BIOL6900 Bioinformatics Chapter 8 Bioinformatics approaches to ribonucleic acid

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BIOL6900 BioinformaticsChapter 8

Bioinformatics approaches to ribonucleic acid

Outline of upcoming lectures

The first part of the course covered sequence analysis,including BLAST (Chapters 1-7).

We begin the next part of the course: functional genomics (Chapters 8-12).

We will study how DNA is transcribed to RNA (i.e. gene expression), and we will discuss microarrays. Then we will study proteins.

We will conclude with a survey of genomes (Ch. 13-20).

32e Fig. 8.1

Types of RNAs

• tRNA, rRNA - together 95% of total RNA

• mRNA,

• Other non-coding RNA:

small nuclear RNA (snRNA);

small nucleolar RNA (snoRNA);

microRNA (~22 nt); short interfering RNA (siRNA)

52e Fig. 8.3

The Rfam family includes alignments and descriptions of RNA families

http://rfam.sanger.ac.uk/

62e Fig. 8.4

Summary of non-coding RNA families in Rfam database that are assigned to the long arm of human chromosome 21.

72e Fig. 8.5

Figure 8.5 Identification of tRNAs using tRNAscan-SE server

82e Fig. 8.5

92e Fig. 8.6

Vienna RNA package

102e Fig. 8.7

Figure 8.7Structure of a eukaryotic ribosomal DNA repeat unit

112e Fig. 8.8

122e Fig. 8.8

132e Fig. 8.9

• by region (e.g. brain versus kidney)

• in development (e.g. fetal versus adult tissue)

• in dynamic response to environmental signals

(e.g. immediate-early response genes)

• in disease states

• by gene activity

Gene expression is regulated in several basic ways

Organism Gene expression changes measured...

bacteria

invertebrates

rodents

human In m

Fig. 6.1Page 158

DNA RNA

phenotypeprotein

DNA RNA

protein DNA RNA

protein

UniGene

microarray

Fig. 6.2Page 159

(Serial Analysis of Gene Expression)

DNA RNA

phenotypeprotein

[1] Transcription[2] RNA processing (splicing)[3] RNA export[4] RNA surveillance

19Fig. 6.3Page 161

exon 1 exon 2 exon 3intron intron

transcription

RNA splicing (remove introns)

polyadenylation

Export to cytoplasm

AAAAA 3’5’

5’ 3’5’3’

202e Fig. 8.10

Relationship of mRNA to genomic DNA for RBP4

~2e Fig. 8.11

222e Fig. 8.11

232e Fig. 8.12

242e Fig. 8.12

exon 2

exon 3

exon 1

252e Fig. 8.12

exon 2

exon 3

exon 1

exon 3

exon 1

query 1: genomic contigNT_037887, nucleotides162875-163708

query 2: cDNA NM_000517

intron

Analysis of gene expression in cDNA libraries

A fundamental approach to studying gene expressionis through cDNA libraries.

• Isolate RNA (always from a specific organism, region, and time point)

• Convert RNA to complementary DNA

• Subclone into a vector

• Sequence the cDNA inserts. These are expressed sequence tags (ESTs)

2e ~Fig. 8.13

vector

insert

UniGene: unique genes via ESTs

• Find UniGene at NCBI: from the home page click All databases (on the top bar) then UniGene, or go to: www.ncbi.nlm.nih.gov/UniGene

• UniGene clusters contain many ESTs

• UniGene data come from many cDNA libraries. Thus, when you look up a gene in UniGene you get information on its abundance and its regional distribution.

Cluster sizes in UniGene

This is a gene with1 EST associated;the cluster size is 1

& Fig. 2.3,Page 23

Cluster sizes in UniGene

This is a gene with10 ESTs associated;the cluster size is 10

Cluster sizes in UniGene (human)

Cluster size (ESTs) Number of clusters1 42,8002 6,5003-4 6,5005-8 5,4009-16 4,10017-32 3,300

500-1000 2,1282000-4000 2338000-16,000 2116,000-30,000 8

UniGene build 194, 8/06

Ten largest human UniGene clusters

Cluster size Gene22,925 eukary. translation EF (Hs. 522463)22,320 eukary. translation EF (Hs. 4395522)16,562 actin, gamma 1 (Hs.514581)16,309 GAPDH (Hs.169476)16,231 actin, beta (Hs.520640)11,076 ribosomal prot. L3 (Hs.119598)10,517 dehydrin (Hs.524390)

10,087 enolase 1 (alpha)(Hs.517145)

9,973 ferritin (Hs.433670)8,966 metastasis associated (Hs.187199)

UniGene build 186, 9/05Table 6.2Page 165

Why ribosomal transcripts are abundantin UniGene

The major types of RNA are:

ribosomal RNA rRNA (~85%)transfer RNA tRNA (~15%)messenger RNA mRNA (~3%)

noncoding RNA ncRNA (<1%)small nuclear RNA snRNAsmall nucleolar RNA snoRNAsmall interfering RNA siRNA

There are three distinctions of similarity in UniGene:

1. "Highly similar to" means >90% in the aligned region.

