster by the data type asexpected For example the conservation features in thecoding regions (CDS) are grouped together and so are
Figure 1 Segments associated with protein coding genes Features considered to predict differential gene expression are depicted on asegment-by-segment basis Segments are determined based on the annotations of Illumina Infinium Human Methylation 450K Beadchip Arraywith augmentations on segments located in gene bodies From 5rsquo to 3rsquo end of the protein coding genes listed are transcription starting sites(TSS) upstream up to 1500 bp (TSS 1500) and 200 bp (TSS 200) first exon which may include 5rsquo UTR first intron exon body last intron and lastexon which may include 3rsquo UTR A full transcript region is determined as the UTRs and coding region together
Figure 2 Performance comparison of models with various feature selection and classification methods The Areas Under the Curve (AUC)of ROC are used as the metric to compare the performance of models with different combinations of feature selection (CFS Gain Ratios andReliefF) and classification (Gaussian SVM Linear SVM Logistic regression Naiumlve Bayes and Random Forest) on the training data with 10 foldcross-validation The model with ReliefF based feature selection and Random Forest classification is selected as the best model
Li et al BMC Bioinformatics 2015 16(Suppl 5)S10httpwwwbiomedcentralcom1471-210516S5S10
Page 6 of 12
most methylation features As expected the CpG islandswithin the promoter are very important for the predictionof gene expression as demonstrated by the three selectedand highly correlated features CG composition featuresTSS200_GC TSS200_CG and TSS200_CGGThe largest group of selected features is the histone
modification features (32 features) followed by the methy-lation features (15 features) (Additional File 1) Theselected features underscore the importance of histoneepigenetic modification in the regulation of gene expres-sion Likewise the importance of methylation features isevident especially for the featured arising from TSS 5rsquoUTR and first exons Interestingly several methylation fea-tures (TSS1500_avgMval first_exon_avgMval andUTR5_avgMval) are clustered with histone modificationfeatures suggesting collinearity between these two types offeatures as shown by others [2829]On the other hand when features are categorized by
location relative to the transcripts (Additional File 1) theTSS200 has the most number of features (13 features)and TSS1500 has 6 features selected for this regionTogether the promoter comprises 28 of all the selectedfeatures This confirms the previously well-known impor-tance of the promoter region for the epigenetic regulationof gene expression [3031] Additionally CDS has the sec-ond highest number of features being selected highlight-ing its significance in regulating gene expression [30]
We also calculated the correlation of each feature togene expression and plotted the top 15 features mostrelevant to gene expression prediction (Figure 3B) Noneof the features have correlations higher than 045 sug-gesting that no single feature is a dominant predictorfor gene expression These features are either histonemodification (11 features) or methylation features (4 fea-tures) consistent with the previous observation on thesignificance of these two types of features
Evaluation of features by data typeTo determine the contribution of different types of fea-tures to gene expression we tested the performance ofmodels when a subset of features from the same datatype were dropped We present the results of four mea-sures of model performance AUC accuracy F-measureand Matthewrsquos correlation coefficient (MCC) (Figure 4)Dropping any individual feature set of nucleotide compo-sition histone modification or CpG methylation did notseem to have a large effect on the model performanceindicating that there is redundancy between feature setsThe sub-model performance for the dropping-off of asingle feature set from the full model is in the followingorder nucleotide composition removal gt histone modifi-cation removal gt CpG methylation removal Thus drop-ping methylation features had the largest effect amongindividual feature set as the AUC decreases from 0864
Figure 3 Top fifteen features from the best model (a) The clustering results on the absolute values of Pearsonrsquos correlation coefficients from67 selected features by the best model The names of different type of features are labeled by different colors Note the length of a segment islisted out separately (b) List of top fifteen features selected by ReliefF feature selection and sorted by their correlation to the classification ofdifferential gene expression
Li et al BMC Bioinformatics 2015 16(Suppl 5)S10httpwwwbiomedcentralcom1471-210516S5S10
Page 7 of 12
in the full model to 0832 in the training set as well asfrom 0836 to 0810 in the testing set Likewise MCCupon single feature set drop-off shows the largest pro-portional change among the four performance measuresand decreases from 056 to 049 on the training set and051 to 045 on the testing setWe also compared the effect of removing both nucleo-
tide and histone features on model performance as com-pared to removing either of them alone As expectedremoving both nucleotide and histone features gives thelowest AUCs lowest accuracies and lowest F-measures in
both training and testing sets However it leads to higherMCC than removing just histones does in the testing setThis suggests that there might be some overfitting withregards to the nucleotide feature set which accounts forthe majority (83) of features prior to feature selection
Evaluation of CpG methylation features by locationsrelative to transcriptsGiven that removing methylation features causes themost reduction of model performance among the singlefeature set drop-off (Figure 5) we next asked the
Figure 4 Evaluation of features generated from various data types (a-b) Effects of feature set drop-off on ROC curves from the 10-foldcross-validation training set (a) and testing set (b) (c) Effects of feature set drop-off on other four metrics AUC Accuracy F-measure and MCCin the training set and testing set
Li et al BMC Bioinformatics 2015 16(Suppl 5)S10httpwwwbiomedcentralcom1471-210516S5S10
Page 8 of 12
question of the relative importance of each methylationfeature categorized by genomic location We performeddrop-off tests by sequentially removing features in eachgenomic location category We first removed the featuresfrom the first exons and first introns (as they are close to
the TSS) then from gene bodies including exons intronsand UTRs and lastly from TSS1500 region such that onlyTSS200 features were kept At each step we re-performedfeature selection and model construction using theremaining methylation features
Figure 5 Evaluation of methylation features by segment (a-b) Effects of segment-based methylation feature set sequential drop-off on ROCcurves from the 10-fold cross-validation training set (a) and testing set (b) (c) Effects of segment-based methylation feature set sequential drop-off on other four metrics AUC Accuracy F-measure and MCC in the training set and testing set
Li et al BMC Bioinformatics 2015 16(Suppl 5)S10httpwwwbiomedcentralcom1471-210516S5S10
Page 9 of 12
All categories of genomic locations provide relevantuseful information that contributes to better predictionof gene expression as each of the sequential feature setdrop-off decreased the performance of the model in bothtraining and testing sets Compared to the removal offirst exon and intron regions and removal of the UTRsand the rest of the gene body removal of TSS1500 leadsto the largest reduction in all four metrics confirmingthe importance of the promoter region in regulating geneexpression Even when only TSS200 features are consid-ered an AUC of 0638 and 0636 are obtained in the test-ing and training sets respectively suggesting that CpGmethylation status in TSS200 is still somewhat predictiveof gene expression However a more accurate predictionusing methylation features should arise from all locationsassociated with the transcripts
DiscussionThe need to build predictive models of gene expressionfrom epigenomics dataAlthough currently integrative analyses between geneexpression and epigenetic modification exist we havefound that quantitative models using epigenetic informa-tion to accurately predict the up or down regulation ofgene expression are currently lacking There are oftencases where researchers can only obtain reliable epige-netics data but not gene expression data For example ifthe samples are archived and processed by FFPE (Forma-lin-fixed paraffin-embedded) one can still perform epige-nomics measurements but not the gene expressionexperiments due to the degradation of mRNA in the sam-ples More importantly a predictive method such as ourscan efficiently facilitate the bench scientists to narrowdown the candidate lists and conduct gene expression vali-dation especially when the epigenetics information is theonly data handy
Selected features and their relevance to gene expressionAll four types of data (CpG methylation histone H3modification nucleotide sequence and conservation)exist in the 67 features that are selected by the bestmodel indicating that all of them contribute to the accu-rate prediction of gene expression Moreover selectedfeatures of the same data type tend to cluster together onthe correlation matrix among the features suggestingthat the relationship within the same data type is closerthan the relationship between different data types Asexpected histone modification and CpG methylation fea-tures are the largest two groups among the four types ofdata signifying their importance to predict gene expres-sion Since nonlinear classification methods performslightly better than linear classification methods it sug-gests that interactions do exist between different types ofdata This is supported by numerous literatures that
enzymes responsible for CpG methylation also interactwith histone modification events [3233]Besides the value of predicting gene expression our
models also provide insights into the relative importanceof different epigenomicsgenome data as well as thegenomic locations We found that CpG methylation fea-tures have more predictive values for differential geneexpression compared to the three types of histone H3modification data Although other kinds of histone mod-ification data can also be obtained to increase the pre-dictive values of histone modification data it is muchmore costly to obtain them relative to the CpG methyla-tion data (the cost of CHIP-Seq on each of the histonemodification marker is similar to an entire CpG methy-lation array) Therefore practically speaking when thebudget is a constraining factor we suggest that assayson CpG methylation should be considered with priorityin predicting differential gene expression Moreover theresults of our models demonstrate that all genomic loca-tions relative to each transcript including promotersexons and gene bodies provide useful information topredict gene expression alternation Although the CpGmethylation signals from the promoters region are moreimportant the methylation signals from other regionssuch as exons introns and UTRs are indicative ofchanges in the gene expression as wellWorth noticing a lot of features that are extracted on
methylation and histone modification are naturallybased on the annotations from Illumina 450K array plat-form for DNA methylation There may be bias on thenumber of features that are hand coded in the modelTo address potential issue we changed TSS200_GC toTSS150_GC in our model and obtained an AUC = 861(compared to 0864) for cross fold validation on thetraining set and an AUC = 834 (compared to 0836) forthe testing set Therefore we think the bias due to rely-ing on the nomenclatures from Illuminarsquos annotation issmall
Limitations and future directionsWe should point out that our current model does notinclude all histone modification data but only threewidely used methylation markers on histone H3(H3K4Me3 H3K27Me3 and H3K36Me3) Moreover thehistone H3 data are drawn from ENCODE cell linessince the TCGA samples do not have such data Theheterogeneity of the sample resources could affect theaccuracy of the model When more histone marker datacoupled with DNA methylation and RNA-Seq databecome publicly available for lung cancer we caninclude them to achieve a better model In the ideal set-ting we would like to build a predictive model that hasmultiple types of epigenomics data obtained from thesame samples Another potential concern is overfitting
Li et al BMC Bioinformatics 2015 16(Suppl 5)S10httpwwwbiomedcentralcom1471-210516S5S10
Page 10 of 12
in the classification model However we split the datasetinto training and independent testing subsets and showthe model performs comparably well on the holdouttesting subset We believe that the model can be repli-cated if we can identify paired RNA-Seq and methyla-tion data In fact we had originally built this model ona private data set which also achieved an AUC of morethan 080 Additionally Figure 4 and 5 both indicatethat our approach does not suffer a significant over-fit-ting problem using the TCGA data and show the domi-nant efforts of histone modification and CpGmethylation which yield an updown gene expressionprediction with an AUCgt080 Currently the model useslung cancer data and it will be interesting find outmore general epigenetic predictors for differential geneexpression in other cancers as well Lastly we shouldpoint out that regulation of gene expression is complexincluding other mechanisms mediated by transcriptionfactors microRNA non-coding RNAs etc The fact thatAUCs hover between 080-090 ranges could be well dueto the fact that features from these other mechanismsare not considered in the current epigenetics model Toincrease the accuracy a more complex model that takesinto account of all these events should be constructed
ConclusionsA new model based on epigenomics data is proposed topredict transcriptome-level differential gene expressionin lung cancers Dropping-off feature sets by data typeshows that CpG methylation features are most impor-tant for the prediction Furthermore methylation fea-tures on all genomic regions relative to protein codinggenes contribute to the differential gene expressionwithin which promoter regions are most important
Additional material
Additional file 1 Table S1 Selected 67 features in the best modelsorted by category and their frequency
Competing interestsThe authors declare that they have no competing interests
Authorsrsquo contributionsLXG envisioned the project and supervised the work TC initiated theproject JL and TC designed and implemented the project SJ assisted theproject JL TC and LXG wrote the manuscript All authors have read revisedand approved the final manuscript
AcknowledgementsThe authors would like to thank Dr Jayson Masaki for reviewing themanuscript
DeclarationsPublication charges for this article were funded by NIHNIGMS P20 COBREGM103457 NIHNIEHS K01 ES025434-01 and Hawaii Community Foundation
This article has been published as part of BMC Bioinformatics Volume 16Supplement 5 2015 Selected articles from the 10th InternationalSymposium on Bioinformatics Research and Applications (ISBRA-14)Bioinformatics The full contents of the supplement are available online athttpwwwbiomedcentralcombmcbioinformaticssupplements16S5
Authorsrsquo details1Department of Biomedical Engineering Johns Hopkins University BaltimoreMD 21218 USA 2Molecular Biosciences and Bioengineering GraduateProgram University of Hawaii at Manoa Honolulu HI 96822 USA3Epidemiology Program University of Hawaii Cancer Center Honolulu HI96813 USA
Published 18 March 2015
References1 Portela A Esteller M Epigenetic modifications and human disease Nature
biotechnology 2010 28(10)1057-10682 Bock C Lengauer T Computational epigenetics Bioinformatics 2008
24(1)1-103 Laird PW Principles and challenges of genomewide DNA methylation
analysis Nature reviews Genetics 2010 11(3)191-2034 Lim SJ Tan TW Tong JC Computational Epigenetics the new scientific
paradigm Bioinformation 2010 4(7)331-3375 Gardiner-Garden M Frommer M CpG islands in vertebrate genomes
Journal of molecular biology 1987 196(2)261-2826 Daura-Oller E Cabre M Montero MA Paternain JL Romeu A Specific gene
hypomethylation and cancer New insights into coding region featuretrends Bioinformation 2009 3(8)340
7 Wild L Flanagan JM Genome-wide hypomethylation in cancer may be apassive consequence of transformation Biochimica et biophysica acta2010 1806(1)50-57
8 Figueroa ME Chen SC Andersson AK Phillips LA Li Y Sotzen J Kundu MDowning JR Melnick A Mullighan CG Integrated genetic and epigeneticanalysis of childhood acute lymphoblastic leukemia The Journal ofclinical investigation 2013 123(7)3099-3111
9 Rhee JK Kim K Chae H Evans J Yan P Zhang BT Gray J Spellman PHuang TH Nephew KP et al Integrated analysis of genome-wide DNAmethylation and gene expression profiles in molecular subtypes ofbreast cancer Nucleic acids research 2013 41(18)8464-8474
10 An integrated encyclopedia of DNA elements in the human genomeNature 2012 489(7414)57-74
11 Karolchik D Hinrichs AS Furey TS Roskin KM Sugnet CW Haussler DKent WJ The UCSC Table Browser data retrieval tool Nucleic acidsresearch 2004 32 Database D493-496
12 Langmead B Salzberg SL Fast gapped-read alignment with Bowtie 2Nature methods 2012 9(4)357-359
13 Li H Handsaker B Wysoker A Fennell T Ruan J Homer N Marth GAbecasis G Durbin R Genome Project Data Processing S The SequenceAlignmentMap format and SAMtools Bioinformatics 200925(16)2078-2079
14 Quinlan AR Hall IM BEDTools a flexible suite of utilities for comparinggenomic features Bioinformatics 2010 26(6)841-842
15 Siepel A Bejerano G Pedersen JS Hinrichs AS Hou M Rosenbloom KClawson H Spieth J Hillier LW Richards S Evolutionarily conservedelements in vertebrate insect worm and yeast genomes Genomeresearch 2005 15(8)1034-1050
16 Love MI Huber W Anders S Moderated estimation of fold change anddispersion for RNA-Seq data with DESeq2 bioRxiv 2014
17 Holm S A simple sequentially rejective multiple test procedureScandinavian journal of statistics 1979 65-70
18 Smyth GK Limma linear models for microarray data Bioinformatics andcomputational biology solutions using R and Bioconductor Springer 2005397-420
19 Quinlan AR Hall IM BEDTools a flexible suite of utilities for comparinggenomic features Bioinformatics 2010 26(6)841-842
20 Pages H Aboyoun P Gentleman R DebRoy S String objects representingbiological sequences and matching algorithms R package version 2009 2(2)
21 Hall MA Smith LA Feature Selection for Machine Learning Comparing aCorrelation-Based Filter Approach to the Wrapper FLAIRS Conference1999 1999 235-239
Li et al BMC Bioinformatics 2015 16(Suppl 5)S10httpwwwbiomedcentralcom1471-210516S5S10
Page 11 of 12
22 Quinlan JR Induction of decision trees Machine learning 1986 1(1)81-10623 Kononenko I Šimec E Robnik-Šikonja M Overcoming the Myopia of
Inductive Learning Algorithms with RELIEFF Applied Intelligence 19977(1)39-55
24 Kira K Rendell LA The feature selection problem Traditional methodsand a new algorithm AAAI 1992 1992 129-134
25 Hall M Frank E Holmes G Pfahringer B Reutemann P Witten IH TheWEKA data mining software an update ACM SIGKDD ExplorationsNewsletter 2009 11(1)10-18
26 Wei T Corrplot visualization of a correlation matrix R package version 02-0 Available on Comprehensive R Archive Network website 2010 [httpCRANR-project orgpackage=corrplot] (accessed 2010)
27 Sing T Sander O Beerenwinkel N Lengauer T ROCR visualizing classifierperformance in R Bioinformatics 2005 21(20)3940-3941
28 Fuks F Hurd PJ Wolf D Nan X Bird AP Kouzarides T The methyl-CpG-binding protein MeCP2 links DNA methylation to histone methylationJournal of Biological Chemistry 2003 278(6)4035-4040
29 Cedar H Bergman Y Linking DNA methylation and histone modificationpatterns and paradigms Nat Rev Genet 2009 10(5)295-304
30 Jones PA Functions of DNA methylation islands start sites gene bodiesand beyond Nat Rev Genet 2012 13(7)484-492
31 Maunakea AK Nagarajan RP Bilenky M Ballinger TJ DrsquoSouza C Fouse SDJohnson BE Hong C Nielsen C Zhao Y Conserved role of intragenic DNAmethylation in regulating alternative promoters Nature 2010466(7303)253-257
32 Bowen NJ Palmer MB Wade PA Chromosomal regulation by MeCP2structural and enzymatic considerations Cellular and molecular lifesciences CMLS 2004 61(17)2163-2167
33 Ooi SK Qiu C Bernstein E Li K Jia D Yang Z Erdjument-Bromage HTempst P Lin SP Allis CD et al DNMT3L connects unmethylated lysine 4of histone H3 to de novo methylation of DNA Nature 2007448(7154)714-717
doi1011861471-2105-16-S5-S10Cite this article as Li et al Using epigenomics data to predict geneexpression in lung cancer BMC Bioinformatics 2015 16(Suppl 5)S10
Submit your next manuscript to BioMed Centraland take full advantage of
bull Convenient online submission
bull Thorough peer review
bull No space constraints or color figure charges
bull Immediate publication on acceptance
bull Inclusion in PubMed CAS Scopus and Google Scholar
bull Research which is freely available for redistribution
Submit your manuscript at wwwbiomedcentralcomsubmit
Li et al BMC Bioinformatics 2015 16(Suppl 5)S10httpwwwbiomedcentralcom1471-210516S5S10
Page 12 of 12
Abstract
Background
Methods
Results
Conclusions
Background
Methods
Data sets
Data processing
Methylation data
Histone data
Human genome data
RNA-seq data
Feature extraction
CpG Methylation features
Histone marker modification features
Nucleotide features
Conservation features
Feature selection
Model evaluation
Software
Results
Summary of input data and features
Model selection and evaluation
Analysis of selected features
Evaluation of features by data type
Evaluation of CpG methylation features by locations relative to transcripts
Discussion
The need to build predictive models of gene expression from epigenomics data
Selected features and their relevance to gene expression
Limitations and future directions
Conclusions
Competing interests
Authorsrsquo contributions
