RNA-seq analysis

transcript

RNA-seq analysisDr.Tech. Daniel Nicorici

FIMM – Institute for Molecular Medicine Finland

CSC - June 2, 2010

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Outline

› RNA sequencing overview

› Finding fusion genes

› Alternative splicing

› Conclusions

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RNA-seq

› high-throughput sequencing technology for sequencing RNAs (actually cDNAs which contain the RNAs' content)

› invaluable tool for study of diseases like cancer

› allows researchers to obtain information like: gene/transcript/exon expressions alternative splicing gene fusions post-transcriptional mutations single nucleotide variations …

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RNA-seq - cont’d

› It reduces greatly the variability between experiments compared to other established measurement technologies like microarrays, exon arrays, etc.

› Due to the small size of the read (cDNA is fragmented before sequencing) the bioinformatics analysis is challenging, e.g.

de novo assembly aligning of sequenced reads computation of gene/transcript/exon expressions

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Reads in RNA-seq

Fig. 1 – Adaptor and reads in RNA-seq

adaptoradaptor

This is sequenced (short reads)

5’ end 3’ end

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Reads in RNA-seq – cont’d

Exon A Exon B

Exon A Exon Btranscript

chromosome

Exon C Exon D

Fig. 2 – Reads’ mappings at chromosome and transcript level

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Why RNA-seq?

RNA-seq

cDNA array

SNPs array

Exon array(alternative splicing)

~1000€/sample

- exon/transcripts expressions- gene expressions- alternative splicing events- SNPs- fusion genes- ...

Exon array(fusion genes)

~700€/sample

~400€/sample

~600€/sample

~700€/sample

Fig. 3 – RNA-seq vs array technologies

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General steps of RNA-seq analysis

1. Filtering of short reads

2. Aligning the reads against a reference

3. Computationaly analysing of reads’ alignments1. compute the gene/transcript/exon expressions

2. find new/known alternative splicing events

3. find new/known fusion genes

4. find new/known SNPs

4. Visualization

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Examples of RNA-seq visualization

Fig. 4 – Visualization using MapView

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Examples of RNA-seq visualization – cont’d

Fig. 5 – Coverage plot

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Coverage plot for gene ERBB2 in breast cancer

Coverage plot for gene ERBB2 in normal breast

130.71

Fig. 6 – Coverage plots visualization 12

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Fig. 7 – Visualization of reads’ mappings using the UCSC browser

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Fig. 8 – Visualization of coverages using UCSC browser

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Fig. 9 – ”Gel-like” visualization of coverages using UCSC browser

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Fig. 10 – Histogram of distances between the paired-end reads

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Fig. 11 – Visualization of candidate fusion genes

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Finding fusion genes

Steps:

1. Reads filtering (quality, B’s, etc.)

2. Align all reads on genome

3. Aligning against the transcriptome all the reads which map uniquely on genome, or do not map on genome

4. Find the candiates fusion-genes by looking for paired-end reads which map simultaneusly on two different transcripts from two different genes

5. Find the fusion junction (e.g. generating exon-exon combinations and find on which one the reads are aligning)

6. Filtering of candidate fusion-genes

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Reads in RNA-seq – cont’d

Exon A Exon B

Exon A Exon Btranscript

chromosome

Exon C Exon D

Fig. 2 – Reads’ mappings at chromosome and transcript level

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Finding fusion genes – cont’d

› RNA-seq data for the leukemia K562 cell line [1] Philadelphia chromosome with the known BCR-ABL fusion genes ~15 000 candidate fusion-genes found ~85% candidate fusion-genes are known paralogs or have no protein

product!!! 15 candidate fusion-genes are found after additional filtering of candidate

fusion-genes where the known BCR-ABL is number one candidate

› Filtering of candidate fusion-genes is highly necessary in order to reduce the large number of candidate fusion-genes (from ten of thousands to tens)!!!

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Alternative splicing

› process by which the gene’s exons are pieced together in multiple ways forming mRNA during the RNA splicing.

› there is a large body of evidence showing the links between alternative splicing and different diseases like cancer

› Shannon’s entropy from information theory has been used previously for finding the imbalance in transcript expression [2,3]

› Jensen-Shannon divergence has been used in quantifying the relative changes in expression of transcripts [4]

› MDL [5] can be used for measuring the relative changes in expression of transcripts too

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Alternative splicing – cont’d

Steps:

1. Reads filtering (quality, B’s, etc.)

2. Align all reads on genome

3. Aligning against the transcriptome all the reads which map uniquely on genome, or do not map on genome

4. Compute (normalized) transcript expressions (e.g. RPKM)

5. Repeat steps 1-4 for all samples

6. Find relative-changes/imbalances between their transcript expressions of the same gene across the group of samples

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Transcript of gene ”G” Sample ”A” Sample ”B”

Transcript 1 3 1

Transcript 2 5 7

Transcript 3 4 2

Transcript 4 4 6

Transcript 5 2 3

Table 1 – Example of a gene with its five transcripts

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)(log18

3log3)( 5,182 bitsCAL

)(log19

1log1)( 5,192 bitsCBL

)(log37

10log10

12log12

4log4)( 5,372 bitsCBAL

imbalanceistherethenBLALBALIf )()()(

0,,,,54321

5, ,,,,

iiiiiniiiii

nCwhere

› Computing the imbalance of transcript expression for example from Table 1 using MDL method [5]:

› MDL’s advantage: the criteria for deciding between balanced/imbalanced is built-in

Transcript of gene ”G” Sample ”A”

Sample ”B”

Transcript 1 3 1

Transcript 2 5 7

Transcript 3 4 2

Transcript 4 4 6

Transcript 5 2 3

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› only the transcripts which are validated (e.g. there are reads which map only on the given transcript [3]) are used for finding the imbalances

› for example in a prostate cancer control sample versus treated sample are found ~3500 alternatively spliced genes

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Conclusions

› RNA-seq data analysis: is computational intensive (when compared to, for example, microarray

analysis) needs very good filtering criteria, which are based on biology mathematics, in

order to improve the quality of the results (i.e. low number of false positives) there is not only one established way of doing it many tools used for analysis, e.g. aligners, samtools, etc., are still work in

progress

› Visualization: multiple facets, i.e. read coverage, fusion genes, etc. depends on the user profile:

1. biologist/medical doctor

2. bioinformatician

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References

1. Berger M. et al., Integrative analysis of the melanoma transcriptome, Genome Research, Feb. 2010.

2. Ritchie W. et al., Entropy measures quantify global splicing disorders in cancer, PLOS Computational Biology, vol. 4, March 2008.

3. Gan Q. et al., Dynamic regulation of alternative splicing and chromatin structure in Drosophila gonads revealed by RNA-seq, Cell Research, May 2010.

4. Trapnell C. et al., Transcript assembly and quantification by RNA-Seq reveals unannotated transcripts and isoform switching during cell differentiation, Nature Biotechnology, vol. 28, May 2010.

5. P. Grunwald, “Minimum description length principle tutorial”, in Advances in Minimum Description Length: Theory and Applications, P. Grunwald, I.J. Myung, and M. Pitt, Eds., pp. 22-79. MIT Press, Cambridge, 2005.

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Acknowledgements

› Olli Kallioniemi

› Janna Saarela

› Henrik Edgren

› Astrid Murumägi

› Sara Kangaspeska

› Pekka Ellonen

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› Thank you!

RNA-seq analysis

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