2. "Moderately Similar to" means 70-90% similar in the aligned region.

3. "Weakly similar to" means <70% similar in the aligned region.

UniGene clusters are often “similar to” a known gene

Species Canis familiaris (dog) Helianthus annuus (sunflower)Salmo salar (Atlantic salmon)Bombyx mori (domestic silkworm)Apis mellifera (honey bee)Lotus corniculatus (Birdsfoot trefoil)Physcomitrella patens (physco. moss) Lactuca sativa (garden lettuce) Malus x domestica (Apple)Hydra magnipapillata Populus tremula x

Populus tremuloides (aspen) Ovis aries (sheep)

UniGene includes 74 species (as of Aug. 2006), all with many ESTs available. Recent entries include:

Currently: ~130 species

Identifying protein-coding genes in genomic DNA remains a tremendous challenge. Genes can be predicted “ab initio” (by analyzing genomic DNA for the features of start and stop sites, exons/intron structures, regulatory regions etc.). When EST data are coupled with gene prediction, the accuracy soars.

Thus all ongoing genome sequencing projects include a major component of large-scale EST sequencing. Typically, this is done at different developmental stages (e.g. embryo versus adult), regions (e.g. brain versus gut), and physiological states (e.g. mosquitoes having fed on blood versus sucrose). EST data are deposited in UniGene. (dbEST)

The significance of UniGene’s continued growth

Digital Differential Display (DDD) in UniGene

• UniGene clusters contain many ESTs

• UniGene data come from many cDNA libraries

• Libraries can be compared electronically

38Fig. 6.6Page 166

39Fig. 6.6Page 166

40Fig. 6.6Page 166

UniGene brainlibraries

UniGene lunglibraries

43Fig. 6.7Page 167

44Fig. 6.7Page 167

n-sec1 up-regulated in brain

CamKII up-regulated

in brain

surfactant up-regulated in lung

fraction of sequences within the pool that mapped to the cluster shown

DDD at UniGene

Question: are there individual RNA transcripts that are differentially present in a comparison of EST libraries?

Approach to estimating statistical significance: Fisher’s exact test.

Pages 165

482e Fig. 8.14

492e Fig. 8.14

DDD at UniGene

Fisher’s exact test is a nonparametric method.

• It does not assume a normal distribution of the observations• It is easy to calculate• It often has less statistical power than parametric tests (such as a t-test)• For nonparametric methods, observations are typically arranged in an array with ranks assigned from 1 to n.

DDD at UniGene

Fisher’s exact test is related to a chi square (2) test, but is appropriate for small sample sizes.

A 2 test is applied to row x column (rc) contingency tables

Determine whether the observed (O) frequencies of occurrence of a categorical value differ significantly from the expected (E) frequency of occurrence. Is O – E larger than expected by chance? rc

2 = i=1

(Oi – Ei)2

Fisher’s exact test provides a p value

Digital differential display (DDD) results in UniGeneare assessed for significance using Fisher’s exact testto generate a p value.

The null hypothesis (that gene 1 is not differentiallyregulated in a comparison of two libraries) is rejectedwhen p is < 0.05/G (where G = the number of UniGeneclusters analyzed).

Pages 165

NA! NB! c! C!

(NA + NB)! g1A! g1B! (NA – g1A)!(NB – g1B)!

Pool A

Pool B

Gene 1 All other genes total

g1A NA-g1A

c = g1A + g1B

NB-g1Bg1B

C = (NA-g1A) + (NB-g1B)

Fisher’s Exact Test: deriving a p value

Table 6-3Page 167

Pitfalls in interpreting cDNA library data

• bias in library construction• variable depth of sequencing• library normalization• error rate in sequencing• contamination (chimeric sequences)

Pages 166-168

55Fig. 6.8p. 168-169

http://mgc.nci.nih.gov

Updated 8/06

Serial analysis of gene expression (SAGE)

• 9 to 11 base “tags” correspond to genes

• measure of gene expression in different biological samples

• SAGE tags can be compared electronically

Tag 1Tag 2Tag n

Cluster 1Cluster 2Cluster 3

Cluster 1

SAGE tags are mapped to UniGene clusters

58Fig. 6.10Page 171

59Fig. 6.11Page 171

61Fig. 6.12Page 171

62Fig. 6.13Page 173

63Fig. 6.14Page 174

64Fig. 6.15Page 175

65Fig. 6.15Page 175

Microarrays: tools for gene expression

A microarray is a solid support (such as a membraneor glass microscope slide) on which DNA of knownsequence is deposited in a grid-like array.

The most common form of microarray is used to measure gene expression. RNA is isolated from matched samples of interest. The RNA is typically converted to cDNA, labeled with fluorescence (or radioactivity), then hybridized to microarrays in order to measure the expression levelsof thousands of genes.