Acknowledgements
Declarations
Authorsrsquo details
References
minimize redundancy Relevance is the correlation of afeature to the class (up-regulated or down-regulatedgene expression) measured by mutual informationwhereas redundancy is the correlation between two fea-tures Redundancy between selected features is mini-mized to keep the number of selected feature smallThe Gain Ratio is an improved method of Information
Gain (IG) Both feature selection methods employ adecision tree in their respective algorithms The GainRatio by name is a ratio of IG but it overcomes thebias of IG which favors features with more dataReliefF is an improved feature selection method from
Relief Relief uses the Manhattan distance of its nearesthit and miss from a random instance to continuouslyupdate a weight vector which is then used to calculate arelevance score Features above a certain relevancethreshold are considered ldquoselectedrdquo [24] ReliefF improveson Relief in several ways including two improvementsparticularly important for this report First ReliefFextends Relief to be able to handle incomplete or partialdata Second ReliefF searches for k near-hits and near-misses and takes their averages as opposed to one near-est hit or miss from Relief k =10 was sufficient to obtainsatisfactory results [23]CFS is the only method that has a built-in system for
selecting the number of features Gain Ratio and ReliefFboth work as ranker systems meaning every input has amatching respective ranked output In order to ensurefairness between feature selection methods we matchedthe numbers of selected features from Gain Ratio andReliefF to be the same as determined by CFS
Model evaluationThe data were split into training and testing sets Thetraining set constituted 80 of the up-regulated anddown-regulated genes and the testing set constituted theremaining 20 genes The training data set underwent10-fold cross validation on various combinations of fea-ture selection and classification methods in order toobtain the best modelAfter determining the best model two sets of drop-off
tests were conducted The first set of tests considered theeffect of data types including nucleotide compositionhistone markers and methylation data on the perfor-mances of sub-models The second set of drop-off testsconsidered the effects of different segments on tran-scripts including gene body exons introns UTRsTSS1500 and TSS200 on the methylation CpG methyla-tion data based sub-models For each drop-off test a setof features was removed from the original input featuresprior to the feature selection and classification Subse-quently the same ReliefF feature selection and RF classifi-cation for the drop-off tests were performed as describedin the previous Feature Selection section
SoftwareWeka 3 data mining software [25] was used for featureselection classifier training and evaluation Various Rpackages were used including Corrplot for generationof the correlation matrix [26] and ROCR for ROCcurves [27] The classification model is available athttpsgithubcomlanagarmireepiPredictor
ResultsSummary of input data and featuresFour types of input data were used to extract the fea-tures including the Illumina 450K CpG methylationarray data from cancer and normal tissues three typesof histone H3 marker CHIP-Seq data from cancer andnormal cell lines genomic nucleotide sequence and con-servation data and RNA-Seq gene expression data fromsamples with coupled CpG methylation data In totalwe calculated 1424 features and summarized the fea-tures by column These features can be divided into twocategories (Table 1) (1) data type based features includ-ing average CpG methylation average methylation logfold change number of hyperhypo-methylated probesmono-nucleotide di-nucleotide and tri-nucleotide com-position histone H3 methylation CHIP-Seq reads andPhastcon conservation scores (2) segment based CpGmethylation features from Illumina 450K BeadChipannotations upstream of the transcription start site(TSS) 1500 TSS200 5rsquo and 3rsquo UTRs exonintron bodyfirst and last exonintron single exonintron and fulltranscript (Figure 1 and Table 1)
Model selection and evaluationThe model uses 2298 gene data points in the training setwith an additional 576 genes kept in the testing set Threedifferent feature selection methods were evaluated in com-bination with five classification methods using 10-foldcross-validation on the training data set (Figure 2) Thethree feature selection methods are correlation-based fea-ture selection (CFS) ReliefF and Gain Ratio In mostcases with combined classification methods except forGaussian SVM ReliefF gives the best AUCs among thethree feature selection methods Among the five classifica-tion methods that we considered namely Gaussian SVMlinear SVM Logistic Regression Naiumlve Bayes and RandomForest the two non-linear methods (Gaussian SVM andRandom Forest) show superior performances to the otherlinear classifiers (Logistic Regression linear SVM andNaiumlve Bayes) This indicates that interactions exist amongthe selected features However the differences are not verybig suggesting that the decision boundary is close to lin-ear Given that the model based on ReliefF feature selec-tion and Random Forest classification gives the best AUCof 0864 it is selected as the best model for the rest of theproject Similarly a ReliefF and Random Forest based
Li et al BMC Bioinformatics 2015 16(Suppl 5)S10httpwwwbiomedcentralcom1471-210516S5S10
Page 4 of 12
Table 1 The list of all features considered prior to feature selectionAverageM value(Methylation)
model has the best predictive performance on the 20holdout data set with an AUC of 0836
Analysis of selected featuresA total of 67 features are selected by the best modelspanning all four types of genomic and epigenomic data
We first explored the relationship among the selectedfeatures Using hierarchical clustering on absolute corre-lation values between features (Figure 3A) we found thatthe selected features tend to cluster by the data type asexpected For example the conservation features in thecoding regions (CDS) are grouped together and so are
Figure 1 Segments associated with protein coding genes Features considered to predict differential gene expression are depicted on asegment-by-segment basis Segments are determined based on the annotations of Illumina Infinium Human Methylation 450K Beadchip Arraywith augmentations on segments located in gene bodies From 5rsquo to 3rsquo end of the protein coding genes listed are transcription starting sites(TSS) upstream up to 1500 bp (TSS 1500) and 200 bp (TSS 200) first exon which may include 5rsquo UTR first intron exon body last intron and lastexon which may include 3rsquo UTR A full transcript region is determined as the UTRs and coding region together
Figure 2 Performance comparison of models with various feature selection and classification methods The Areas Under the Curve (AUC)of ROC are used as the metric to compare the performance of models with different combinations of feature selection (CFS Gain Ratios andReliefF) and classification (Gaussian SVM Linear SVM Logistic regression Naiumlve Bayes and Random Forest) on the training data with 10 foldcross-validation The model with ReliefF based feature selection and Random Forest classification is selected as the best model
Li et al BMC Bioinformatics 2015 16(Suppl 5)S10httpwwwbiomedcentralcom1471-210516S5S10
Page 6 of 12
most methylation features As expected the CpG islandswithin the promoter are very important for the predictionof gene expression as demonstrated by the three selectedand highly correlated features CG composition featuresTSS200_GC TSS200_CG and TSS200_CGGThe largest group of selected features is the histone
modification features (32 features) followed by the methy-lation features (15 features) (Additional File 1) Theselected features underscore the importance of histoneepigenetic modification in the regulation of gene expres-sion Likewise the importance of methylation features isevident especially for the featured arising from TSS 5rsquoUTR and first exons Interestingly several methylation fea-tures (TSS1500_avgMval first_exon_avgMval andUTR5_avgMval) are clustered with histone modificationfeatures suggesting collinearity between these two types offeatures as shown by others [2829]On the other hand when features are categorized by
location relative to the transcripts (Additional File 1) theTSS200 has the most number of features (13 features)and TSS1500 has 6 features selected for this regionTogether the promoter comprises 28 of all the selectedfeatures This confirms the previously well-known impor-tance of the promoter region for the epigenetic regulationof gene expression [3031] Additionally CDS has the sec-ond highest number of features being selected highlight-ing its significance in regulating gene expression [30]
We also calculated the correlation of each feature togene expression and plotted the top 15 features mostrelevant to gene expression prediction (Figure 3B) Noneof the features have correlations higher than 045 sug-gesting that no single feature is a dominant predictorfor gene expression These features are either histonemodification (11 features) or methylation features (4 fea-tures) consistent with the previous observation on thesignificance of these two types of features
Evaluation of features by data typeTo determine the contribution of different types of fea-tures to gene expression we tested the performance ofmodels when a subset of features from the same datatype were dropped We present the results of four mea-sures of model performance AUC accuracy F-measureand Matthewrsquos correlation coefficient (MCC) (Figure 4)Dropping any individual feature set of nucleotide compo-sition histone modification or CpG methylation did notseem to have a large effect on the model performanceindicating that there is redundancy between feature setsThe sub-model performance for the dropping-off of asingle feature set from the full model is in the followingorder nucleotide composition removal gt histone modifi-cation removal gt CpG methylation removal Thus drop-ping methylation features had the largest effect amongindividual feature set as the AUC decreases from 0864
Figure 3 Top fifteen features from the best model (a) The clustering results on the absolute values of Pearsonrsquos correlation coefficients from67 selected features by the best model The names of different type of features are labeled by different colors Note the length of a segment islisted out separately (b) List of top fifteen features selected by ReliefF feature selection and sorted by their correlation to the classification ofdifferential gene expression
Li et al BMC Bioinformatics 2015 16(Suppl 5)S10httpwwwbiomedcentralcom1471-210516S5S10
Page 7 of 12
in the full model to 0832 in the training set as well asfrom 0836 to 0810 in the testing set Likewise MCCupon single feature set drop-off shows the largest pro-portional change among the four performance measuresand decreases from 056 to 049 on the training set and051 to 045 on the testing setWe also compared the effect of removing both nucleo-
tide and histone features on model performance as com-pared to removing either of them alone As expectedremoving both nucleotide and histone features gives thelowest AUCs lowest accuracies and lowest F-measures in
both training and testing sets However it leads to higherMCC than removing just histones does in the testing setThis suggests that there might be some overfitting withregards to the nucleotide feature set which accounts forthe majority (83) of features prior to feature selection
Evaluation of CpG methylation features by locationsrelative to transcriptsGiven that removing methylation features causes themost reduction of model performance among the singlefeature set drop-off (Figure 5) we next asked the
Figure 4 Evaluation of features generated from various data types (a-b) Effects of feature set drop-off on ROC curves from the 10-foldcross-validation training set (a) and testing set (b) (c) Effects of feature set drop-off on other four metrics AUC Accuracy F-measure and MCCin the training set and testing set
Li et al BMC Bioinformatics 2015 16(Suppl 5)S10httpwwwbiomedcentralcom1471-210516S5S10
Page 8 of 12
question of the relative importance of each methylationfeature categorized by genomic location We performeddrop-off tests by sequentially removing features in eachgenomic location category We first removed the featuresfrom the first exons and first introns (as they are close to
the TSS) then from gene bodies including exons intronsand UTRs and lastly from TSS1500 region such that onlyTSS200 features were kept At each step we re-performedfeature selection and model construction using theremaining methylation features
Figure 5 Evaluation of methylation features by segment (a-b) Effects of segment-based methylation feature set sequential drop-off on ROCcurves from the 10-fold cross-validation training set (a) and testing set (b) (c) Effects of segment-based methylation feature set sequential drop-off on other four metrics AUC Accuracy F-measure and MCC in the training set and testing set
Li et al BMC Bioinformatics 2015 16(Suppl 5)S10httpwwwbiomedcentralcom1471-210516S5S10
Page 9 of 12
All categories of genomic locations provide relevantuseful information that contributes to better predictionof gene expression as each of the sequential feature setdrop-off decreased the performance of the model in bothtraining and testing sets Compared to the removal offirst exon and intron regions and removal of the UTRsand the rest of the gene body removal of TSS1500 leadsto the largest reduction in all four metrics confirmingthe importance of the promoter region in regulating geneexpression Even when only TSS200 features are consid-ered an AUC of 0638 and 0636 are obtained in the test-ing and training sets respectively suggesting that CpGmethylation status in TSS200 is still somewhat predictiveof gene expression However a more accurate predictionusing methylation features should arise from all locationsassociated with the transcripts
DiscussionThe need to build predictive models of gene expressionfrom epigenomics dataAlthough currently integrative analyses between geneexpression and epigenetic modification exist we havefound that quantitative models using epigenetic informa-tion to accurately predict the up or down regulation ofgene expression are currently lacking There are oftencases where researchers can only obtain reliable epige-netics data but not gene expression data For example ifthe samples are archived and processed by FFPE (Forma-lin-fixed paraffin-embedded) one can still perform epige-nomics measurements but not the gene expressionexperiments due to the degradation of mRNA in the sam-ples More importantly a predictive method such as ourscan efficiently facilitate the bench scientists to narrowdown the candidate lists and conduct gene expression vali-dation especially when the epigenetics information is theonly data handy
Selected features and their relevance to gene expressionAll four types of data (CpG methylation histone H3modification nucleotide sequence and conservation)exist in the 67 features that are selected by the bestmodel indicating that all of them contribute to the accu-rate prediction of gene expression Moreover selectedfeatures of the same data type tend to cluster together onthe correlation matrix among the features suggestingthat the relationship within the same data type is closerthan the relationship between different data types Asexpected histone modification and CpG methylation fea-tures are the largest two groups among the four types ofdata signifying their importance to predict gene expres-sion Since nonlinear classification methods performslightly better than linear classification methods it sug-gests that interactions do exist between different types ofdata This is supported by numerous literatures that
enzymes responsible for CpG methylation also interactwith histone modification events [3233]Besides the value of predicting gene expression our
models also provide insights into the relative importanceof different epigenomicsgenome data as well as thegenomic locations We found that CpG methylation fea-tures have more predictive values for differential geneexpression compared to the three types of histone H3modification data Although other kinds of histone mod-ification data can also be obtained to increase the pre-dictive values of histone modification data it is muchmore costly to obtain them relative to the CpG methyla-tion data (the cost of CHIP-Seq on each of the histonemodification marker is similar to an entire CpG methy-lation array) Therefore practically speaking when thebudget is a constraining factor we suggest that assayson CpG methylation should be considered with priorityin predicting differential gene expression Moreover theresults of our models demonstrate that all genomic loca-tions relative to each transcript including promotersexons and gene bodies provide useful information topredict gene expression alternation Although the CpGmethylation signals from the promoters region are moreimportant the methylation signals from other regionssuch as exons introns and UTRs are indicative ofchanges in the gene expression as wellWorth noticing a lot of features that are extracted on
methylation and histone modification are naturallybased on the annotations from Illumina 450K array plat-form for DNA methylation There may be bias on thenumber of features that are hand coded in the modelTo address potential issue we changed TSS200_GC toTSS150_GC in our model and obtained an AUC = 861(compared to 0864) for cross fold validation on thetraining set and an AUC = 834 (compared to 0836) forthe testing set Therefore we think the bias due to rely-ing on the nomenclatures from Illuminarsquos annotation issmall
Limitations and future directionsWe should point out that our current model does notinclude all histone modification data but only threewidely used methylation markers on histone H3(H3K4Me3 H3K27Me3 and H3K36Me3) Moreover thehistone H3 data are drawn from ENCODE cell linessince the TCGA samples do not have such data Theheterogeneity of the sample resources could affect theaccuracy of the model When more histone marker datacoupled with DNA methylation and RNA-Seq databecome publicly available for lung cancer we caninclude them to achieve a better model In the ideal set-ting we would like to build a predictive model that hasmultiple types of epigenomics data obtained from thesame samples Another potential concern is overfitting
Li et al BMC Bioinformatics 2015 16(Suppl 5)S10httpwwwbiomedcentralcom1471-210516S5S10
Page 10 of 12
in the classification model However we split the datasetinto training and independent testing subsets and showthe model performs comparably well on the holdouttesting subset We believe that the model can be repli-cated if we can identify paired RNA-Seq and methyla-tion data In fact we had originally built this model ona private data set which also achieved an AUC of morethan 080 Additionally Figure 4 and 5 both indicatethat our approach does not suffer a significant over-fit-ting problem using the TCGA data and show the domi-nant efforts of histone modification and CpGmethylation which yield an updown gene expressionprediction with an AUCgt080 Currently the model useslung cancer data and it will be interesting find outmore general epigenetic predictors for differential geneexpression in other cancers as well Lastly we shouldpoint out that regulation of gene expression is complexincluding other mechanisms mediated by transcriptionfactors microRNA non-coding RNAs etc The fact thatAUCs hover between 080-090 ranges could be well dueto the fact that features from these other mechanismsare not considered in the current epigenetics model Toincrease the accuracy a more complex model that takesinto account of all these events should be constructed
ConclusionsA new model based on epigenomics data is proposed topredict transcriptome-level differential gene expressionin lung cancers Dropping-off feature sets by data typeshows that CpG methylation features are most impor-tant for the prediction Furthermore methylation fea-tures on all genomic regions relative to protein codinggenes contribute to the differential gene expressionwithin which promoter regions are most important
Additional material
Additional file 1 Table S1 Selected 67 features in the best modelsorted by category and their frequency
Competing interestsThe authors declare that they have no competing interests
Authorsrsquo contributionsLXG envisioned the project and supervised the work TC initiated theproject JL and TC designed and implemented the project SJ assisted theproject JL TC and LXG wrote the manuscript All authors have read revisedand approved the final manuscript
AcknowledgementsThe authors would like to thank Dr Jayson Masaki for reviewing themanuscript
DeclarationsPublication charges for this article were funded by NIHNIGMS P20 COBREGM103457 NIHNIEHS K01 ES025434-01 and Hawaii Community Foundation
This article has been published as part of BMC Bioinformatics Volume 16Supplement 5 2015 Selected articles from the 10th InternationalSymposium on Bioinformatics Research and Applications (ISBRA-14)Bioinformatics The full contents of the supplement are available online athttpwwwbiomedcentralcombmcbioinformaticssupplements16S5
Authorsrsquo details1Department of Biomedical Engineering Johns Hopkins University BaltimoreMD 21218 USA 2Molecular Biosciences and Bioengineering GraduateProgram University of Hawaii at Manoa Honolulu HI 96822 USA3Epidemiology Program University of Hawaii Cancer Center Honolulu HI96813 USA
Published 18 March 2015
References1 Portela A Esteller M Epigenetic modifications and human disease Nature
biotechnology 2010 28(10)1057-10682 Bock C Lengauer T Computational epigenetics Bioinformatics 2008
24(1)1-103 Laird PW Principles and challenges of genomewide DNA methylation
analysis Nature reviews Genetics 2010 11(3)191-2034 Lim SJ Tan TW Tong JC Computational Epigenetics the new scientific
paradigm Bioinformation 2010 4(7)331-3375 Gardiner-Garden M Frommer M CpG islands in vertebrate genomes
Journal of molecular biology 1987 196(2)261-2826 Daura-Oller E Cabre M Montero MA Paternain JL Romeu A Specific gene
hypomethylation and cancer New insights into coding region featuretrends Bioinformation 2009 3(8)340
7 Wild L Flanagan JM Genome-wide hypomethylation in cancer may be apassive consequence of transformation Biochimica et biophysica acta2010 1806(1)50-57
8 Figueroa ME Chen SC Andersson AK Phillips LA Li Y Sotzen J Kundu MDowning JR Melnick A Mullighan CG Integrated genetic and epigeneticanalysis of childhood acute lymphoblastic leukemia The Journal ofclinical investigation 2013 123(7)3099-3111
9 Rhee JK Kim K Chae H Evans J Yan P Zhang BT Gray J Spellman PHuang TH Nephew KP et al Integrated analysis of genome-wide DNAmethylation and gene expression profiles in molecular subtypes ofbreast cancer Nucleic acids research 2013 41(18)8464-8474
10 An integrated encyclopedia of DNA elements in the human genomeNature 2012 489(7414)57-74
11 Karolchik D Hinrichs AS Furey TS Roskin KM Sugnet CW Haussler DKent WJ The UCSC Table Browser data retrieval tool Nucleic acidsresearch 2004 32 Database D493-496
12 Langmead B Salzberg SL Fast gapped-read alignment with Bowtie 2Nature methods 2012 9(4)357-359
13 Li H Handsaker B Wysoker A Fennell T Ruan J Homer N Marth GAbecasis G Durbin R Genome Project Data Processing S The SequenceAlignmentMap format and SAMtools Bioinformatics 200925(16)2078-2079
14 Quinlan AR Hall IM BEDTools a flexible suite of utilities for comparinggenomic features Bioinformatics 2010 26(6)841-842
15 Siepel A Bejerano G Pedersen JS Hinrichs AS Hou M Rosenbloom KClawson H Spieth J Hillier LW Richards S Evolutionarily conservedelements in vertebrate insect worm and yeast genomes Genomeresearch 2005 15(8)1034-1050
16 Love MI Huber W Anders S Moderated estimation of fold change anddispersion for RNA-Seq data with DESeq2 bioRxiv 2014
17 Holm S A simple sequentially rejective multiple test procedureScandinavian journal of statistics 1979 65-70
18 Smyth GK Limma linear models for microarray data Bioinformatics andcomputational biology solutions using R and Bioconductor Springer 2005397-420
19 Quinlan AR Hall IM BEDTools a flexible suite of utilities for comparinggenomic features Bioinformatics 2010 26(6)841-842
20 Pages H Aboyoun P Gentleman R DebRoy S String objects representingbiological sequences and matching algorithms R package version 2009 2(2)
21 Hall MA Smith LA Feature Selection for Machine Learning Comparing aCorrelation-Based Filter Approach to the Wrapper FLAIRS Conference1999 1999 235-239
Li et al BMC Bioinformatics 2015 16(Suppl 5)S10httpwwwbiomedcentralcom1471-210516S5S10
Page 11 of 12
22 Quinlan JR Induction of decision trees Machine learning 1986 1(1)81-10623 Kononenko I Šimec E Robnik-Šikonja M Overcoming the Myopia of
Inductive Learning Algorithms with RELIEFF Applied Intelligence 19977(1)39-55
24 Kira K Rendell LA The feature selection problem Traditional methodsand a new algorithm AAAI 1992 1992 129-134
25 Hall M Frank E Holmes G Pfahringer B Reutemann P Witten IH TheWEKA data mining software an update ACM SIGKDD ExplorationsNewsletter 2009 11(1)10-18
26 Wei T Corrplot visualization of a correlation matrix R package version 02-0 Available on Comprehensive R Archive Network website 2010 [httpCRANR-project orgpackage=corrplot] (accessed 2010)
27 Sing T Sander O Beerenwinkel N Lengauer T ROCR visualizing classifierperformance in R Bioinformatics 2005 21(20)3940-3941
28 Fuks F Hurd PJ Wolf D Nan X Bird AP Kouzarides T The methyl-CpG-binding protein MeCP2 links DNA methylation to histone methylationJournal of Biological Chemistry 2003 278(6)4035-4040
29 Cedar H Bergman Y Linking DNA methylation and histone modificationpatterns and paradigms Nat Rev Genet 2009 10(5)295-304
30 Jones PA Functions of DNA methylation islands start sites gene bodiesand beyond Nat Rev Genet 2012 13(7)484-492
31 Maunakea AK Nagarajan RP Bilenky M Ballinger TJ DrsquoSouza C Fouse SDJohnson BE Hong C Nielsen C Zhao Y Conserved role of intragenic DNAmethylation in regulating alternative promoters Nature 2010466(7303)253-257
32 Bowen NJ Palmer MB Wade PA Chromosomal regulation by MeCP2structural and enzymatic considerations Cellular and molecular lifesciences CMLS 2004 61(17)2163-2167
33 Ooi SK Qiu C Bernstein E Li K Jia D Yang Z Erdjument-Bromage HTempst P Lin SP Allis CD et al DNMT3L connects unmethylated lysine 4of histone H3 to de novo methylation of DNA Nature 2007448(7154)714-717
doi1011861471-2105-16-S5-S10Cite this article as Li et al Using epigenomics data to predict geneexpression in lung cancer BMC Bioinformatics 2015 16(Suppl 5)S10
Submit your next manuscript to BioMed Centraland take full advantage of
bull Convenient online submission
bull Thorough peer review
bull No space constraints or color figure charges
bull Immediate publication on acceptance
bull Inclusion in PubMed CAS Scopus and Google Scholar
bull Research which is freely available for redistribution
Submit your manuscript at wwwbiomedcentralcomsubmit
Li et al BMC Bioinformatics 2015 16(Suppl 5)S10httpwwwbiomedcentralcom1471-210516S5S10
Page 12 of 12
Abstract
Background
Methods
Results
Conclusions
Background
Methods
Data sets
Data processing
Methylation data
Histone data
Human genome data
RNA-seq data
Feature extraction
CpG Methylation features
Histone marker modification features
Nucleotide features
Conservation features
Feature selection
Model evaluation
Software
Results
Summary of input data and features
Model selection and evaluation
Analysis of selected features
Evaluation of features by data type
Evaluation of CpG methylation features by locations relative to transcripts
Discussion
The need to build predictive models of gene expression from epigenomics data
Selected features and their relevance to gene expression
Limitations and future directions
Conclusions
Competing interests
Authorsrsquo contributions
Acknowledgements
Declarations
Authorsrsquo details
References
Table 1 The list of all features considered prior to feature selectionAverageM value(Methylation)
model has the best predictive performance on the 20holdout data set with an AUC of 0836
Analysis of selected featuresA total of 67 features are selected by the best modelspanning all four types of genomic and epigenomic data
We first explored the relationship among the selectedfeatures Using hierarchical clustering on absolute corre-lation values between features (Figure 3A) we found thatthe selected features tend to cluster by the data type asexpected For example the conservation features in thecoding regions (CDS) are grouped together and so are
Figure 1 Segments associated with protein coding genes Features considered to predict differential gene expression are depicted on asegment-by-segment basis Segments are determined based on the annotations of Illumina Infinium Human Methylation 450K Beadchip Arraywith augmentations on segments located in gene bodies From 5rsquo to 3rsquo end of the protein coding genes listed are transcription starting sites(TSS) upstream up to 1500 bp (TSS 1500) and 200 bp (TSS 200) first exon which may include 5rsquo UTR first intron exon body last intron and lastexon which may include 3rsquo UTR A full transcript region is determined as the UTRs and coding region together
Figure 2 Performance comparison of models with various feature selection and classification methods The Areas Under the Curve (AUC)of ROC are used as the metric to compare the performance of models with different combinations of feature selection (CFS Gain Ratios andReliefF) and classification (Gaussian SVM Linear SVM Logistic regression Naiumlve Bayes and Random Forest) on the training data with 10 foldcross-validation The model with ReliefF based feature selection and Random Forest classification is selected as the best model
Li et al BMC Bioinformatics 2015 16(Suppl 5)S10httpwwwbiomedcentralcom1471-210516S5S10
Page 6 of 12
most methylation features As expected the CpG islandswithin the promoter are very important for the predictionof gene expression as demonstrated by the three selectedand highly correlated features CG composition featuresTSS200_GC TSS200_CG and TSS200_CGGThe largest group of selected features is the histone
modification features (32 features) followed by the methy-lation features (15 features) (Additional File 1) Theselected features underscore the importance of histoneepigenetic modification in the regulation of gene expres-sion Likewise the importance of methylation features isevident especially for the featured arising from TSS 5rsquoUTR and first exons Interestingly several methylation fea-tures (TSS1500_avgMval first_exon_avgMval andUTR5_avgMval) are clustered with histone modificationfeatures suggesting collinearity between these two types offeatures as shown by others [2829]On the other hand when features are categorized by
location relative to the transcripts (Additional File 1) theTSS200 has the most number of features (13 features)and TSS1500 has 6 features selected for this regionTogether the promoter comprises 28 of all the selectedfeatures This confirms the previously well-known impor-tance of the promoter region for the epigenetic regulationof gene expression [3031] Additionally CDS has the sec-ond highest number of features being selected highlight-ing its significance in regulating gene expression [30]
We also calculated the correlation of each feature togene expression and plotted the top 15 features mostrelevant to gene expression prediction (Figure 3B) Noneof the features have correlations higher than 045 sug-gesting that no single feature is a dominant predictorfor gene expression These features are either histonemodification (11 features) or methylation features (4 fea-tures) consistent with the previous observation on thesignificance of these two types of features
Evaluation of features by data typeTo determine the contribution of different types of fea-tures to gene expression we tested the performance ofmodels when a subset of features from the same datatype were dropped We present the results of four mea-sures of model performance AUC accuracy F-measureand Matthewrsquos correlation coefficient (MCC) (Figure 4)Dropping any individual feature set of nucleotide compo-sition histone modification or CpG methylation did notseem to have a large effect on the model performanceindicating that there is redundancy between feature setsThe sub-model performance for the dropping-off of asingle feature set from the full model is in the followingorder nucleotide composition removal gt histone modifi-cation removal gt CpG methylation removal Thus drop-ping methylation features had the largest effect amongindividual feature set as the AUC decreases from 0864
Figure 3 Top fifteen features from the best model (a) The clustering results on the absolute values of Pearsonrsquos correlation coefficients from67 selected features by the best model The names of different type of features are labeled by different colors Note the length of a segment islisted out separately (b) List of top fifteen features selected by ReliefF feature selection and sorted by their correlation to the classification ofdifferential gene expression
Li et al BMC Bioinformatics 2015 16(Suppl 5)S10httpwwwbiomedcentralcom1471-210516S5S10
Page 7 of 12
in the full model to 0832 in the training set as well asfrom 0836 to 0810 in the testing set Likewise MCCupon single feature set drop-off shows the largest pro-portional change among the four performance measuresand decreases from 056 to 049 on the training set and051 to 045 on the testing setWe also compared the effect of removing both nucleo-
tide and histone features on model performance as com-pared to removing either of them alone As expectedremoving both nucleotide and histone features gives thelowest AUCs lowest accuracies and lowest F-measures in
both training and testing sets However it leads to higherMCC than removing just histones does in the testing setThis suggests that there might be some overfitting withregards to the nucleotide feature set which accounts forthe majority (83) of features prior to feature selection
Evaluation of CpG methylation features by locationsrelative to transcriptsGiven that removing methylation features causes themost reduction of model performance among the singlefeature set drop-off (Figure 5) we next asked the
Figure 4 Evaluation of features generated from various data types (a-b) Effects of feature set drop-off on ROC curves from the 10-foldcross-validation training set (a) and testing set (b) (c) Effects of feature set drop-off on other four metrics AUC Accuracy F-measure and MCCin the training set and testing set
Li et al BMC Bioinformatics 2015 16(Suppl 5)S10httpwwwbiomedcentralcom1471-210516S5S10
Page 8 of 12
question of the relative importance of each methylationfeature categorized by genomic location We performeddrop-off tests by sequentially removing features in eachgenomic location category We first removed the featuresfrom the first exons and first introns (as they are close to
the TSS) then from gene bodies including exons intronsand UTRs and lastly from TSS1500 region such that onlyTSS200 features were kept At each step we re-performedfeature selection and model construction using theremaining methylation features
Figure 5 Evaluation of methylation features by segment (a-b) Effects of segment-based methylation feature set sequential drop-off on ROCcurves from the 10-fold cross-validation training set (a) and testing set (b) (c) Effects of segment-based methylation feature set sequential drop-off on other four metrics AUC Accuracy F-measure and MCC in the training set and testing set
Li et al BMC Bioinformatics 2015 16(Suppl 5)S10httpwwwbiomedcentralcom1471-210516S5S10
Page 9 of 12
All categories of genomic locations provide relevantuseful information that contributes to better predictionof gene expression as each of the sequential feature setdrop-off decreased the performance of the model in bothtraining and testing sets Compared to the removal offirst exon and intron regions and removal of the UTRsand the rest of the gene body removal of TSS1500 leadsto the largest reduction in all four metrics confirmingthe importance of the promoter region in regulating geneexpression Even when only TSS200 features are consid-ered an AUC of 0638 and 0636 are obtained in the test-ing and training sets respectively suggesting that CpGmethylation status in TSS200 is still somewhat predictiveof gene expression However a more accurate predictionusing methylation features should arise from all locationsassociated with the transcripts
DiscussionThe need to build predictive models of gene expressionfrom epigenomics dataAlthough currently integrative analyses between geneexpression and epigenetic modification exist we havefound that quantitative models using epigenetic informa-tion to accurately predict the up or down regulation ofgene expression are currently lacking There are oftencases where researchers can only obtain reliable epige-netics data but not gene expression data For example ifthe samples are archived and processed by FFPE (Forma-lin-fixed paraffin-embedded) one can still perform epige-nomics measurements but not the gene expressionexperiments due to the degradation of mRNA in the sam-ples More importantly a predictive method such as ourscan efficiently facilitate the bench scientists to narrowdown the candidate lists and conduct gene expression vali-dation especially when the epigenetics information is theonly data handy
Selected features and their relevance to gene expressionAll four types of data (CpG methylation histone H3modification nucleotide sequence and conservation)exist in the 67 features that are selected by the bestmodel indicating that all of them contribute to the accu-rate prediction of gene expression Moreover selectedfeatures of the same data type tend to cluster together onthe correlation matrix among the features suggestingthat the relationship within the same data type is closerthan the relationship between different data types Asexpected histone modification and CpG methylation fea-tures are the largest two groups among the four types ofdata signifying their importance to predict gene expres-sion Since nonlinear classification methods performslightly better than linear classification methods it sug-gests that interactions do exist between different types ofdata This is supported by numerous literatures that
enzymes responsible for CpG methylation also interactwith histone modification events [3233]Besides the value of predicting gene expression our
models also provide insights into the relative importanceof different epigenomicsgenome data as well as thegenomic locations We found that CpG methylation fea-tures have more predictive values for differential geneexpression compared to the three types of histone H3modification data Although other kinds of histone mod-ification data can also be obtained to increase the pre-dictive values of histone modification data it is muchmore costly to obtain them relative to the CpG methyla-tion data (the cost of CHIP-Seq on each of the histonemodification marker is similar to an entire CpG methy-lation array) Therefore practically speaking when thebudget is a constraining factor we suggest that assayson CpG methylation should be considered with priorityin predicting differential gene expression Moreover theresults of our models demonstrate that all genomic loca-tions relative to each transcript including promotersexons and gene bodies provide useful information topredict gene expression alternation Although the CpGmethylation signals from the promoters region are moreimportant the methylation signals from other regionssuch as exons introns and UTRs are indicative ofchanges in the gene expression as wellWorth noticing a lot of features that are extracted on
methylation and histone modification are naturallybased on the annotations from Illumina 450K array plat-form for DNA methylation There may be bias on thenumber of features that are hand coded in the modelTo address potential issue we changed TSS200_GC toTSS150_GC in our model and obtained an AUC = 861(compared to 0864) for cross fold validation on thetraining set and an AUC = 834 (compared to 0836) forthe testing set Therefore we think the bias due to rely-ing on the nomenclatures from Illuminarsquos annotation issmall
Limitations and future directionsWe should point out that our current model does notinclude all histone modification data but only threewidely used methylation markers on histone H3(H3K4Me3 H3K27Me3 and H3K36Me3) Moreover thehistone H3 data are drawn from ENCODE cell linessince the TCGA samples do not have such data Theheterogeneity of the sample resources could affect theaccuracy of the model When more histone marker datacoupled with DNA methylation and RNA-Seq databecome publicly available for lung cancer we caninclude them to achieve a better model In the ideal set-ting we would like to build a predictive model that hasmultiple types of epigenomics data obtained from thesame samples Another potential concern is overfitting
Li et al BMC Bioinformatics 2015 16(Suppl 5)S10httpwwwbiomedcentralcom1471-210516S5S10
Page 10 of 12
in the classification model However we split the datasetinto training and independent testing subsets and showthe model performs comparably well on the holdouttesting subset We believe that the model can be repli-cated if we can identify paired RNA-Seq and methyla-tion data In fact we had originally built this model ona private data set which also achieved an AUC of morethan 080 Additionally Figure 4 and 5 both indicatethat our approach does not suffer a significant over-fit-ting problem using the TCGA data and show the domi-nant efforts of histone modification and CpGmethylation which yield an updown gene expressionprediction with an AUCgt080 Currently the model useslung cancer data and it will be interesting find outmore general epigenetic predictors for differential geneexpression in other cancers as well Lastly we shouldpoint out that regulation of gene expression is complexincluding other mechanisms mediated by transcriptionfactors microRNA non-coding RNAs etc The fact thatAUCs hover between 080-090 ranges could be well dueto the fact that features from these other mechanismsare not considered in the current epigenetics model Toincrease the accuracy a more complex model that takesinto account of all these events should be constructed
ConclusionsA new model based on epigenomics data is proposed topredict transcriptome-level differential gene expressionin lung cancers Dropping-off feature sets by data typeshows that CpG methylation features are most impor-tant for the prediction Furthermore methylation fea-tures on all genomic regions relative to protein codinggenes contribute to the differential gene expressionwithin which promoter regions are most important
Additional material
Additional file 1 Table S1 Selected 67 features in the best modelsorted by category and their frequency
Competing interestsThe authors declare that they have no competing interests
Authorsrsquo contributionsLXG envisioned the project and supervised the work TC initiated theproject JL and TC designed and implemented the project SJ assisted theproject JL TC and LXG wrote the manuscript All authors have read revisedand approved the final manuscript
AcknowledgementsThe authors would like to thank Dr Jayson Masaki for reviewing themanuscript
DeclarationsPublication charges for this article were funded by NIHNIGMS P20 COBREGM103457 NIHNIEHS K01 ES025434-01 and Hawaii Community Foundation
This article has been published as part of BMC Bioinformatics Volume 16Supplement 5 2015 Selected articles from the 10th InternationalSymposium on Bioinformatics Research and Applications (ISBRA-14)Bioinformatics The full contents of the supplement are available online athttpwwwbiomedcentralcombmcbioinformaticssupplements16S5