• Wildtype versus mutant

• Cultured cells +/- drug

• Physiological states (hibernation, cell polarity formation)

• Normal versus diseased tissue (cancer, autism)

Questions addressed using microarrays

• metazoans: human, mouse, rat, worm, insect

• fungi: yeast

• plants: Arabidopsis

• many other: e.g. bacteria, viruses

Organisms represented on microarrays

Fast Data on >20,000 genes in several weeks

Comprehensive Entire yeast or mouse genome on a chip

Flexible • As more genomes are sequenced, more arrays can be made. • Custom arrays can be made

to represent genes of interest

Easy Submit RNA samples to a core facility

Cheap? Chip representing 20,000 genes for $350; robotic spotter/scanner cost $100,000

Advantages of microarray experiments

Table 6-4Page 175

Cost Some researchers can’t afford to doappropriate controls, replicates

RNA The final product of gene expression is proteinsignificance (see pages 174-176 for references)

Quality Impossible to assess elements on array surfacecontrol Artifacts with image analysis

Artifacts with data analysis

Disadvantages of microarray experiments

Table 6-5Page 176

purify RNA, label

hybridize,wash, image

Biological insight

Sampleacquisition

Dataacquisition

Data analysis

Data confirmation

data storage

experimentaldesign

Fig. 6.16Page 176

Stage 1: Experimental design

[1] Biological samples: technical and biological replicates

[2] RNA extraction, conversion, labeling, hybridization

[3] Arrangement of array elements on a surface

Sample 1 Sample 2 Sample 3

Fig. 6.17Page 177

Samples 1,2 Samples 1,3 Samples 2,3

Sample 1, pool Sample 2, poolSamples 2,1:switch dyes

2e Fig. 8.18

Stage 2: RNA and probe preparation

For Affymetrix chips, need total RNA (about 10 ug)

Confirm purity by running agarose gel

Measure a260/a280 to confirm purity, quantity

Stage 3: hybridization to DNA arrays

The array consists of cDNA or oligonucleotides

Oligonucleotides can be deposited by photolithography

The sample is converted to cRNA or cDNA

Microarrays: array surface

Southern et al. (1999) Nature Genetics, microarray supplement 2e Fig. 8.19

Stage 4: Image analysis

RNA expression levels are quantitated

Fluorescence intensity is measured with a scanner,or radioactivity with a phosphorimager

Control

Differential Gene Expression on a cDNA Microarray

B Crystallin is over-expressed in Rett Syndrome

2e Fig. 8.20

802e Fig. 8.21

81Fig. 8.21

82Fig. 8.21

83Fig. 8.21

Stage 5: Microarray data analysis

• How can arrays be compared? • Which genes are regulated?• Are differences authentic?• What are the criteria for statistical significance?• Are there meaningful patterns in the data (such as groups)?

preprocessing

inferential statistics

exploratory statistics

preprocessing

inferential statistics

exploratory statistics

t-tests

global normalizationlocal normalizationscatter plots

clustering

Matrix of genes versus samples

T-test: • for each gene, calculate the mean expression value in control (C) and experimental (E) samples• Null hypothesis: the mean C and E values are the same• Use a t-test to see whether the null hypothesis can be rejected with a particular cutoff value (e.g. p < 0.05)• Correct the p value for multiple comparisons (e.g. if you measure expression values in 10,000 genes, then 5% (500 genes) might vary by chance alone).

small p value; ratio large

small p value; ratio unimpressive

Perform a t-test in Excel to compare the mean of two groups,and to compare fold change to probability values

disease vs normal

t-test to determine statistical significance

difference between mean of disease and normalt statistic = variation due to error

Tissue type

ANOVA partitions total data variability

variation between DS and normalF ratio = variation due to error

Before partitioning After partitioning

Subjectdisease vs normal

disease vs normal

Matrix of genes versus samples

Metric (define distance)

supervised,unsupervised

analyses

clusteringtrees(hierarchical,k-means)

self-organizing

principalcomponentsanalysis

Stage 5: MIAME

In an effort to standardize microarray data presentationand analysis, Alvis Brazma and colleagues at 17institutions introduced Minimum Information About aMicroarray Experiment (MIAME). The MIAME framework standardizes six areas of information:• experimental design• microarray design• sample preparation• hybridization procedures• image analysis• controls for normalization

Visit http://www.mged.org

Stage 6: Biological confirmation

Microarray experiments can be thought of as“hypothesis-generating” experiments.

The differential up- or down-regulation of specificgenes can be measured using independent assayssuch as

-- Northern blots-- polymerase chain reaction (RT-PCR)-- in situ hybridization

Stage 7: Microarray databases

There are two main repositories:

Gene expression omnibus (GEO) at NCBI

ArrayExpress at the European Bioinformatics Institute (EBI)

See the URLs on page 184

Gene expression omnibus (GEO)

NCBI repository for gene expression data

http://www.dnachip.org

• Stanford Microarray Database http://www.dnachip.org

• links at http://pevsnerlab.kennedykrieger.org/

Microarrays: web resources

BIOL6900 Bioinformatics Chapter 8 Bioinformatics approaches to ribonucleic acid

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