Authorsrsquo details1Department of Biomedical Engineering Johns Hopkins University BaltimoreMD 21218 USA 2Molecular Biosciences and Bioengineering GraduateProgram University of Hawaii at Manoa Honolulu HI 96822 USA3Epidemiology Program University of Hawaii Cancer Center Honolulu HI96813 USA
Published 18 March 2015
References1 Portela A Esteller M Epigenetic modifications and human disease Nature
biotechnology 2010 28(10)1057-10682 Bock C Lengauer T Computational epigenetics Bioinformatics 2008
24(1)1-103 Laird PW Principles and challenges of genomewide DNA methylation
analysis Nature reviews Genetics 2010 11(3)191-2034 Lim SJ Tan TW Tong JC Computational Epigenetics the new scientific
paradigm Bioinformation 2010 4(7)331-3375 Gardiner-Garden M Frommer M CpG islands in vertebrate genomes
Journal of molecular biology 1987 196(2)261-2826 Daura-Oller E Cabre M Montero MA Paternain JL Romeu A Specific gene
hypomethylation and cancer New insights into coding region featuretrends Bioinformation 2009 3(8)340
7 Wild L Flanagan JM Genome-wide hypomethylation in cancer may be apassive consequence of transformation Biochimica et biophysica acta2010 1806(1)50-57
8 Figueroa ME Chen SC Andersson AK Phillips LA Li Y Sotzen J Kundu MDowning JR Melnick A Mullighan CG Integrated genetic and epigeneticanalysis of childhood acute lymphoblastic leukemia The Journal ofclinical investigation 2013 123(7)3099-3111
9 Rhee JK Kim K Chae H Evans J Yan P Zhang BT Gray J Spellman PHuang TH Nephew KP et al Integrated analysis of genome-wide DNAmethylation and gene expression profiles in molecular subtypes ofbreast cancer Nucleic acids research 2013 41(18)8464-8474
10 An integrated encyclopedia of DNA elements in the human genomeNature 2012 489(7414)57-74
11 Karolchik D Hinrichs AS Furey TS Roskin KM Sugnet CW Haussler DKent WJ The UCSC Table Browser data retrieval tool Nucleic acidsresearch 2004 32 Database D493-496
12 Langmead B Salzberg SL Fast gapped-read alignment with Bowtie 2Nature methods 2012 9(4)357-359
13 Li H Handsaker B Wysoker A Fennell T Ruan J Homer N Marth GAbecasis G Durbin R Genome Project Data Processing S The SequenceAlignmentMap format and SAMtools Bioinformatics 200925(16)2078-2079
14 Quinlan AR Hall IM BEDTools a flexible suite of utilities for comparinggenomic features Bioinformatics 2010 26(6)841-842
15 Siepel A Bejerano G Pedersen JS Hinrichs AS Hou M Rosenbloom KClawson H Spieth J Hillier LW Richards S Evolutionarily conservedelements in vertebrate insect worm and yeast genomes Genomeresearch 2005 15(8)1034-1050
16 Love MI Huber W Anders S Moderated estimation of fold change anddispersion for RNA-Seq data with DESeq2 bioRxiv 2014
17 Holm S A simple sequentially rejective multiple test procedureScandinavian journal of statistics 1979 65-70
18 Smyth GK Limma linear models for microarray data Bioinformatics andcomputational biology solutions using R and Bioconductor Springer 2005397-420
19 Quinlan AR Hall IM BEDTools a flexible suite of utilities for comparinggenomic features Bioinformatics 2010 26(6)841-842
20 Pages H Aboyoun P Gentleman R DebRoy S String objects representingbiological sequences and matching algorithms R package version 2009 2(2)
21 Hall MA Smith LA Feature Selection for Machine Learning Comparing aCorrelation-Based Filter Approach to the Wrapper FLAIRS Conference1999 1999 235-239
Li et al BMC Bioinformatics 2015 16(Suppl 5)S10httpwwwbiomedcentralcom1471-210516S5S10
Page 11 of 12
22 Quinlan JR Induction of decision trees Machine learning 1986 1(1)81-10623 Kononenko I Šimec E Robnik-Šikonja M Overcoming the Myopia of
Inductive Learning Algorithms with RELIEFF Applied Intelligence 19977(1)39-55
24 Kira K Rendell LA The feature selection problem Traditional methodsand a new algorithm AAAI 1992 1992 129-134
25 Hall M Frank E Holmes G Pfahringer B Reutemann P Witten IH TheWEKA data mining software an update ACM SIGKDD ExplorationsNewsletter 2009 11(1)10-18
26 Wei T Corrplot visualization of a correlation matrix R package version 02-0 Available on Comprehensive R Archive Network website 2010 [httpCRANR-project orgpackage=corrplot] (accessed 2010)
27 Sing T Sander O Beerenwinkel N Lengauer T ROCR visualizing classifierperformance in R Bioinformatics 2005 21(20)3940-3941
28 Fuks F Hurd PJ Wolf D Nan X Bird AP Kouzarides T The methyl-CpG-binding protein MeCP2 links DNA methylation to histone methylationJournal of Biological Chemistry 2003 278(6)4035-4040
29 Cedar H Bergman Y Linking DNA methylation and histone modificationpatterns and paradigms Nat Rev Genet 2009 10(5)295-304
30 Jones PA Functions of DNA methylation islands start sites gene bodiesand beyond Nat Rev Genet 2012 13(7)484-492
31 Maunakea AK Nagarajan RP Bilenky M Ballinger TJ DrsquoSouza C Fouse SDJohnson BE Hong C Nielsen C Zhao Y Conserved role of intragenic DNAmethylation in regulating alternative promoters Nature 2010466(7303)253-257
32 Bowen NJ Palmer MB Wade PA Chromosomal regulation by MeCP2structural and enzymatic considerations Cellular and molecular lifesciences CMLS 2004 61(17)2163-2167
33 Ooi SK Qiu C Bernstein E Li K Jia D Yang Z Erdjument-Bromage HTempst P Lin SP Allis CD et al DNMT3L connects unmethylated lysine 4of histone H3 to de novo methylation of DNA Nature 2007448(7154)714-717
doi1011861471-2105-16-S5-S10Cite this article as Li et al Using epigenomics data to predict geneexpression in lung cancer BMC Bioinformatics 2015 16(Suppl 5)S10
Submit your next manuscript to BioMed Centraland take full advantage of
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Li et al BMC Bioinformatics 2015 16(Suppl 5)S10httpwwwbiomedcentralcom1471-210516S5S10
Page 12 of 12
Abstract
Background
Methods
Results
Conclusions
Background
Methods
Data sets
Data processing
Methylation data
Histone data
Human genome data
RNA-seq data
Feature extraction
CpG Methylation features
Histone marker modification features
Nucleotide features
Conservation features
Feature selection
Model evaluation
Software
Results
Summary of input data and features
Model selection and evaluation
Analysis of selected features
Evaluation of features by data type
Evaluation of CpG methylation features by locations relative to transcripts
Discussion
The need to build predictive models of gene expression from epigenomics data
Selected features and their relevance to gene expression
Limitations and future directions
Conclusions
Competing interests
Authorsrsquo contributions
Acknowledgements
Declarations
Authorsrsquo details
References
model has the best predictive performance on the 20holdout data set with an AUC of 0836
Analysis of selected featuresA total of 67 features are selected by the best modelspanning all four types of genomic and epigenomic data
We first explored the relationship among the selectedfeatures Using hierarchical clustering on absolute corre-lation values between features (Figure 3A) we found thatthe selected features tend to cluster by the data type asexpected For example the conservation features in thecoding regions (CDS) are grouped together and so are
Figure 1 Segments associated with protein coding genes Features considered to predict differential gene expression are depicted on asegment-by-segment basis Segments are determined based on the annotations of Illumina Infinium Human Methylation 450K Beadchip Arraywith augmentations on segments located in gene bodies From 5rsquo to 3rsquo end of the protein coding genes listed are transcription starting sites(TSS) upstream up to 1500 bp (TSS 1500) and 200 bp (TSS 200) first exon which may include 5rsquo UTR first intron exon body last intron and lastexon which may include 3rsquo UTR A full transcript region is determined as the UTRs and coding region together
Figure 2 Performance comparison of models with various feature selection and classification methods The Areas Under the Curve (AUC)of ROC are used as the metric to compare the performance of models with different combinations of feature selection (CFS Gain Ratios andReliefF) and classification (Gaussian SVM Linear SVM Logistic regression Naiumlve Bayes and Random Forest) on the training data with 10 foldcross-validation The model with ReliefF based feature selection and Random Forest classification is selected as the best model
Li et al BMC Bioinformatics 2015 16(Suppl 5)S10httpwwwbiomedcentralcom1471-210516S5S10
Page 6 of 12
most methylation features As expected the CpG islandswithin the promoter are very important for the predictionof gene expression as demonstrated by the three selectedand highly correlated features CG composition featuresTSS200_GC TSS200_CG and TSS200_CGGThe largest group of selected features is the histone
modification features (32 features) followed by the methy-lation features (15 features) (Additional File 1) Theselected features underscore the importance of histoneepigenetic modification in the regulation of gene expres-sion Likewise the importance of methylation features isevident especially for the featured arising from TSS 5rsquoUTR and first exons Interestingly several methylation fea-tures (TSS1500_avgMval first_exon_avgMval andUTR5_avgMval) are clustered with histone modificationfeatures suggesting collinearity between these two types offeatures as shown by others [2829]On the other hand when features are categorized by
location relative to the transcripts (Additional File 1) theTSS200 has the most number of features (13 features)and TSS1500 has 6 features selected for this regionTogether the promoter comprises 28 of all the selectedfeatures This confirms the previously well-known impor-tance of the promoter region for the epigenetic regulationof gene expression [3031] Additionally CDS has the sec-ond highest number of features being selected highlight-ing its significance in regulating gene expression [30]
We also calculated the correlation of each feature togene expression and plotted the top 15 features mostrelevant to gene expression prediction (Figure 3B) Noneof the features have correlations higher than 045 sug-gesting that no single feature is a dominant predictorfor gene expression These features are either histonemodification (11 features) or methylation features (4 fea-tures) consistent with the previous observation on thesignificance of these two types of features
Evaluation of features by data typeTo determine the contribution of different types of fea-tures to gene expression we tested the performance ofmodels when a subset of features from the same datatype were dropped We present the results of four mea-sures of model performance AUC accuracy F-measureand Matthewrsquos correlation coefficient (MCC) (Figure 4)Dropping any individual feature set of nucleotide compo-sition histone modification or CpG methylation did notseem to have a large effect on the model performanceindicating that there is redundancy between feature setsThe sub-model performance for the dropping-off of asingle feature set from the full model is in the followingorder nucleotide composition removal gt histone modifi-cation removal gt CpG methylation removal Thus drop-ping methylation features had the largest effect amongindividual feature set as the AUC decreases from 0864
Figure 3 Top fifteen features from the best model (a) The clustering results on the absolute values of Pearsonrsquos correlation coefficients from67 selected features by the best model The names of different type of features are labeled by different colors Note the length of a segment islisted out separately (b) List of top fifteen features selected by ReliefF feature selection and sorted by their correlation to the classification ofdifferential gene expression
Li et al BMC Bioinformatics 2015 16(Suppl 5)S10httpwwwbiomedcentralcom1471-210516S5S10
Page 7 of 12
in the full model to 0832 in the training set as well asfrom 0836 to 0810 in the testing set Likewise MCCupon single feature set drop-off shows the largest pro-portional change among the four performance measuresand decreases from 056 to 049 on the training set and051 to 045 on the testing setWe also compared the effect of removing both nucleo-
tide and histone features on model performance as com-pared to removing either of them alone As expectedremoving both nucleotide and histone features gives thelowest AUCs lowest accuracies and lowest F-measures in
both training and testing sets However it leads to higherMCC than removing just histones does in the testing setThis suggests that there might be some overfitting withregards to the nucleotide feature set which accounts forthe majority (83) of features prior to feature selection
Evaluation of CpG methylation features by locationsrelative to transcriptsGiven that removing methylation features causes themost reduction of model performance among the singlefeature set drop-off (Figure 5) we next asked the
Figure 4 Evaluation of features generated from various data types (a-b) Effects of feature set drop-off on ROC curves from the 10-foldcross-validation training set (a) and testing set (b) (c) Effects of feature set drop-off on other four metrics AUC Accuracy F-measure and MCCin the training set and testing set
Li et al BMC Bioinformatics 2015 16(Suppl 5)S10httpwwwbiomedcentralcom1471-210516S5S10
Page 8 of 12
question of the relative importance of each methylationfeature categorized by genomic location We performeddrop-off tests by sequentially removing features in eachgenomic location category We first removed the featuresfrom the first exons and first introns (as they are close to
the TSS) then from gene bodies including exons intronsand UTRs and lastly from TSS1500 region such that onlyTSS200 features were kept At each step we re-performedfeature selection and model construction using theremaining methylation features
Figure 5 Evaluation of methylation features by segment (a-b) Effects of segment-based methylation feature set sequential drop-off on ROCcurves from the 10-fold cross-validation training set (a) and testing set (b) (c) Effects of segment-based methylation feature set sequential drop-off on other four metrics AUC Accuracy F-measure and MCC in the training set and testing set
Li et al BMC Bioinformatics 2015 16(Suppl 5)S10httpwwwbiomedcentralcom1471-210516S5S10
Page 9 of 12
All categories of genomic locations provide relevantuseful information that contributes to better predictionof gene expression as each of the sequential feature setdrop-off decreased the performance of the model in bothtraining and testing sets Compared to the removal offirst exon and intron regions and removal of the UTRsand the rest of the gene body removal of TSS1500 leadsto the largest reduction in all four metrics confirmingthe importance of the promoter region in regulating geneexpression Even when only TSS200 features are consid-ered an AUC of 0638 and 0636 are obtained in the test-ing and training sets respectively suggesting that CpGmethylation status in TSS200 is still somewhat predictiveof gene expression However a more accurate predictionusing methylation features should arise from all locationsassociated with the transcripts
DiscussionThe need to build predictive models of gene expressionfrom epigenomics dataAlthough currently integrative analyses between geneexpression and epigenetic modification exist we havefound that quantitative models using epigenetic informa-tion to accurately predict the up or down regulation ofgene expression are currently lacking There are oftencases where researchers can only obtain reliable epige-netics data but not gene expression data For example ifthe samples are archived and processed by FFPE (Forma-lin-fixed paraffin-embedded) one can still perform epige-nomics measurements but not the gene expressionexperiments due to the degradation of mRNA in the sam-ples More importantly a predictive method such as ourscan efficiently facilitate the bench scientists to narrowdown the candidate lists and conduct gene expression vali-dation especially when the epigenetics information is theonly data handy
Selected features and their relevance to gene expressionAll four types of data (CpG methylation histone H3modification nucleotide sequence and conservation)exist in the 67 features that are selected by the bestmodel indicating that all of them contribute to the accu-rate prediction of gene expression Moreover selectedfeatures of the same data type tend to cluster together onthe correlation matrix among the features suggestingthat the relationship within the same data type is closerthan the relationship between different data types Asexpected histone modification and CpG methylation fea-tures are the largest two groups among the four types ofdata signifying their importance to predict gene expres-sion Since nonlinear classification methods performslightly better than linear classification methods it sug-gests that interactions do exist between different types ofdata This is supported by numerous literatures that
enzymes responsible for CpG methylation also interactwith histone modification events [3233]Besides the value of predicting gene expression our
models also provide insights into the relative importanceof different epigenomicsgenome data as well as thegenomic locations We found that CpG methylation fea-tures have more predictive values for differential geneexpression compared to the three types of histone H3modification data Although other kinds of histone mod-ification data can also be obtained to increase the pre-dictive values of histone modification data it is muchmore costly to obtain them relative to the CpG methyla-tion data (the cost of CHIP-Seq on each of the histonemodification marker is similar to an entire CpG methy-lation array) Therefore practically speaking when thebudget is a constraining factor we suggest that assayson CpG methylation should be considered with priorityin predicting differential gene expression Moreover theresults of our models demonstrate that all genomic loca-tions relative to each transcript including promotersexons and gene bodies provide useful information topredict gene expression alternation Although the CpGmethylation signals from the promoters region are moreimportant the methylation signals from other regionssuch as exons introns and UTRs are indicative ofchanges in the gene expression as wellWorth noticing a lot of features that are extracted on
methylation and histone modification are naturallybased on the annotations from Illumina 450K array plat-form for DNA methylation There may be bias on thenumber of features that are hand coded in the modelTo address potential issue we changed TSS200_GC toTSS150_GC in our model and obtained an AUC = 861(compared to 0864) for cross fold validation on thetraining set and an AUC = 834 (compared to 0836) forthe testing set Therefore we think the bias due to rely-ing on the nomenclatures from Illuminarsquos annotation issmall
Limitations and future directionsWe should point out that our current model does notinclude all histone modification data but only threewidely used methylation markers on histone H3(H3K4Me3 H3K27Me3 and H3K36Me3) Moreover thehistone H3 data are drawn from ENCODE cell linessince the TCGA samples do not have such data Theheterogeneity of the sample resources could affect theaccuracy of the model When more histone marker datacoupled with DNA methylation and RNA-Seq databecome publicly available for lung cancer we caninclude them to achieve a better model In the ideal set-ting we would like to build a predictive model that hasmultiple types of epigenomics data obtained from thesame samples Another potential concern is overfitting
Li et al BMC Bioinformatics 2015 16(Suppl 5)S10httpwwwbiomedcentralcom1471-210516S5S10
Page 10 of 12
in the classification model However we split the datasetinto training and independent testing subsets and showthe model performs comparably well on the holdouttesting subset We believe that the model can be repli-cated if we can identify paired RNA-Seq and methyla-tion data In fact we had originally built this model ona private data set which also achieved an AUC of morethan 080 Additionally Figure 4 and 5 both indicatethat our approach does not suffer a significant over-fit-ting problem using the TCGA data and show the domi-nant efforts of histone modification and CpGmethylation which yield an updown gene expressionprediction with an AUCgt080 Currently the model useslung cancer data and it will be interesting find outmore general epigenetic predictors for differential geneexpression in other cancers as well Lastly we shouldpoint out that regulation of gene expression is complexincluding other mechanisms mediated by transcriptionfactors microRNA non-coding RNAs etc The fact thatAUCs hover between 080-090 ranges could be well dueto the fact that features from these other mechanismsare not considered in the current epigenetics model Toincrease the accuracy a more complex model that takesinto account of all these events should be constructed
ConclusionsA new model based on epigenomics data is proposed topredict transcriptome-level differential gene expressionin lung cancers Dropping-off feature sets by data typeshows that CpG methylation features are most impor-tant for the prediction Furthermore methylation fea-tures on all genomic regions relative to protein codinggenes contribute to the differential gene expressionwithin which promoter regions are most important
Additional material
Additional file 1 Table S1 Selected 67 features in the best modelsorted by category and their frequency
Competing interestsThe authors declare that they have no competing interests
Authorsrsquo contributionsLXG envisioned the project and supervised the work TC initiated theproject JL and TC designed and implemented the project SJ assisted theproject JL TC and LXG wrote the manuscript All authors have read revisedand approved the final manuscript
AcknowledgementsThe authors would like to thank Dr Jayson Masaki for reviewing themanuscript
DeclarationsPublication charges for this article were funded by NIHNIGMS P20 COBREGM103457 NIHNIEHS K01 ES025434-01 and Hawaii Community Foundation
This article has been published as part of BMC Bioinformatics Volume 16Supplement 5 2015 Selected articles from the 10th InternationalSymposium on Bioinformatics Research and Applications (ISBRA-14)Bioinformatics The full contents of the supplement are available online athttpwwwbiomedcentralcombmcbioinformaticssupplements16S5
Authorsrsquo details1Department of Biomedical Engineering Johns Hopkins University BaltimoreMD 21218 USA 2Molecular Biosciences and Bioengineering GraduateProgram University of Hawaii at Manoa Honolulu HI 96822 USA3Epidemiology Program University of Hawaii Cancer Center Honolulu HI96813 USA
Published 18 March 2015
References1 Portela A Esteller M Epigenetic modifications and human disease Nature
biotechnology 2010 28(10)1057-10682 Bock C Lengauer T Computational epigenetics Bioinformatics 2008
24(1)1-103 Laird PW Principles and challenges of genomewide DNA methylation
analysis Nature reviews Genetics 2010 11(3)191-2034 Lim SJ Tan TW Tong JC Computational Epigenetics the new scientific
paradigm Bioinformation 2010 4(7)331-3375 Gardiner-Garden M Frommer M CpG islands in vertebrate genomes
Journal of molecular biology 1987 196(2)261-2826 Daura-Oller E Cabre M Montero MA Paternain JL Romeu A Specific gene
hypomethylation and cancer New insights into coding region featuretrends Bioinformation 2009 3(8)340
7 Wild L Flanagan JM Genome-wide hypomethylation in cancer may be apassive consequence of transformation Biochimica et biophysica acta2010 1806(1)50-57
8 Figueroa ME Chen SC Andersson AK Phillips LA Li Y Sotzen J Kundu MDowning JR Melnick A Mullighan CG Integrated genetic and epigeneticanalysis of childhood acute lymphoblastic leukemia The Journal ofclinical investigation 2013 123(7)3099-3111
9 Rhee JK Kim K Chae H Evans J Yan P Zhang BT Gray J Spellman PHuang TH Nephew KP et al Integrated analysis of genome-wide DNAmethylation and gene expression profiles in molecular subtypes ofbreast cancer Nucleic acids research 2013 41(18)8464-8474
10 An integrated encyclopedia of DNA elements in the human genomeNature 2012 489(7414)57-74
11 Karolchik D Hinrichs AS Furey TS Roskin KM Sugnet CW Haussler DKent WJ The UCSC Table Browser data retrieval tool Nucleic acidsresearch 2004 32 Database D493-496
12 Langmead B Salzberg SL Fast gapped-read alignment with Bowtie 2Nature methods 2012 9(4)357-359
13 Li H Handsaker B Wysoker A Fennell T Ruan J Homer N Marth GAbecasis G Durbin R Genome Project Data Processing S The SequenceAlignmentMap format and SAMtools Bioinformatics 200925(16)2078-2079
14 Quinlan AR Hall IM BEDTools a flexible suite of utilities for comparinggenomic features Bioinformatics 2010 26(6)841-842
15 Siepel A Bejerano G Pedersen JS Hinrichs AS Hou M Rosenbloom KClawson H Spieth J Hillier LW Richards S Evolutionarily conservedelements in vertebrate insect worm and yeast genomes Genomeresearch 2005 15(8)1034-1050
16 Love MI Huber W Anders S Moderated estimation of fold change anddispersion for RNA-Seq data with DESeq2 bioRxiv 2014
17 Holm S A simple sequentially rejective multiple test procedureScandinavian journal of statistics 1979 65-70
18 Smyth GK Limma linear models for microarray data Bioinformatics andcomputational biology solutions using R and Bioconductor Springer 2005397-420
19 Quinlan AR Hall IM BEDTools a flexible suite of utilities for comparinggenomic features Bioinformatics 2010 26(6)841-842
20 Pages H Aboyoun P Gentleman R DebRoy S String objects representingbiological sequences and matching algorithms R package version 2009 2(2)
21 Hall MA Smith LA Feature Selection for Machine Learning Comparing aCorrelation-Based Filter Approach to the Wrapper FLAIRS Conference1999 1999 235-239
Li et al BMC Bioinformatics 2015 16(Suppl 5)S10httpwwwbiomedcentralcom1471-210516S5S10
Page 11 of 12
22 Quinlan JR Induction of decision trees Machine learning 1986 1(1)81-10623 Kononenko I Šimec E Robnik-Šikonja M Overcoming the Myopia of
Inductive Learning Algorithms with RELIEFF Applied Intelligence 19977(1)39-55
24 Kira K Rendell LA The feature selection problem Traditional methodsand a new algorithm AAAI 1992 1992 129-134
25 Hall M Frank E Holmes G Pfahringer B Reutemann P Witten IH TheWEKA data mining software an update ACM SIGKDD ExplorationsNewsletter 2009 11(1)10-18
26 Wei T Corrplot visualization of a correlation matrix R package version 02-0 Available on Comprehensive R Archive Network website 2010 [httpCRANR-project orgpackage=corrplot] (accessed 2010)
27 Sing T Sander O Beerenwinkel N Lengauer T ROCR visualizing classifierperformance in R Bioinformatics 2005 21(20)3940-3941
28 Fuks F Hurd PJ Wolf D Nan X Bird AP Kouzarides T The methyl-CpG-binding protein MeCP2 links DNA methylation to histone methylationJournal of Biological Chemistry 2003 278(6)4035-4040
29 Cedar H Bergman Y Linking DNA methylation and histone modificationpatterns and paradigms Nat Rev Genet 2009 10(5)295-304
30 Jones PA Functions of DNA methylation islands start sites gene bodiesand beyond Nat Rev Genet 2012 13(7)484-492
31 Maunakea AK Nagarajan RP Bilenky M Ballinger TJ DrsquoSouza C Fouse SDJohnson BE Hong C Nielsen C Zhao Y Conserved role of intragenic DNAmethylation in regulating alternative promoters Nature 2010466(7303)253-257
32 Bowen NJ Palmer MB Wade PA Chromosomal regulation by MeCP2structural and enzymatic considerations Cellular and molecular lifesciences CMLS 2004 61(17)2163-2167
33 Ooi SK Qiu C Bernstein E Li K Jia D Yang Z Erdjument-Bromage HTempst P Lin SP Allis CD et al DNMT3L connects unmethylated lysine 4of histone H3 to de novo methylation of DNA Nature 2007448(7154)714-717
doi1011861471-2105-16-S5-S10Cite this article as Li et al Using epigenomics data to predict geneexpression in lung cancer BMC Bioinformatics 2015 16(Suppl 5)S10
Submit your next manuscript to BioMed Centraland take full advantage of
bull Convenient online submission
bull Thorough peer review
bull No space constraints or color figure charges
bull Immediate publication on acceptance
bull Inclusion in PubMed CAS Scopus and Google Scholar
bull Research which is freely available for redistribution
Submit your manuscript at wwwbiomedcentralcomsubmit
Li et al BMC Bioinformatics 2015 16(Suppl 5)S10httpwwwbiomedcentralcom1471-210516S5S10
Page 12 of 12
Abstract
Background
Methods
Results
Conclusions
Background
Methods
Data sets
Data processing
Methylation data
Histone data
Human genome data
RNA-seq data
Feature extraction
CpG Methylation features
Histone marker modification features
Nucleotide features
Conservation features
Feature selection
Model evaluation
Software
Results
Summary of input data and features
Model selection and evaluation
Analysis of selected features
Evaluation of features by data type
Evaluation of CpG methylation features by locations relative to transcripts
Discussion
The need to build predictive models of gene expression from epigenomics data
Selected features and their relevance to gene expression
Limitations and future directions
Conclusions
Competing interests
Authorsrsquo contributions
Acknowledgements
Declarations
Authorsrsquo details
References
most methylation features As expected the CpG islandswithin the promoter are very important for the predictionof gene expression as demonstrated by the three selectedand highly correlated features CG composition featuresTSS200_GC TSS200_CG and TSS200_CGGThe largest group of selected features is the histone
modification features (32 features) followed by the methy-lation features (15 features) (Additional File 1) Theselected features underscore the importance of histoneepigenetic modification in the regulation of gene expres-sion Likewise the importance of methylation features isevident especially for the featured arising from TSS 5rsquoUTR and first exons Interestingly several methylation fea-tures (TSS1500_avgMval first_exon_avgMval andUTR5_avgMval) are clustered with histone modificationfeatures suggesting collinearity between these two types offeatures as shown by others [2829]On the other hand when features are categorized by
location relative to the transcripts (Additional File 1) theTSS200 has the most number of features (13 features)and TSS1500 has 6 features selected for this regionTogether the promoter comprises 28 of all the selectedfeatures This confirms the previously well-known impor-tance of the promoter region for the epigenetic regulationof gene expression [3031] Additionally CDS has the sec-ond highest number of features being selected highlight-ing its significance in regulating gene expression [30]
We also calculated the correlation of each feature togene expression and plotted the top 15 features mostrelevant to gene expression prediction (Figure 3B) Noneof the features have correlations higher than 045 sug-gesting that no single feature is a dominant predictorfor gene expression These features are either histonemodification (11 features) or methylation features (4 fea-tures) consistent with the previous observation on thesignificance of these two types of features
Evaluation of features by data typeTo determine the contribution of different types of fea-tures to gene expression we tested the performance ofmodels when a subset of features from the same datatype were dropped We present the results of four mea-sures of model performance AUC accuracy F-measureand Matthewrsquos correlation coefficient (MCC) (Figure 4)Dropping any individual feature set of nucleotide compo-sition histone modification or CpG methylation did notseem to have a large effect on the model performanceindicating that there is redundancy between feature setsThe sub-model performance for the dropping-off of asingle feature set from the full model is in the followingorder nucleotide composition removal gt histone modifi-cation removal gt CpG methylation removal Thus drop-ping methylation features had the largest effect amongindividual feature set as the AUC decreases from 0864
Figure 3 Top fifteen features from the best model (a) The clustering results on the absolute values of Pearsonrsquos correlation coefficients from67 selected features by the best model The names of different type of features are labeled by different colors Note the length of a segment islisted out separately (b) List of top fifteen features selected by ReliefF feature selection and sorted by their correlation to the classification ofdifferential gene expression
Li et al BMC Bioinformatics 2015 16(Suppl 5)S10httpwwwbiomedcentralcom1471-210516S5S10
Page 7 of 12
in the full model to 0832 in the training set as well asfrom 0836 to 0810 in the testing set Likewise MCCupon single feature set drop-off shows the largest pro-portional change among the four performance measuresand decreases from 056 to 049 on the training set and051 to 045 on the testing setWe also compared the effect of removing both nucleo-
tide and histone features on model performance as com-pared to removing either of them alone As expectedremoving both nucleotide and histone features gives thelowest AUCs lowest accuracies and lowest F-measures in
both training and testing sets However it leads to higherMCC than removing just histones does in the testing setThis suggests that there might be some overfitting withregards to the nucleotide feature set which accounts forthe majority (83) of features prior to feature selection
Evaluation of CpG methylation features by locationsrelative to transcriptsGiven that removing methylation features causes themost reduction of model performance among the singlefeature set drop-off (Figure 5) we next asked the
Figure 4 Evaluation of features generated from various data types (a-b) Effects of feature set drop-off on ROC curves from the 10-foldcross-validation training set (a) and testing set (b) (c) Effects of feature set drop-off on other four metrics AUC Accuracy F-measure and MCCin the training set and testing set
Li et al BMC Bioinformatics 2015 16(Suppl 5)S10httpwwwbiomedcentralcom1471-210516S5S10
Page 8 of 12
question of the relative importance of each methylationfeature categorized by genomic location We performeddrop-off tests by sequentially removing features in eachgenomic location category We first removed the featuresfrom the first exons and first introns (as they are close to
the TSS) then from gene bodies including exons intronsand UTRs and lastly from TSS1500 region such that onlyTSS200 features were kept At each step we re-performedfeature selection and model construction using theremaining methylation features
Figure 5 Evaluation of methylation features by segment (a-b) Effects of segment-based methylation feature set sequential drop-off on ROCcurves from the 10-fold cross-validation training set (a) and testing set (b) (c) Effects of segment-based methylation feature set sequential drop-off on other four metrics AUC Accuracy F-measure and MCC in the training set and testing set
Li et al BMC Bioinformatics 2015 16(Suppl 5)S10httpwwwbiomedcentralcom1471-210516S5S10
Page 9 of 12
All categories of genomic locations provide relevantuseful information that contributes to better predictionof gene expression as each of the sequential feature setdrop-off decreased the performance of the model in bothtraining and testing sets Compared to the removal offirst exon and intron regions and removal of the UTRsand the rest of the gene body removal of TSS1500 leadsto the largest reduction in all four metrics confirmingthe importance of the promoter region in regulating geneexpression Even when only TSS200 features are consid-ered an AUC of 0638 and 0636 are obtained in the test-ing and training sets respectively suggesting that CpGmethylation status in TSS200 is still somewhat predictiveof gene expression However a more accurate predictionusing methylation features should arise from all locationsassociated with the transcripts
DiscussionThe need to build predictive models of gene expressionfrom epigenomics dataAlthough currently integrative analyses between geneexpression and epigenetic modification exist we havefound that quantitative models using epigenetic informa-tion to accurately predict the up or down regulation ofgene expression are currently lacking There are oftencases where researchers can only obtain reliable epige-netics data but not gene expression data For example ifthe samples are archived and processed by FFPE (Forma-lin-fixed paraffin-embedded) one can still perform epige-nomics measurements but not the gene expressionexperiments due to the degradation of mRNA in the sam-ples More importantly a predictive method such as ourscan efficiently facilitate the bench scientists to narrowdown the candidate lists and conduct gene expression vali-dation especially when the epigenetics information is theonly data handy
Selected features and their relevance to gene expressionAll four types of data (CpG methylation histone H3modification nucleotide sequence and conservation)exist in the 67 features that are selected by the bestmodel indicating that all of them contribute to the accu-rate prediction of gene expression Moreover selectedfeatures of the same data type tend to cluster together onthe correlation matrix among the features suggestingthat the relationship within the same data type is closerthan the relationship between different data types Asexpected histone modification and CpG methylation fea-tures are the largest two groups among the four types ofdata signifying their importance to predict gene expres-sion Since nonlinear classification methods performslightly better than linear classification methods it sug-gests that interactions do exist between different types ofdata This is supported by numerous literatures that
enzymes responsible for CpG methylation also interactwith histone modification events [3233]Besides the value of predicting gene expression our
models also provide insights into the relative importanceof different epigenomicsgenome data as well as thegenomic locations We found that CpG methylation fea-tures have more predictive values for differential geneexpression compared to the three types of histone H3modification data Although other kinds of histone mod-ification data can also be obtained to increase the pre-dictive values of histone modification data it is muchmore costly to obtain them relative to the CpG methyla-tion data (the cost of CHIP-Seq on each of the histonemodification marker is similar to an entire CpG methy-lation array) Therefore practically speaking when thebudget is a constraining factor we suggest that assayson CpG methylation should be considered with priorityin predicting differential gene expression Moreover theresults of our models demonstrate that all genomic loca-tions relative to each transcript including promotersexons and gene bodies provide useful information topredict gene expression alternation Although the CpGmethylation signals from the promoters region are moreimportant the methylation signals from other regionssuch as exons introns and UTRs are indicative ofchanges in the gene expression as wellWorth noticing a lot of features that are extracted on
methylation and histone modification are naturallybased on the annotations from Illumina 450K array plat-form for DNA methylation There may be bias on thenumber of features that are hand coded in the modelTo address potential issue we changed TSS200_GC toTSS150_GC in our model and obtained an AUC = 861(compared to 0864) for cross fold validation on thetraining set and an AUC = 834 (compared to 0836) forthe testing set Therefore we think the bias due to rely-ing on the nomenclatures from Illuminarsquos annotation issmall
Limitations and future directionsWe should point out that our current model does notinclude all histone modification data but only threewidely used methylation markers on histone H3(H3K4Me3 H3K27Me3 and H3K36Me3) Moreover thehistone H3 data are drawn from ENCODE cell linessince the TCGA samples do not have such data Theheterogeneity of the sample resources could affect theaccuracy of the model When more histone marker datacoupled with DNA methylation and RNA-Seq databecome publicly available for lung cancer we caninclude them to achieve a better model In the ideal set-ting we would like to build a predictive model that hasmultiple types of epigenomics data obtained from thesame samples Another potential concern is overfitting
Li et al BMC Bioinformatics 2015 16(Suppl 5)S10httpwwwbiomedcentralcom1471-210516S5S10
Page 10 of 12
in the classification model However we split the datasetinto training and independent testing subsets and showthe model performs comparably well on the holdouttesting subset We believe that the model can be repli-cated if we can identify paired RNA-Seq and methyla-tion data In fact we had originally built this model ona private data set which also achieved an AUC of morethan 080 Additionally Figure 4 and 5 both indicatethat our approach does not suffer a significant over-fit-ting problem using the TCGA data and show the domi-nant efforts of histone modification and CpGmethylation which yield an updown gene expressionprediction with an AUCgt080 Currently the model useslung cancer data and it will be interesting find outmore general epigenetic predictors for differential geneexpression in other cancers as well Lastly we shouldpoint out that regulation of gene expression is complexincluding other mechanisms mediated by transcriptionfactors microRNA non-coding RNAs etc The fact thatAUCs hover between 080-090 ranges could be well dueto the fact that features from these other mechanismsare not considered in the current epigenetics model Toincrease the accuracy a more complex model that takesinto account of all these events should be constructed
ConclusionsA new model based on epigenomics data is proposed topredict transcriptome-level differential gene expressionin lung cancers Dropping-off feature sets by data typeshows that CpG methylation features are most impor-tant for the prediction Furthermore methylation fea-tures on all genomic regions relative to protein codinggenes contribute to the differential gene expressionwithin which promoter regions are most important
Additional material
Additional file 1 Table S1 Selected 67 features in the best modelsorted by category and their frequency
Competing interestsThe authors declare that they have no competing interests
Authorsrsquo contributionsLXG envisioned the project and supervised the work TC initiated theproject JL and TC designed and implemented the project SJ assisted theproject JL TC and LXG wrote the manuscript All authors have read revisedand approved the final manuscript
AcknowledgementsThe authors would like to thank Dr Jayson Masaki for reviewing themanuscript
DeclarationsPublication charges for this article were funded by NIHNIGMS P20 COBREGM103457 NIHNIEHS K01 ES025434-01 and Hawaii Community Foundation
This article has been published as part of BMC Bioinformatics Volume 16Supplement 5 2015 Selected articles from the 10th InternationalSymposium on Bioinformatics Research and Applications (ISBRA-14)Bioinformatics The full contents of the supplement are available online athttpwwwbiomedcentralcombmcbioinformaticssupplements16S5
Authorsrsquo details1Department of Biomedical Engineering Johns Hopkins University BaltimoreMD 21218 USA 2Molecular Biosciences and Bioengineering GraduateProgram University of Hawaii at Manoa Honolulu HI 96822 USA3Epidemiology Program University of Hawaii Cancer Center Honolulu HI96813 USA
Published 18 March 2015
References1 Portela A Esteller M Epigenetic modifications and human disease Nature
biotechnology 2010 28(10)1057-10682 Bock C Lengauer T Computational epigenetics Bioinformatics 2008
24(1)1-103 Laird PW Principles and challenges of genomewide DNA methylation
analysis Nature reviews Genetics 2010 11(3)191-2034 Lim SJ Tan TW Tong JC Computational Epigenetics the new scientific
paradigm Bioinformation 2010 4(7)331-3375 Gardiner-Garden M Frommer M CpG islands in vertebrate genomes
Journal of molecular biology 1987 196(2)261-2826 Daura-Oller E Cabre M Montero MA Paternain JL Romeu A Specific gene
hypomethylation and cancer New insights into coding region featuretrends Bioinformation 2009 3(8)340
7 Wild L Flanagan JM Genome-wide hypomethylation in cancer may be apassive consequence of transformation Biochimica et biophysica acta2010 1806(1)50-57
8 Figueroa ME Chen SC Andersson AK Phillips LA Li Y Sotzen J Kundu MDowning JR Melnick A Mullighan CG Integrated genetic and epigeneticanalysis of childhood acute lymphoblastic leukemia The Journal ofclinical investigation 2013 123(7)3099-3111
9 Rhee JK Kim K Chae H Evans J Yan P Zhang BT Gray J Spellman PHuang TH Nephew KP et al Integrated analysis of genome-wide DNAmethylation and gene expression profiles in molecular subtypes ofbreast cancer Nucleic acids research 2013 41(18)8464-8474
10 An integrated encyclopedia of DNA elements in the human genomeNature 2012 489(7414)57-74
11 Karolchik D Hinrichs AS Furey TS Roskin KM Sugnet CW Haussler DKent WJ The UCSC Table Browser data retrieval tool Nucleic acidsresearch 2004 32 Database D493-496
12 Langmead B Salzberg SL Fast gapped-read alignment with Bowtie 2Nature methods 2012 9(4)357-359
13 Li H Handsaker B Wysoker A Fennell T Ruan J Homer N Marth GAbecasis G Durbin R Genome Project Data Processing S The SequenceAlignmentMap format and SAMtools Bioinformatics 200925(16)2078-2079
14 Quinlan AR Hall IM BEDTools a flexible suite of utilities for comparinggenomic features Bioinformatics 2010 26(6)841-842
15 Siepel A Bejerano G Pedersen JS Hinrichs AS Hou M Rosenbloom KClawson H Spieth J Hillier LW Richards S Evolutionarily conservedelements in vertebrate insect worm and yeast genomes Genomeresearch 2005 15(8)1034-1050
16 Love MI Huber W Anders S Moderated estimation of fold change anddispersion for RNA-Seq data with DESeq2 bioRxiv 2014
17 Holm S A simple sequentially rejective multiple test procedureScandinavian journal of statistics 1979 65-70
18 Smyth GK Limma linear models for microarray data Bioinformatics andcomputational biology solutions using R and Bioconductor Springer 2005397-420
19 Quinlan AR Hall IM BEDTools a flexible suite of utilities for comparinggenomic features Bioinformatics 2010 26(6)841-842
20 Pages H Aboyoun P Gentleman R DebRoy S String objects representingbiological sequences and matching algorithms R package version 2009 2(2)
21 Hall MA Smith LA Feature Selection for Machine Learning Comparing aCorrelation-Based Filter Approach to the Wrapper FLAIRS Conference1999 1999 235-239
Li et al BMC Bioinformatics 2015 16(Suppl 5)S10httpwwwbiomedcentralcom1471-210516S5S10
Page 11 of 12
22 Quinlan JR Induction of decision trees Machine learning 1986 1(1)81-10623 Kononenko I Šimec E Robnik-Šikonja M Overcoming the Myopia of
Inductive Learning Algorithms with RELIEFF Applied Intelligence 19977(1)39-55
24 Kira K Rendell LA The feature selection problem Traditional methodsand a new algorithm AAAI 1992 1992 129-134
25 Hall M Frank E Holmes G Pfahringer B Reutemann P Witten IH TheWEKA data mining software an update ACM SIGKDD ExplorationsNewsletter 2009 11(1)10-18
26 Wei T Corrplot visualization of a correlation matrix R package version 02-0 Available on Comprehensive R Archive Network website 2010 [httpCRANR-project orgpackage=corrplot] (accessed 2010)
27 Sing T Sander O Beerenwinkel N Lengauer T ROCR visualizing classifierperformance in R Bioinformatics 2005 21(20)3940-3941
28 Fuks F Hurd PJ Wolf D Nan X Bird AP Kouzarides T The methyl-CpG-binding protein MeCP2 links DNA methylation to histone methylationJournal of Biological Chemistry 2003 278(6)4035-4040
29 Cedar H Bergman Y Linking DNA methylation and histone modificationpatterns and paradigms Nat Rev Genet 2009 10(5)295-304
30 Jones PA Functions of DNA methylation islands start sites gene bodiesand beyond Nat Rev Genet 2012 13(7)484-492
31 Maunakea AK Nagarajan RP Bilenky M Ballinger TJ DrsquoSouza C Fouse SDJohnson BE Hong C Nielsen C Zhao Y Conserved role of intragenic DNAmethylation in regulating alternative promoters Nature 2010466(7303)253-257
32 Bowen NJ Palmer MB Wade PA Chromosomal regulation by MeCP2structural and enzymatic considerations Cellular and molecular lifesciences CMLS 2004 61(17)2163-2167
33 Ooi SK Qiu C Bernstein E Li K Jia D Yang Z Erdjument-Bromage HTempst P Lin SP Allis CD et al DNMT3L connects unmethylated lysine 4of histone H3 to de novo methylation of DNA Nature 2007448(7154)714-717
doi1011861471-2105-16-S5-S10Cite this article as Li et al Using epigenomics data to predict geneexpression in lung cancer BMC Bioinformatics 2015 16(Suppl 5)S10
Submit your next manuscript to BioMed Centraland take full advantage of
bull Convenient online submission
bull Thorough peer review
bull No space constraints or color figure charges
bull Immediate publication on acceptance
bull Inclusion in PubMed CAS Scopus and Google Scholar
bull Research which is freely available for redistribution
Submit your manuscript at wwwbiomedcentralcomsubmit
Li et al BMC Bioinformatics 2015 16(Suppl 5)S10httpwwwbiomedcentralcom1471-210516S5S10
Page 12 of 12
Abstract
Background
Methods
Results
Conclusions
Background
Methods
Data sets
Data processing
Methylation data
Histone data
Human genome data
RNA-seq data
Feature extraction
CpG Methylation features
Histone marker modification features
Nucleotide features
Conservation features
Feature selection
Model evaluation
Software
Results
Summary of input data and features
Model selection and evaluation
Analysis of selected features
Evaluation of features by data type
Evaluation of CpG methylation features by locations relative to transcripts
Discussion
The need to build predictive models of gene expression from epigenomics data
Selected features and their relevance to gene expression
Limitations and future directions
Conclusions
Competing interests
Authorsrsquo contributions
Acknowledgements
Declarations
Authorsrsquo details
References
in the full model to 0832 in the training set as well asfrom 0836 to 0810 in the testing set Likewise MCCupon single feature set drop-off shows the largest pro-portional change among the four performance measuresand decreases from 056 to 049 on the training set and051 to 045 on the testing setWe also compared the effect of removing both nucleo-
tide and histone features on model performance as com-pared to removing either of them alone As expectedremoving both nucleotide and histone features gives thelowest AUCs lowest accuracies and lowest F-measures in
both training and testing sets However it leads to higherMCC than removing just histones does in the testing setThis suggests that there might be some overfitting withregards to the nucleotide feature set which accounts forthe majority (83) of features prior to feature selection
Evaluation of CpG methylation features by locationsrelative to transcriptsGiven that removing methylation features causes themost reduction of model performance among the singlefeature set drop-off (Figure 5) we next asked the
Figure 4 Evaluation of features generated from various data types (a-b) Effects of feature set drop-off on ROC curves from the 10-foldcross-validation training set (a) and testing set (b) (c) Effects of feature set drop-off on other four metrics AUC Accuracy F-measure and MCCin the training set and testing set
Li et al BMC Bioinformatics 2015 16(Suppl 5)S10httpwwwbiomedcentralcom1471-210516S5S10
Page 8 of 12
question of the relative importance of each methylationfeature categorized by genomic location We performeddrop-off tests by sequentially removing features in eachgenomic location category We first removed the featuresfrom the first exons and first introns (as they are close to
the TSS) then from gene bodies including exons intronsand UTRs and lastly from TSS1500 region such that onlyTSS200 features were kept At each step we re-performedfeature selection and model construction using theremaining methylation features
Figure 5 Evaluation of methylation features by segment (a-b) Effects of segment-based methylation feature set sequential drop-off on ROCcurves from the 10-fold cross-validation training set (a) and testing set (b) (c) Effects of segment-based methylation feature set sequential drop-off on other four metrics AUC Accuracy F-measure and MCC in the training set and testing set
Li et al BMC Bioinformatics 2015 16(Suppl 5)S10httpwwwbiomedcentralcom1471-210516S5S10
Page 9 of 12
All categories of genomic locations provide relevantuseful information that contributes to better predictionof gene expression as each of the sequential feature setdrop-off decreased the performance of the model in bothtraining and testing sets Compared to the removal offirst exon and intron regions and removal of the UTRsand the rest of the gene body removal of TSS1500 leadsto the largest reduction in all four metrics confirmingthe importance of the promoter region in regulating geneexpression Even when only TSS200 features are consid-ered an AUC of 0638 and 0636 are obtained in the test-ing and training sets respectively suggesting that CpGmethylation status in TSS200 is still somewhat predictiveof gene expression However a more accurate predictionusing methylation features should arise from all locationsassociated with the transcripts
DiscussionThe need to build predictive models of gene expressionfrom epigenomics dataAlthough currently integrative analyses between geneexpression and epigenetic modification exist we havefound that quantitative models using epigenetic informa-tion to accurately predict the up or down regulation ofgene expression are currently lacking There are oftencases where researchers can only obtain reliable epige-netics data but not gene expression data For example ifthe samples are archived and processed by FFPE (Forma-lin-fixed paraffin-embedded) one can still perform epige-nomics measurements but not the gene expressionexperiments due to the degradation of mRNA in the sam-ples More importantly a predictive method such as ourscan efficiently facilitate the bench scientists to narrowdown the candidate lists and conduct gene expression vali-dation especially when the epigenetics information is theonly data handy
Selected features and their relevance to gene expressionAll four types of data (CpG methylation histone H3modification nucleotide sequence and conservation)exist in the 67 features that are selected by the bestmodel indicating that all of them contribute to the accu-rate prediction of gene expression Moreover selectedfeatures of the same data type tend to cluster together onthe correlation matrix among the features suggestingthat the relationship within the same data type is closerthan the relationship between different data types Asexpected histone modification and CpG methylation fea-tures are the largest two groups among the four types ofdata signifying their importance to predict gene expres-sion Since nonlinear classification methods performslightly better than linear classification methods it sug-gests that interactions do exist between different types ofdata This is supported by numerous literatures that
enzymes responsible for CpG methylation also interactwith histone modification events [3233]Besides the value of predicting gene expression our
models also provide insights into the relative importanceof different epigenomicsgenome data as well as thegenomic locations We found that CpG methylation fea-tures have more predictive values for differential geneexpression compared to the three types of histone H3modification data Although other kinds of histone mod-ification data can also be obtained to increase the pre-dictive values of histone modification data it is muchmore costly to obtain them relative to the CpG methyla-tion data (the cost of CHIP-Seq on each of the histonemodification marker is similar to an entire CpG methy-lation array) Therefore practically speaking when thebudget is a constraining factor we suggest that assayson CpG methylation should be considered with priorityin predicting differential gene expression Moreover theresults of our models demonstrate that all genomic loca-tions relative to each transcript including promotersexons and gene bodies provide useful information topredict gene expression alternation Although the CpGmethylation signals from the promoters region are moreimportant the methylation signals from other regionssuch as exons introns and UTRs are indicative ofchanges in the gene expression as wellWorth noticing a lot of features that are extracted on
methylation and histone modification are naturallybased on the annotations from Illumina 450K array plat-form for DNA methylation There may be bias on thenumber of features that are hand coded in the modelTo address potential issue we changed TSS200_GC toTSS150_GC in our model and obtained an AUC = 861(compared to 0864) for cross fold validation on thetraining set and an AUC = 834 (compared to 0836) forthe testing set Therefore we think the bias due to rely-ing on the nomenclatures from Illuminarsquos annotation issmall
Limitations and future directionsWe should point out that our current model does notinclude all histone modification data but only threewidely used methylation markers on histone H3(H3K4Me3 H3K27Me3 and H3K36Me3) Moreover thehistone H3 data are drawn from ENCODE cell linessince the TCGA samples do not have such data Theheterogeneity of the sample resources could affect theaccuracy of the model When more histone marker datacoupled with DNA methylation and RNA-Seq databecome publicly available for lung cancer we caninclude them to achieve a better model In the ideal set-ting we would like to build a predictive model that hasmultiple types of epigenomics data obtained from thesame samples Another potential concern is overfitting
Li et al BMC Bioinformatics 2015 16(Suppl 5)S10httpwwwbiomedcentralcom1471-210516S5S10
Page 10 of 12
in the classification model However we split the datasetinto training and independent testing subsets and showthe model performs comparably well on the holdouttesting subset We believe that the model can be repli-cated if we can identify paired RNA-Seq and methyla-tion data In fact we had originally built this model ona private data set which also achieved an AUC of morethan 080 Additionally Figure 4 and 5 both indicatethat our approach does not suffer a significant over-fit-ting problem using the TCGA data and show the domi-nant efforts of histone modification and CpGmethylation which yield an updown gene expressionprediction with an AUCgt080 Currently the model useslung cancer data and it will be interesting find outmore general epigenetic predictors for differential geneexpression in other cancers as well Lastly we shouldpoint out that regulation of gene expression is complexincluding other mechanisms mediated by transcriptionfactors microRNA non-coding RNAs etc The fact thatAUCs hover between 080-090 ranges could be well dueto the fact that features from these other mechanismsare not considered in the current epigenetics model Toincrease the accuracy a more complex model that takesinto account of all these events should be constructed
ConclusionsA new model based on epigenomics data is proposed topredict transcriptome-level differential gene expressionin lung cancers Dropping-off feature sets by data typeshows that CpG methylation features are most impor-tant for the prediction Furthermore methylation fea-tures on all genomic regions relative to protein codinggenes contribute to the differential gene expressionwithin which promoter regions are most important
Additional material
Additional file 1 Table S1 Selected 67 features in the best modelsorted by category and their frequency
Competing interestsThe authors declare that they have no competing interests
Authorsrsquo contributionsLXG envisioned the project and supervised the work TC initiated theproject JL and TC designed and implemented the project SJ assisted theproject JL TC and LXG wrote the manuscript All authors have read revisedand approved the final manuscript
AcknowledgementsThe authors would like to thank Dr Jayson Masaki for reviewing themanuscript
DeclarationsPublication charges for this article were funded by NIHNIGMS P20 COBREGM103457 NIHNIEHS K01 ES025434-01 and Hawaii Community Foundation
This article has been published as part of BMC Bioinformatics Volume 16Supplement 5 2015 Selected articles from the 10th InternationalSymposium on Bioinformatics Research and Applications (ISBRA-14)Bioinformatics The full contents of the supplement are available online athttpwwwbiomedcentralcombmcbioinformaticssupplements16S5
Authorsrsquo details1Department of Biomedical Engineering Johns Hopkins University BaltimoreMD 21218 USA 2Molecular Biosciences and Bioengineering GraduateProgram University of Hawaii at Manoa Honolulu HI 96822 USA3Epidemiology Program University of Hawaii Cancer Center Honolulu HI96813 USA
Published 18 March 2015
References1 Portela A Esteller M Epigenetic modifications and human disease Nature
biotechnology 2010 28(10)1057-10682 Bock C Lengauer T Computational epigenetics Bioinformatics 2008
24(1)1-103 Laird PW Principles and challenges of genomewide DNA methylation
analysis Nature reviews Genetics 2010 11(3)191-2034 Lim SJ Tan TW Tong JC Computational Epigenetics the new scientific
paradigm Bioinformation 2010 4(7)331-3375 Gardiner-Garden M Frommer M CpG islands in vertebrate genomes
Journal of molecular biology 1987 196(2)261-2826 Daura-Oller E Cabre M Montero MA Paternain JL Romeu A Specific gene
hypomethylation and cancer New insights into coding region featuretrends Bioinformation 2009 3(8)340
7 Wild L Flanagan JM Genome-wide hypomethylation in cancer may be apassive consequence of transformation Biochimica et biophysica acta2010 1806(1)50-57
8 Figueroa ME Chen SC Andersson AK Phillips LA Li Y Sotzen J Kundu MDowning JR Melnick A Mullighan CG Integrated genetic and epigeneticanalysis of childhood acute lymphoblastic leukemia The Journal ofclinical investigation 2013 123(7)3099-3111
9 Rhee JK Kim K Chae H Evans J Yan P Zhang BT Gray J Spellman PHuang TH Nephew KP et al Integrated analysis of genome-wide DNAmethylation and gene expression profiles in molecular subtypes ofbreast cancer Nucleic acids research 2013 41(18)8464-8474
10 An integrated encyclopedia of DNA elements in the human genomeNature 2012 489(7414)57-74
11 Karolchik D Hinrichs AS Furey TS Roskin KM Sugnet CW Haussler DKent WJ The UCSC Table Browser data retrieval tool Nucleic acidsresearch 2004 32 Database D493-496
12 Langmead B Salzberg SL Fast gapped-read alignment with Bowtie 2Nature methods 2012 9(4)357-359
13 Li H Handsaker B Wysoker A Fennell T Ruan J Homer N Marth GAbecasis G Durbin R Genome Project Data Processing S The SequenceAlignmentMap format and SAMtools Bioinformatics 200925(16)2078-2079
14 Quinlan AR Hall IM BEDTools a flexible suite of utilities for comparinggenomic features Bioinformatics 2010 26(6)841-842
15 Siepel A Bejerano G Pedersen JS Hinrichs AS Hou M Rosenbloom KClawson H Spieth J Hillier LW Richards S Evolutionarily conservedelements in vertebrate insect worm and yeast genomes Genomeresearch 2005 15(8)1034-1050
16 Love MI Huber W Anders S Moderated estimation of fold change anddispersion for RNA-Seq data with DESeq2 bioRxiv 2014
17 Holm S A simple sequentially rejective multiple test procedureScandinavian journal of statistics 1979 65-70
18 Smyth GK Limma linear models for microarray data Bioinformatics andcomputational biology solutions using R and Bioconductor Springer 2005397-420
19 Quinlan AR Hall IM BEDTools a flexible suite of utilities for comparinggenomic features Bioinformatics 2010 26(6)841-842
20 Pages H Aboyoun P Gentleman R DebRoy S String objects representingbiological sequences and matching algorithms R package version 2009 2(2)
21 Hall MA Smith LA Feature Selection for Machine Learning Comparing aCorrelation-Based Filter Approach to the Wrapper FLAIRS Conference1999 1999 235-239
Li et al BMC Bioinformatics 2015 16(Suppl 5)S10httpwwwbiomedcentralcom1471-210516S5S10
Page 11 of 12
22 Quinlan JR Induction of decision trees Machine learning 1986 1(1)81-10623 Kononenko I Šimec E Robnik-Šikonja M Overcoming the Myopia of
Inductive Learning Algorithms with RELIEFF Applied Intelligence 19977(1)39-55
24 Kira K Rendell LA The feature selection problem Traditional methodsand a new algorithm AAAI 1992 1992 129-134
25 Hall M Frank E Holmes G Pfahringer B Reutemann P Witten IH TheWEKA data mining software an update ACM SIGKDD ExplorationsNewsletter 2009 11(1)10-18
26 Wei T Corrplot visualization of a correlation matrix R package version 02-0 Available on Comprehensive R Archive Network website 2010 [httpCRANR-project orgpackage=corrplot] (accessed 2010)
27 Sing T Sander O Beerenwinkel N Lengauer T ROCR visualizing classifierperformance in R Bioinformatics 2005 21(20)3940-3941
28 Fuks F Hurd PJ Wolf D Nan X Bird AP Kouzarides T The methyl-CpG-binding protein MeCP2 links DNA methylation to histone methylationJournal of Biological Chemistry 2003 278(6)4035-4040
29 Cedar H Bergman Y Linking DNA methylation and histone modificationpatterns and paradigms Nat Rev Genet 2009 10(5)295-304
30 Jones PA Functions of DNA methylation islands start sites gene bodiesand beyond Nat Rev Genet 2012 13(7)484-492
31 Maunakea AK Nagarajan RP Bilenky M Ballinger TJ DrsquoSouza C Fouse SDJohnson BE Hong C Nielsen C Zhao Y Conserved role of intragenic DNAmethylation in regulating alternative promoters Nature 2010466(7303)253-257
32 Bowen NJ Palmer MB Wade PA Chromosomal regulation by MeCP2structural and enzymatic considerations Cellular and molecular lifesciences CMLS 2004 61(17)2163-2167
33 Ooi SK Qiu C Bernstein E Li K Jia D Yang Z Erdjument-Bromage HTempst P Lin SP Allis CD et al DNMT3L connects unmethylated lysine 4of histone H3 to de novo methylation of DNA Nature 2007448(7154)714-717
doi1011861471-2105-16-S5-S10Cite this article as Li et al Using epigenomics data to predict geneexpression in lung cancer BMC Bioinformatics 2015 16(Suppl 5)S10
Submit your next manuscript to BioMed Centraland take full advantage of
bull Convenient online submission
bull Thorough peer review
bull No space constraints or color figure charges
bull Immediate publication on acceptance
bull Inclusion in PubMed CAS Scopus and Google Scholar
bull Research which is freely available for redistribution
Submit your manuscript at wwwbiomedcentralcomsubmit
Li et al BMC Bioinformatics 2015 16(Suppl 5)S10httpwwwbiomedcentralcom1471-210516S5S10
Page 12 of 12
Abstract
Background
Methods
Results
Conclusions
Background
Methods
Data sets
Data processing
Methylation data
Histone data
Human genome data
RNA-seq data
Feature extraction
CpG Methylation features
Histone marker modification features
Nucleotide features
Conservation features
Feature selection
Model evaluation
Software
Results
Summary of input data and features
Model selection and evaluation
Analysis of selected features
Evaluation of features by data type
Evaluation of CpG methylation features by locations relative to transcripts
Discussion
The need to build predictive models of gene expression from epigenomics data
Selected features and their relevance to gene expression
Limitations and future directions
Conclusions
Competing interests
Authorsrsquo contributions
Acknowledgements
Declarations
Authorsrsquo details
References
question of the relative importance of each methylationfeature categorized by genomic location We performeddrop-off tests by sequentially removing features in eachgenomic location category We first removed the featuresfrom the first exons and first introns (as they are close to
the TSS) then from gene bodies including exons intronsand UTRs and lastly from TSS1500 region such that onlyTSS200 features were kept At each step we re-performedfeature selection and model construction using theremaining methylation features
Figure 5 Evaluation of methylation features by segment (a-b) Effects of segment-based methylation feature set sequential drop-off on ROCcurves from the 10-fold cross-validation training set (a) and testing set (b) (c) Effects of segment-based methylation feature set sequential drop-off on other four metrics AUC Accuracy F-measure and MCC in the training set and testing set
Li et al BMC Bioinformatics 2015 16(Suppl 5)S10httpwwwbiomedcentralcom1471-210516S5S10
Page 9 of 12
All categories of genomic locations provide relevantuseful information that contributes to better predictionof gene expression as each of the sequential feature setdrop-off decreased the performance of the model in bothtraining and testing sets Compared to the removal offirst exon and intron regions and removal of the UTRsand the rest of the gene body removal of TSS1500 leadsto the largest reduction in all four metrics confirmingthe importance of the promoter region in regulating geneexpression Even when only TSS200 features are consid-ered an AUC of 0638 and 0636 are obtained in the test-ing and training sets respectively suggesting that CpGmethylation status in TSS200 is still somewhat predictiveof gene expression However a more accurate predictionusing methylation features should arise from all locationsassociated with the transcripts
DiscussionThe need to build predictive models of gene expressionfrom epigenomics dataAlthough currently integrative analyses between geneexpression and epigenetic modification exist we havefound that quantitative models using epigenetic informa-tion to accurately predict the up or down regulation ofgene expression are currently lacking There are oftencases where researchers can only obtain reliable epige-netics data but not gene expression data For example ifthe samples are archived and processed by FFPE (Forma-lin-fixed paraffin-embedded) one can still perform epige-nomics measurements but not the gene expressionexperiments due to the degradation of mRNA in the sam-ples More importantly a predictive method such as ourscan efficiently facilitate the bench scientists to narrowdown the candidate lists and conduct gene expression vali-dation especially when the epigenetics information is theonly data handy
Selected features and their relevance to gene expressionAll four types of data (CpG methylation histone H3modification nucleotide sequence and conservation)exist in the 67 features that are selected by the bestmodel indicating that all of them contribute to the accu-rate prediction of gene expression Moreover selectedfeatures of the same data type tend to cluster together onthe correlation matrix among the features suggestingthat the relationship within the same data type is closerthan the relationship between different data types Asexpected histone modification and CpG methylation fea-tures are the largest two groups among the four types ofdata signifying their importance to predict gene expres-sion Since nonlinear classification methods performslightly better than linear classification methods it sug-gests that interactions do exist between different types ofdata This is supported by numerous literatures that
enzymes responsible for CpG methylation also interactwith histone modification events [3233]Besides the value of predicting gene expression our
models also provide insights into the relative importanceof different epigenomicsgenome data as well as thegenomic locations We found that CpG methylation fea-tures have more predictive values for differential geneexpression compared to the three types of histone H3modification data Although other kinds of histone mod-ification data can also be obtained to increase the pre-dictive values of histone modification data it is muchmore costly to obtain them relative to the CpG methyla-tion data (the cost of CHIP-Seq on each of the histonemodification marker is similar to an entire CpG methy-lation array) Therefore practically speaking when thebudget is a constraining factor we suggest that assayson CpG methylation should be considered with priorityin predicting differential gene expression Moreover theresults of our models demonstrate that all genomic loca-tions relative to each transcript including promotersexons and gene bodies provide useful information topredict gene expression alternation Although the CpGmethylation signals from the promoters region are moreimportant the methylation signals from other regionssuch as exons introns and UTRs are indicative ofchanges in the gene expression as wellWorth noticing a lot of features that are extracted on
methylation and histone modification are naturallybased on the annotations from Illumina 450K array plat-form for DNA methylation There may be bias on thenumber of features that are hand coded in the modelTo address potential issue we changed TSS200_GC toTSS150_GC in our model and obtained an AUC = 861(compared to 0864) for cross fold validation on thetraining set and an AUC = 834 (compared to 0836) forthe testing set Therefore we think the bias due to rely-ing on the nomenclatures from Illuminarsquos annotation issmall
Limitations and future directionsWe should point out that our current model does notinclude all histone modification data but only threewidely used methylation markers on histone H3(H3K4Me3 H3K27Me3 and H3K36Me3) Moreover thehistone H3 data are drawn from ENCODE cell linessince the TCGA samples do not have such data Theheterogeneity of the sample resources could affect theaccuracy of the model When more histone marker datacoupled with DNA methylation and RNA-Seq databecome publicly available for lung cancer we caninclude them to achieve a better model In the ideal set-ting we would like to build a predictive model that hasmultiple types of epigenomics data obtained from thesame samples Another potential concern is overfitting
Li et al BMC Bioinformatics 2015 16(Suppl 5)S10httpwwwbiomedcentralcom1471-210516S5S10
Page 10 of 12
in the classification model However we split the datasetinto training and independent testing subsets and showthe model performs comparably well on the holdouttesting subset We believe that the model can be repli-cated if we can identify paired RNA-Seq and methyla-tion data In fact we had originally built this model ona private data set which also achieved an AUC of morethan 080 Additionally Figure 4 and 5 both indicatethat our approach does not suffer a significant over-fit-ting problem using the TCGA data and show the domi-nant efforts of histone modification and CpGmethylation which yield an updown gene expressionprediction with an AUCgt080 Currently the model useslung cancer data and it will be interesting find outmore general epigenetic predictors for differential geneexpression in other cancers as well Lastly we shouldpoint out that regulation of gene expression is complexincluding other mechanisms mediated by transcriptionfactors microRNA non-coding RNAs etc The fact thatAUCs hover between 080-090 ranges could be well dueto the fact that features from these other mechanismsare not considered in the current epigenetics model Toincrease the accuracy a more complex model that takesinto account of all these events should be constructed
ConclusionsA new model based on epigenomics data is proposed topredict transcriptome-level differential gene expressionin lung cancers Dropping-off feature sets by data typeshows that CpG methylation features are most impor-tant for the prediction Furthermore methylation fea-tures on all genomic regions relative to protein codinggenes contribute to the differential gene expressionwithin which promoter regions are most important
Additional material
Additional file 1 Table S1 Selected 67 features in the best modelsorted by category and their frequency
Competing interestsThe authors declare that they have no competing interests
Authorsrsquo contributionsLXG envisioned the project and supervised the work TC initiated theproject JL and TC designed and implemented the project SJ assisted theproject JL TC and LXG wrote the manuscript All authors have read revisedand approved the final manuscript
AcknowledgementsThe authors would like to thank Dr Jayson Masaki for reviewing themanuscript
DeclarationsPublication charges for this article were funded by NIHNIGMS P20 COBREGM103457 NIHNIEHS K01 ES025434-01 and Hawaii Community Foundation
This article has been published as part of BMC Bioinformatics Volume 16Supplement 5 2015 Selected articles from the 10th InternationalSymposium on Bioinformatics Research and Applications (ISBRA-14)Bioinformatics The full contents of the supplement are available online athttpwwwbiomedcentralcombmcbioinformaticssupplements16S5
Authorsrsquo details1Department of Biomedical Engineering Johns Hopkins University BaltimoreMD 21218 USA 2Molecular Biosciences and Bioengineering GraduateProgram University of Hawaii at Manoa Honolulu HI 96822 USA3Epidemiology Program University of Hawaii Cancer Center Honolulu HI96813 USA
Published 18 March 2015
References1 Portela A Esteller M Epigenetic modifications and human disease Nature
biotechnology 2010 28(10)1057-10682 Bock C Lengauer T Computational epigenetics Bioinformatics 2008
24(1)1-103 Laird PW Principles and challenges of genomewide DNA methylation
analysis Nature reviews Genetics 2010 11(3)191-2034 Lim SJ Tan TW Tong JC Computational Epigenetics the new scientific
paradigm Bioinformation 2010 4(7)331-3375 Gardiner-Garden M Frommer M CpG islands in vertebrate genomes
Journal of molecular biology 1987 196(2)261-2826 Daura-Oller E Cabre M Montero MA Paternain JL Romeu A Specific gene
hypomethylation and cancer New insights into coding region featuretrends Bioinformation 2009 3(8)340
7 Wild L Flanagan JM Genome-wide hypomethylation in cancer may be apassive consequence of transformation Biochimica et biophysica acta2010 1806(1)50-57
8 Figueroa ME Chen SC Andersson AK Phillips LA Li Y Sotzen J Kundu MDowning JR Melnick A Mullighan CG Integrated genetic and epigeneticanalysis of childhood acute lymphoblastic leukemia The Journal ofclinical investigation 2013 123(7)3099-3111
9 Rhee JK Kim K Chae H Evans J Yan P Zhang BT Gray J Spellman PHuang TH Nephew KP et al Integrated analysis of genome-wide DNAmethylation and gene expression profiles in molecular subtypes ofbreast cancer Nucleic acids research 2013 41(18)8464-8474
10 An integrated encyclopedia of DNA elements in the human genomeNature 2012 489(7414)57-74
11 Karolchik D Hinrichs AS Furey TS Roskin KM Sugnet CW Haussler DKent WJ The UCSC Table Browser data retrieval tool Nucleic acidsresearch 2004 32 Database D493-496
12 Langmead B Salzberg SL Fast gapped-read alignment with Bowtie 2Nature methods 2012 9(4)357-359
13 Li H Handsaker B Wysoker A Fennell T Ruan J Homer N Marth GAbecasis G Durbin R Genome Project Data Processing S The SequenceAlignmentMap format and SAMtools Bioinformatics 200925(16)2078-2079
14 Quinlan AR Hall IM BEDTools a flexible suite of utilities for comparinggenomic features Bioinformatics 2010 26(6)841-842
15 Siepel A Bejerano G Pedersen JS Hinrichs AS Hou M Rosenbloom KClawson H Spieth J Hillier LW Richards S Evolutionarily conservedelements in vertebrate insect worm and yeast genomes Genomeresearch 2005 15(8)1034-1050
16 Love MI Huber W Anders S Moderated estimation of fold change anddispersion for RNA-Seq data with DESeq2 bioRxiv 2014
17 Holm S A simple sequentially rejective multiple test procedureScandinavian journal of statistics 1979 65-70
18 Smyth GK Limma linear models for microarray data Bioinformatics andcomputational biology solutions using R and Bioconductor Springer 2005397-420
19 Quinlan AR Hall IM BEDTools a flexible suite of utilities for comparinggenomic features Bioinformatics 2010 26(6)841-842
20 Pages H Aboyoun P Gentleman R DebRoy S String objects representingbiological sequences and matching algorithms R package version 2009 2(2)
21 Hall MA Smith LA Feature Selection for Machine Learning Comparing aCorrelation-Based Filter Approach to the Wrapper FLAIRS Conference1999 1999 235-239
Li et al BMC Bioinformatics 2015 16(Suppl 5)S10httpwwwbiomedcentralcom1471-210516S5S10
Page 11 of 12
22 Quinlan JR Induction of decision trees Machine learning 1986 1(1)81-10623 Kononenko I Šimec E Robnik-Šikonja M Overcoming the Myopia of
Inductive Learning Algorithms with RELIEFF Applied Intelligence 19977(1)39-55
24 Kira K Rendell LA The feature selection problem Traditional methodsand a new algorithm AAAI 1992 1992 129-134
25 Hall M Frank E Holmes G Pfahringer B Reutemann P Witten IH TheWEKA data mining software an update ACM SIGKDD ExplorationsNewsletter 2009 11(1)10-18
26 Wei T Corrplot visualization of a correlation matrix R package version 02-0 Available on Comprehensive R Archive Network website 2010 [httpCRANR-project orgpackage=corrplot] (accessed 2010)
27 Sing T Sander O Beerenwinkel N Lengauer T ROCR visualizing classifierperformance in R Bioinformatics 2005 21(20)3940-3941
28 Fuks F Hurd PJ Wolf D Nan X Bird AP Kouzarides T The methyl-CpG-binding protein MeCP2 links DNA methylation to histone methylationJournal of Biological Chemistry 2003 278(6)4035-4040
29 Cedar H Bergman Y Linking DNA methylation and histone modificationpatterns and paradigms Nat Rev Genet 2009 10(5)295-304
30 Jones PA Functions of DNA methylation islands start sites gene bodiesand beyond Nat Rev Genet 2012 13(7)484-492
31 Maunakea AK Nagarajan RP Bilenky M Ballinger TJ DrsquoSouza C Fouse SDJohnson BE Hong C Nielsen C Zhao Y Conserved role of intragenic DNAmethylation in regulating alternative promoters Nature 2010466(7303)253-257
32 Bowen NJ Palmer MB Wade PA Chromosomal regulation by MeCP2structural and enzymatic considerations Cellular and molecular lifesciences CMLS 2004 61(17)2163-2167
33 Ooi SK Qiu C Bernstein E Li K Jia D Yang Z Erdjument-Bromage HTempst P Lin SP Allis CD et al DNMT3L connects unmethylated lysine 4of histone H3 to de novo methylation of DNA Nature 2007448(7154)714-717
doi1011861471-2105-16-S5-S10Cite this article as Li et al Using epigenomics data to predict geneexpression in lung cancer BMC Bioinformatics 2015 16(Suppl 5)S10
Submit your next manuscript to BioMed Centraland take full advantage of
bull Convenient online submission
bull Thorough peer review
bull No space constraints or color figure charges
bull Immediate publication on acceptance
bull Inclusion in PubMed CAS Scopus and Google Scholar
bull Research which is freely available for redistribution
Submit your manuscript at wwwbiomedcentralcomsubmit
Li et al BMC Bioinformatics 2015 16(Suppl 5)S10httpwwwbiomedcentralcom1471-210516S5S10
Page 12 of 12
Abstract
Background
Methods
Results
Conclusions
Background
Methods
Data sets
Data processing
Methylation data
Histone data
Human genome data
RNA-seq data
Feature extraction
CpG Methylation features
Histone marker modification features
Nucleotide features
Conservation features
Feature selection
Model evaluation
Software
Results
Summary of input data and features
Model selection and evaluation
Analysis of selected features
Evaluation of features by data type
Evaluation of CpG methylation features by locations relative to transcripts
Discussion
The need to build predictive models of gene expression from epigenomics data
Selected features and their relevance to gene expression
Limitations and future directions
Conclusions
Competing interests
Authorsrsquo contributions
Acknowledgements
Declarations
Authorsrsquo details
References
All categories of genomic locations provide relevantuseful information that contributes to better predictionof gene expression as each of the sequential feature setdrop-off decreased the performance of the model in bothtraining and testing sets Compared to the removal offirst exon and intron regions and removal of the UTRsand the rest of the gene body removal of TSS1500 leadsto the largest reduction in all four metrics confirmingthe importance of the promoter region in regulating geneexpression Even when only TSS200 features are consid-ered an AUC of 0638 and 0636 are obtained in the test-ing and training sets respectively suggesting that CpGmethylation status in TSS200 is still somewhat predictiveof gene expression However a more accurate predictionusing methylation features should arise from all locationsassociated with the transcripts
DiscussionThe need to build predictive models of gene expressionfrom epigenomics dataAlthough currently integrative analyses between geneexpression and epigenetic modification exist we havefound that quantitative models using epigenetic informa-tion to accurately predict the up or down regulation ofgene expression are currently lacking There are oftencases where researchers can only obtain reliable epige-netics data but not gene expression data For example ifthe samples are archived and processed by FFPE (Forma-lin-fixed paraffin-embedded) one can still perform epige-nomics measurements but not the gene expressionexperiments due to the degradation of mRNA in the sam-ples More importantly a predictive method such as ourscan efficiently facilitate the bench scientists to narrowdown the candidate lists and conduct gene expression vali-dation especially when the epigenetics information is theonly data handy
Selected features and their relevance to gene expressionAll four types of data (CpG methylation histone H3modification nucleotide sequence and conservation)exist in the 67 features that are selected by the bestmodel indicating that all of them contribute to the accu-rate prediction of gene expression Moreover selectedfeatures of the same data type tend to cluster together onthe correlation matrix among the features suggestingthat the relationship within the same data type is closerthan the relationship between different data types Asexpected histone modification and CpG methylation fea-tures are the largest two groups among the four types ofdata signifying their importance to predict gene expres-sion Since nonlinear classification methods performslightly better than linear classification methods it sug-gests that interactions do exist between different types ofdata This is supported by numerous literatures that
enzymes responsible for CpG methylation also interactwith histone modification events [3233]Besides the value of predicting gene expression our
models also provide insights into the relative importanceof different epigenomicsgenome data as well as thegenomic locations We found that CpG methylation fea-tures have more predictive values for differential geneexpression compared to the three types of histone H3modification data Although other kinds of histone mod-ification data can also be obtained to increase the pre-dictive values of histone modification data it is muchmore costly to obtain them relative to the CpG methyla-tion data (the cost of CHIP-Seq on each of the histonemodification marker is similar to an entire CpG methy-lation array) Therefore practically speaking when thebudget is a constraining factor we suggest that assayson CpG methylation should be considered with priorityin predicting differential gene expression Moreover theresults of our models demonstrate that all genomic loca-tions relative to each transcript including promotersexons and gene bodies provide useful information topredict gene expression alternation Although the CpGmethylation signals from the promoters region are moreimportant the methylation signals from other regionssuch as exons introns and UTRs are indicative ofchanges in the gene expression as wellWorth noticing a lot of features that are extracted on
methylation and histone modification are naturallybased on the annotations from Illumina 450K array plat-form for DNA methylation There may be bias on thenumber of features that are hand coded in the modelTo address potential issue we changed TSS200_GC toTSS150_GC in our model and obtained an AUC = 861(compared to 0864) for cross fold validation on thetraining set and an AUC = 834 (compared to 0836) forthe testing set Therefore we think the bias due to rely-ing on the nomenclatures from Illuminarsquos annotation issmall
Limitations and future directionsWe should point out that our current model does notinclude all histone modification data but only threewidely used methylation markers on histone H3(H3K4Me3 H3K27Me3 and H3K36Me3) Moreover thehistone H3 data are drawn from ENCODE cell linessince the TCGA samples do not have such data Theheterogeneity of the sample resources could affect theaccuracy of the model When more histone marker datacoupled with DNA methylation and RNA-Seq databecome publicly available for lung cancer we caninclude them to achieve a better model In the ideal set-ting we would like to build a predictive model that hasmultiple types of epigenomics data obtained from thesame samples Another potential concern is overfitting
Li et al BMC Bioinformatics 2015 16(Suppl 5)S10httpwwwbiomedcentralcom1471-210516S5S10
Page 10 of 12
in the classification model However we split the datasetinto training and independent testing subsets and showthe model performs comparably well on the holdouttesting subset We believe that the model can be repli-cated if we can identify paired RNA-Seq and methyla-tion data In fact we had originally built this model ona private data set which also achieved an AUC of morethan 080 Additionally Figure 4 and 5 both indicatethat our approach does not suffer a significant over-fit-ting problem using the TCGA data and show the domi-nant efforts of histone modification and CpGmethylation which yield an updown gene expressionprediction with an AUCgt080 Currently the model useslung cancer data and it will be interesting find outmore general epigenetic predictors for differential geneexpression in other cancers as well Lastly we shouldpoint out that regulation of gene expression is complexincluding other mechanisms mediated by transcriptionfactors microRNA non-coding RNAs etc The fact thatAUCs hover between 080-090 ranges could be well dueto the fact that features from these other mechanismsare not considered in the current epigenetics model Toincrease the accuracy a more complex model that takesinto account of all these events should be constructed
ConclusionsA new model based on epigenomics data is proposed topredict transcriptome-level differential gene expressionin lung cancers Dropping-off feature sets by data typeshows that CpG methylation features are most impor-tant for the prediction Furthermore methylation fea-tures on all genomic regions relative to protein codinggenes contribute to the differential gene expressionwithin which promoter regions are most important
Additional material
Additional file 1 Table S1 Selected 67 features in the best modelsorted by category and their frequency
Competing interestsThe authors declare that they have no competing interests
Authorsrsquo contributionsLXG envisioned the project and supervised the work TC initiated theproject JL and TC designed and implemented the project SJ assisted theproject JL TC and LXG wrote the manuscript All authors have read revisedand approved the final manuscript
AcknowledgementsThe authors would like to thank Dr Jayson Masaki for reviewing themanuscript
DeclarationsPublication charges for this article were funded by NIHNIGMS P20 COBREGM103457 NIHNIEHS K01 ES025434-01 and Hawaii Community Foundation
This article has been published as part of BMC Bioinformatics Volume 16Supplement 5 2015 Selected articles from the 10th InternationalSymposium on Bioinformatics Research and Applications (ISBRA-14)Bioinformatics The full contents of the supplement are available online athttpwwwbiomedcentralcombmcbioinformaticssupplements16S5
Authorsrsquo details1Department of Biomedical Engineering Johns Hopkins University BaltimoreMD 21218 USA 2Molecular Biosciences and Bioengineering GraduateProgram University of Hawaii at Manoa Honolulu HI 96822 USA3Epidemiology Program University of Hawaii Cancer Center Honolulu HI96813 USA
Published 18 March 2015
References1 Portela A Esteller M Epigenetic modifications and human disease Nature
biotechnology 2010 28(10)1057-10682 Bock C Lengauer T Computational epigenetics Bioinformatics 2008
24(1)1-103 Laird PW Principles and challenges of genomewide DNA methylation
analysis Nature reviews Genetics 2010 11(3)191-2034 Lim SJ Tan TW Tong JC Computational Epigenetics the new scientific
paradigm Bioinformation 2010 4(7)331-3375 Gardiner-Garden M Frommer M CpG islands in vertebrate genomes
Journal of molecular biology 1987 196(2)261-2826 Daura-Oller E Cabre M Montero MA Paternain JL Romeu A Specific gene
hypomethylation and cancer New insights into coding region featuretrends Bioinformation 2009 3(8)340
7 Wild L Flanagan JM Genome-wide hypomethylation in cancer may be apassive consequence of transformation Biochimica et biophysica acta2010 1806(1)50-57
8 Figueroa ME Chen SC Andersson AK Phillips LA Li Y Sotzen J Kundu MDowning JR Melnick A Mullighan CG Integrated genetic and epigeneticanalysis of childhood acute lymphoblastic leukemia The Journal ofclinical investigation 2013 123(7)3099-3111
9 Rhee JK Kim K Chae H Evans J Yan P Zhang BT Gray J Spellman PHuang TH Nephew KP et al Integrated analysis of genome-wide DNAmethylation and gene expression profiles in molecular subtypes ofbreast cancer Nucleic acids research 2013 41(18)8464-8474
10 An integrated encyclopedia of DNA elements in the human genomeNature 2012 489(7414)57-74
11 Karolchik D Hinrichs AS Furey TS Roskin KM Sugnet CW Haussler DKent WJ The UCSC Table Browser data retrieval tool Nucleic acidsresearch 2004 32 Database D493-496
12 Langmead B Salzberg SL Fast gapped-read alignment with Bowtie 2Nature methods 2012 9(4)357-359
13 Li H Handsaker B Wysoker A Fennell T Ruan J Homer N Marth GAbecasis G Durbin R Genome Project Data Processing S The SequenceAlignmentMap format and SAMtools Bioinformatics 200925(16)2078-2079
14 Quinlan AR Hall IM BEDTools a flexible suite of utilities for comparinggenomic features Bioinformatics 2010 26(6)841-842
15 Siepel A Bejerano G Pedersen JS Hinrichs AS Hou M Rosenbloom KClawson H Spieth J Hillier LW Richards S Evolutionarily conservedelements in vertebrate insect worm and yeast genomes Genomeresearch 2005 15(8)1034-1050
16 Love MI Huber W Anders S Moderated estimation of fold change anddispersion for RNA-Seq data with DESeq2 bioRxiv 2014
17 Holm S A simple sequentially rejective multiple test procedureScandinavian journal of statistics 1979 65-70
18 Smyth GK Limma linear models for microarray data Bioinformatics andcomputational biology solutions using R and Bioconductor Springer 2005397-420
19 Quinlan AR Hall IM BEDTools a flexible suite of utilities for comparinggenomic features Bioinformatics 2010 26(6)841-842
20 Pages H Aboyoun P Gentleman R DebRoy S String objects representingbiological sequences and matching algorithms R package version 2009 2(2)
21 Hall MA Smith LA Feature Selection for Machine Learning Comparing aCorrelation-Based Filter Approach to the Wrapper FLAIRS Conference1999 1999 235-239
Li et al BMC Bioinformatics 2015 16(Suppl 5)S10httpwwwbiomedcentralcom1471-210516S5S10
Page 11 of 12
22 Quinlan JR Induction of decision trees Machine learning 1986 1(1)81-10623 Kononenko I Šimec E Robnik-Šikonja M Overcoming the Myopia of
Inductive Learning Algorithms with RELIEFF Applied Intelligence 19977(1)39-55
24 Kira K Rendell LA The feature selection problem Traditional methodsand a new algorithm AAAI 1992 1992 129-134
25 Hall M Frank E Holmes G Pfahringer B Reutemann P Witten IH TheWEKA data mining software an update ACM SIGKDD ExplorationsNewsletter 2009 11(1)10-18
26 Wei T Corrplot visualization of a correlation matrix R package version 02-0 Available on Comprehensive R Archive Network website 2010 [httpCRANR-project orgpackage=corrplot] (accessed 2010)
27 Sing T Sander O Beerenwinkel N Lengauer T ROCR visualizing classifierperformance in R Bioinformatics 2005 21(20)3940-3941
28 Fuks F Hurd PJ Wolf D Nan X Bird AP Kouzarides T The methyl-CpG-binding protein MeCP2 links DNA methylation to histone methylationJournal of Biological Chemistry 2003 278(6)4035-4040
29 Cedar H Bergman Y Linking DNA methylation and histone modificationpatterns and paradigms Nat Rev Genet 2009 10(5)295-304
30 Jones PA Functions of DNA methylation islands start sites gene bodiesand beyond Nat Rev Genet 2012 13(7)484-492
31 Maunakea AK Nagarajan RP Bilenky M Ballinger TJ DrsquoSouza C Fouse SDJohnson BE Hong C Nielsen C Zhao Y Conserved role of intragenic DNAmethylation in regulating alternative promoters Nature 2010466(7303)253-257
32 Bowen NJ Palmer MB Wade PA Chromosomal regulation by MeCP2structural and enzymatic considerations Cellular and molecular lifesciences CMLS 2004 61(17)2163-2167
33 Ooi SK Qiu C Bernstein E Li K Jia D Yang Z Erdjument-Bromage HTempst P Lin SP Allis CD et al DNMT3L connects unmethylated lysine 4of histone H3 to de novo methylation of DNA Nature 2007448(7154)714-717
doi1011861471-2105-16-S5-S10Cite this article as Li et al Using epigenomics data to predict geneexpression in lung cancer BMC Bioinformatics 2015 16(Suppl 5)S10
Submit your next manuscript to BioMed Centraland take full advantage of
bull Convenient online submission
bull Thorough peer review
bull No space constraints or color figure charges
bull Immediate publication on acceptance
bull Inclusion in PubMed CAS Scopus and Google Scholar
bull Research which is freely available for redistribution
Submit your manuscript at wwwbiomedcentralcomsubmit
Li et al BMC Bioinformatics 2015 16(Suppl 5)S10httpwwwbiomedcentralcom1471-210516S5S10
Page 12 of 12
Abstract
Background
Methods
Results
Conclusions
Background
Methods
Data sets
Data processing
Methylation data
Histone data
Human genome data
RNA-seq data
Feature extraction
CpG Methylation features
Histone marker modification features
Nucleotide features
Conservation features
Feature selection
Model evaluation
Software
Results
Summary of input data and features
Model selection and evaluation
Analysis of selected features
Evaluation of features by data type
Evaluation of CpG methylation features by locations relative to transcripts
Discussion
The need to build predictive models of gene expression from epigenomics data
Selected features and their relevance to gene expression
Limitations and future directions
Conclusions
Competing interests
Authorsrsquo contributions
Acknowledgements
Declarations
Authorsrsquo details
References
in the classification model However we split the datasetinto training and independent testing subsets and showthe model performs comparably well on the holdouttesting subset We believe that the model can be repli-cated if we can identify paired RNA-Seq and methyla-tion data In fact we had originally built this model ona private data set which also achieved an AUC of morethan 080 Additionally Figure 4 and 5 both indicatethat our approach does not suffer a significant over-fit-ting problem using the TCGA data and show the domi-nant efforts of histone modification and CpGmethylation which yield an updown gene expressionprediction with an AUCgt080 Currently the model useslung cancer data and it will be interesting find outmore general epigenetic predictors for differential geneexpression in other cancers as well Lastly we shouldpoint out that regulation of gene expression is complexincluding other mechanisms mediated by transcriptionfactors microRNA non-coding RNAs etc The fact thatAUCs hover between 080-090 ranges could be well dueto the fact that features from these other mechanismsare not considered in the current epigenetics model Toincrease the accuracy a more complex model that takesinto account of all these events should be constructed
ConclusionsA new model based on epigenomics data is proposed topredict transcriptome-level differential gene expressionin lung cancers Dropping-off feature sets by data typeshows that CpG methylation features are most impor-tant for the prediction Furthermore methylation fea-tures on all genomic regions relative to protein codinggenes contribute to the differential gene expressionwithin which promoter regions are most important
Additional material
Additional file 1 Table S1 Selected 67 features in the best modelsorted by category and their frequency
Competing interestsThe authors declare that they have no competing interests
Authorsrsquo contributionsLXG envisioned the project and supervised the work TC initiated theproject JL and TC designed and implemented the project SJ assisted theproject JL TC and LXG wrote the manuscript All authors have read revisedand approved the final manuscript
AcknowledgementsThe authors would like to thank Dr Jayson Masaki for reviewing themanuscript
DeclarationsPublication charges for this article were funded by NIHNIGMS P20 COBREGM103457 NIHNIEHS K01 ES025434-01 and Hawaii Community Foundation
This article has been published as part of BMC Bioinformatics Volume 16Supplement 5 2015 Selected articles from the 10th InternationalSymposium on Bioinformatics Research and Applications (ISBRA-14)Bioinformatics The full contents of the supplement are available online athttpwwwbiomedcentralcombmcbioinformaticssupplements16S5
Authorsrsquo details1Department of Biomedical Engineering Johns Hopkins University BaltimoreMD 21218 USA 2Molecular Biosciences and Bioengineering GraduateProgram University of Hawaii at Manoa Honolulu HI 96822 USA3Epidemiology Program University of Hawaii Cancer Center Honolulu HI96813 USA
Published 18 March 2015
References1 Portela A Esteller M Epigenetic modifications and human disease Nature
biotechnology 2010 28(10)1057-10682 Bock C Lengauer T Computational epigenetics Bioinformatics 2008
24(1)1-103 Laird PW Principles and challenges of genomewide DNA methylation
analysis Nature reviews Genetics 2010 11(3)191-2034 Lim SJ Tan TW Tong JC Computational Epigenetics the new scientific
paradigm Bioinformation 2010 4(7)331-3375 Gardiner-Garden M Frommer M CpG islands in vertebrate genomes
Journal of molecular biology 1987 196(2)261-2826 Daura-Oller E Cabre M Montero MA Paternain JL Romeu A Specific gene
hypomethylation and cancer New insights into coding region featuretrends Bioinformation 2009 3(8)340
7 Wild L Flanagan JM Genome-wide hypomethylation in cancer may be apassive consequence of transformation Biochimica et biophysica acta2010 1806(1)50-57
8 Figueroa ME Chen SC Andersson AK Phillips LA Li Y Sotzen J Kundu MDowning JR Melnick A Mullighan CG Integrated genetic and epigeneticanalysis of childhood acute lymphoblastic leukemia The Journal ofclinical investigation 2013 123(7)3099-3111
9 Rhee JK Kim K Chae H Evans J Yan P Zhang BT Gray J Spellman PHuang TH Nephew KP et al Integrated analysis of genome-wide DNAmethylation and gene expression profiles in molecular subtypes ofbreast cancer Nucleic acids research 2013 41(18)8464-8474
10 An integrated encyclopedia of DNA elements in the human genomeNature 2012 489(7414)57-74
11 Karolchik D Hinrichs AS Furey TS Roskin KM Sugnet CW Haussler DKent WJ The UCSC Table Browser data retrieval tool Nucleic acidsresearch 2004 32 Database D493-496
12 Langmead B Salzberg SL Fast gapped-read alignment with Bowtie 2Nature methods 2012 9(4)357-359
13 Li H Handsaker B Wysoker A Fennell T Ruan J Homer N Marth GAbecasis G Durbin R Genome Project Data Processing S The SequenceAlignmentMap format and SAMtools Bioinformatics 200925(16)2078-2079
14 Quinlan AR Hall IM BEDTools a flexible suite of utilities for comparinggenomic features Bioinformatics 2010 26(6)841-842
15 Siepel A Bejerano G Pedersen JS Hinrichs AS Hou M Rosenbloom KClawson H Spieth J Hillier LW Richards S Evolutionarily conservedelements in vertebrate insect worm and yeast genomes Genomeresearch 2005 15(8)1034-1050
16 Love MI Huber W Anders S Moderated estimation of fold change anddispersion for RNA-Seq data with DESeq2 bioRxiv 2014
17 Holm S A simple sequentially rejective multiple test procedureScandinavian journal of statistics 1979 65-70
18 Smyth GK Limma linear models for microarray data Bioinformatics andcomputational biology solutions using R and Bioconductor Springer 2005397-420
19 Quinlan AR Hall IM BEDTools a flexible suite of utilities for comparinggenomic features Bioinformatics 2010 26(6)841-842
20 Pages H Aboyoun P Gentleman R DebRoy S String objects representingbiological sequences and matching algorithms R package version 2009 2(2)
21 Hall MA Smith LA Feature Selection for Machine Learning Comparing aCorrelation-Based Filter Approach to the Wrapper FLAIRS Conference1999 1999 235-239
Li et al BMC Bioinformatics 2015 16(Suppl 5)S10httpwwwbiomedcentralcom1471-210516S5S10
Page 11 of 12
22 Quinlan JR Induction of decision trees Machine learning 1986 1(1)81-10623 Kononenko I Šimec E Robnik-Šikonja M Overcoming the Myopia of
Inductive Learning Algorithms with RELIEFF Applied Intelligence 19977(1)39-55
24 Kira K Rendell LA The feature selection problem Traditional methodsand a new algorithm AAAI 1992 1992 129-134
25 Hall M Frank E Holmes G Pfahringer B Reutemann P Witten IH TheWEKA data mining software an update ACM SIGKDD ExplorationsNewsletter 2009 11(1)10-18
26 Wei T Corrplot visualization of a correlation matrix R package version 02-0 Available on Comprehensive R Archive Network website 2010 [httpCRANR-project orgpackage=corrplot] (accessed 2010)
27 Sing T Sander O Beerenwinkel N Lengauer T ROCR visualizing classifierperformance in R Bioinformatics 2005 21(20)3940-3941
28 Fuks F Hurd PJ Wolf D Nan X Bird AP Kouzarides T The methyl-CpG-binding protein MeCP2 links DNA methylation to histone methylationJournal of Biological Chemistry 2003 278(6)4035-4040
29 Cedar H Bergman Y Linking DNA methylation and histone modificationpatterns and paradigms Nat Rev Genet 2009 10(5)295-304
30 Jones PA Functions of DNA methylation islands start sites gene bodiesand beyond Nat Rev Genet 2012 13(7)484-492
31 Maunakea AK Nagarajan RP Bilenky M Ballinger TJ DrsquoSouza C Fouse SDJohnson BE Hong C Nielsen C Zhao Y Conserved role of intragenic DNAmethylation in regulating alternative promoters Nature 2010466(7303)253-257
32 Bowen NJ Palmer MB Wade PA Chromosomal regulation by MeCP2structural and enzymatic considerations Cellular and molecular lifesciences CMLS 2004 61(17)2163-2167
33 Ooi SK Qiu C Bernstein E Li K Jia D Yang Z Erdjument-Bromage HTempst P Lin SP Allis CD et al DNMT3L connects unmethylated lysine 4of histone H3 to de novo methylation of DNA Nature 2007448(7154)714-717
doi1011861471-2105-16-S5-S10Cite this article as Li et al Using epigenomics data to predict geneexpression in lung cancer BMC Bioinformatics 2015 16(Suppl 5)S10
Submit your next manuscript to BioMed Centraland take full advantage of
bull Convenient online submission
bull Thorough peer review
bull No space constraints or color figure charges
bull Immediate publication on acceptance
bull Inclusion in PubMed CAS Scopus and Google Scholar
bull Research which is freely available for redistribution
Submit your manuscript at wwwbiomedcentralcomsubmit
Li et al BMC Bioinformatics 2015 16(Suppl 5)S10httpwwwbiomedcentralcom1471-210516S5S10
Page 12 of 12
Abstract
Background
Methods
Results
Conclusions
Background
Methods
Data sets
Data processing
Methylation data
Histone data
Human genome data
RNA-seq data
Feature extraction
CpG Methylation features
Histone marker modification features
Nucleotide features
Conservation features
Feature selection
Model evaluation
Software
Results
Summary of input data and features
Model selection and evaluation
Analysis of selected features
Evaluation of features by data type
Evaluation of CpG methylation features by locations relative to transcripts
Discussion
The need to build predictive models of gene expression from epigenomics data
Selected features and their relevance to gene expression
Limitations and future directions
Conclusions
Competing interests
Authorsrsquo contributions
Acknowledgements
Declarations
Authorsrsquo details
References
22 Quinlan JR Induction of decision trees Machine learning 1986 1(1)81-10623 Kononenko I Šimec E Robnik-Šikonja M Overcoming the Myopia of
Inductive Learning Algorithms with RELIEFF Applied Intelligence 19977(1)39-55
24 Kira K Rendell LA The feature selection problem Traditional methodsand a new algorithm AAAI 1992 1992 129-134
25 Hall M Frank E Holmes G Pfahringer B Reutemann P Witten IH TheWEKA data mining software an update ACM SIGKDD ExplorationsNewsletter 2009 11(1)10-18
26 Wei T Corrplot visualization of a correlation matrix R package version 02-0 Available on Comprehensive R Archive Network website 2010 [httpCRANR-project orgpackage=corrplot] (accessed 2010)
27 Sing T Sander O Beerenwinkel N Lengauer T ROCR visualizing classifierperformance in R Bioinformatics 2005 21(20)3940-3941
28 Fuks F Hurd PJ Wolf D Nan X Bird AP Kouzarides T The methyl-CpG-binding protein MeCP2 links DNA methylation to histone methylationJournal of Biological Chemistry 2003 278(6)4035-4040
29 Cedar H Bergman Y Linking DNA methylation and histone modificationpatterns and paradigms Nat Rev Genet 2009 10(5)295-304
30 Jones PA Functions of DNA methylation islands start sites gene bodiesand beyond Nat Rev Genet 2012 13(7)484-492
31 Maunakea AK Nagarajan RP Bilenky M Ballinger TJ DrsquoSouza C Fouse SDJohnson BE Hong C Nielsen C Zhao Y Conserved role of intragenic DNAmethylation in regulating alternative promoters Nature 2010466(7303)253-257
32 Bowen NJ Palmer MB Wade PA Chromosomal regulation by MeCP2structural and enzymatic considerations Cellular and molecular lifesciences CMLS 2004 61(17)2163-2167
33 Ooi SK Qiu C Bernstein E Li K Jia D Yang Z Erdjument-Bromage HTempst P Lin SP Allis CD et al DNMT3L connects unmethylated lysine 4of histone H3 to de novo methylation of DNA Nature 2007448(7154)714-717
doi1011861471-2105-16-S5-S10Cite this article as Li et al Using epigenomics data to predict geneexpression in lung cancer BMC Bioinformatics 2015 16(Suppl 5)S10
Submit your next manuscript to BioMed Centraland take full advantage of
bull Convenient online submission
bull Thorough peer review
bull No space constraints or color figure charges
bull Immediate publication on acceptance
bull Inclusion in PubMed CAS Scopus and Google Scholar
bull Research which is freely available for redistribution
Submit your manuscript at wwwbiomedcentralcomsubmit
Li et al BMC Bioinformatics 2015 16(Suppl 5)S10httpwwwbiomedcentralcom1471-210516S5S10
Page 12 of 12
Abstract
Background
Methods
Results
Conclusions
Background
Methods
Data sets
Data processing
Methylation data
Histone data
Human genome data
RNA-seq data
Feature extraction
CpG Methylation features
Histone marker modification features
Nucleotide features
Conservation features
Feature selection
Model evaluation
Software
Results
Summary of input data and features
Model selection and evaluation
Analysis of selected features
Evaluation of features by data type
Evaluation of CpG methylation features by locations relative to transcripts
Discussion
The need to build predictive models of gene expression from epigenomics data
Selected features and their relevance to gene expression
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