Bioinformatics approaches to gene expression

Introduction to BioinformaticsSeptember 18, 2006

J. Pevsner (pevsner@kennedykrieger.org)

Copyright notice

Many of the images in this powerpoint presentationare from Bioinformatics and Functional Genomicsby Jonathan Pevsner (ISBN 0-471-21004-8). Copyright © 2003 by John Wiley & Sons, Inc.

These images and materials may not be usedwithout permission from the publisher. We welcomeinstructors to use these powerpoints for educationalpurposes, but please acknowledge the source.

The book has a homepage at http://www.bioinfbook.orgincluding hyperlinks to the book chapters.

Outline of upcoming lectures

The first part of the course covered sequence analysis,including BLAST (Chapters 1-5).

Today we begin the next part of the course: functional genomics (Chapters 6-11).

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 perform multiple sequence alignments,then visualize those alignments in phylogenetic trees.

A new course (2nd quarter) will cover genomes (Ch. 12-18).

• 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

Page 157

Organism Gene expression changes measured...

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Fig. 6.1Page 158

DNA RNA

cDNA

phenotypeprotein

Page 159

DNA RNA

cDNA

protein DNA RNA

cDNA

protein

UniGeneSAGE

microarrayFig. 6.2Page 159

DNA RNA

cDNA

phenotypeprotein

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

Page 160

Fig. 6.3Page 161

exon 1 exon 2 exon 3intron intron

transcription

RNA splicing (remove introns)

polyadenylation

Export to cytoplasm

AAAAA 3’5’

5’

5’

5’ 3’5’3’

3’

3’

Relationship of mRNA to genomic DNA for RBP4

Fig. 6.4Page 162

Analysis of gene expression in cDNA libraries

A fundamental approach to studying gene expressionis through cDNA libraries.

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

• Convert RNA to complementary DNA

• Subclone into a vector

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

Page 162-163

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.

Page 164

Cluster sizes in UniGene

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

Page 164& Fig. 2.3,Page 23

Cluster sizes in UniGene

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

Page 164

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 2,128≈2000-4000 233≈8000-16,000 21≈16,000-30,000 8

UniGene build 194, 8/06

Cluster sizes in UniGene (human)

Cluster size Number of clusters1 ≈10,4002 7,1003-4 6,8005-8 5,3009-16 3,80017-32 3,100

≈500-1000 1,500≈2000-4000 130≈8000-16,000 12≈16,000-30,000 3

UniGene build 186, 9/05 Page 164

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

Ten largest human UniGene clusters

Cluster size Gene29,533 eukary. translation EF (Hs.439552)16,837 GAPDH (Hs.169476)15,486 mRNA, placental villi (Hs.406283)15,453 actin, gamma 1 (Hs.14376)12,905 actin, beta (Hs.426930)10,760 ribosomal prot. S2 (Hs.498569)10,680 ribosomal prot. L3 (Hs.119598)10,339 tubulin, alpha (Hs.446608)9,763 ribosomal protein P0 (Hs.443796)9,573 ferritin (Hs.433670)

UniGene build 172, 8/04Table 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.

Page 164

UniGene clusters are often “similar to” a known gene

SpeciesCanis 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 magnipapillataPopulus tremula x

Populus tremuloides (aspen) Ovis aries (sheep)

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

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.

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

Page 165

Fig. 6.6Page 166

Fig. 6.6Page 166

Fig. 6.6Page 166

UniGene brainlibraries

UniGene lunglibraries

Fig. 6.7Page 167

Fig. 6.7Page 167

n-sec1 up-regulated in brain

CamKII up-regulated

in brain

surfactant up-regulated in lung

Page 167

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

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 (χ2) test, but is appropriate for small sample sizes.

A χ2 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?

rcχ2 = Σ

i=1

(Oi – Ei)2

Ei

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.

p =

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

total

Gene 1 All other genes total

NA

NB

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

Fig. 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 differentbiological samples

• SAGE tags can be compared electronically

Page 169

Tag 1

Tag 1Tag 2Tag n

Cluster 1Cluster 2Cluster 3

Cluster 1

SAGE tags are mapped to UniGene clusters

Page 169

Fig. 6.10Page 171

Fig. 6.11Page 171

Fig. 6.12Page 171

Fig. 6.13Page 173

Fig. 6.14Page 174

Fig. 6.15Page 175

Fig. 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.

Page 173

• Wildtype versus mutant

• Cultured cells +/- drug

• Physiological states (hibernation, cell polarity formation)

• Normal versus diseased tissue (cancer, autism)

Questions addressed using microarrays

Page 173

• 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

Page 177

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

Fig. 6.17Page 177

Stage 2: RNA and probe preparation

Page 178

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

Confirm purity by running agarose gel

Measure a260/a280 to confirm purity, quantity

School of Medicine basic sciences Affymetrix corehttp://www.hopkinsmedicine.org/IBBS/HiT/cores.html

Johns Hopkins University NIDDK Gene Profiling Centerhttp://www.hopkinsmedicine.org/nephrology/microarray/

The Hopkins Expressionistshttp://astor.som.jhmi.edu/hex/

Johns Hopkins School of Public Health corehttp://www.jhsph.edu/Dept/EHS/Centers/UrbanEnviroHlth/FacilityB

Stage 3: hybridization to DNA arrays

Page 178-179

The array consists of cDNA or oligonucleotides

Oligonucleotides can be deposited by photolithography

The sample is converted to cRNA or cDNA

Source: Nature Genetics (1999) microarray supplement

Microarrays: array surface

Fig. 6.18Page 179Southern et al. (1999) Nature Genetics, microarray supplement

Microarrays: robotic spotters

See Nature Genetics (1999) microarray supplement

Stage 4: Image analysis

Page 180

RNA expression levels are quantitated

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

Rett

Control

Differential Gene Expression on a cDNA Microarray

α B Crystallin is over-expressed in Rett Syndrome

Fig. 6.19Page 180

Fig. 6.20Page 181

Fig. 6.20Page 181

Fig. 6.20Page 181

Fig. 6.20Page 181

Stage 5: Microarray data analysis

Page 180

This is the subject of Wednesday’s class

• 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

Page 180

Stage 5: Microarray data analysis

preprocessing

inferential statistics

exploratory statistics

t-tests

global normalizationlocal normalizationscatter plots

clustering

Page 180

Stage 5: Microarray data analysis

Matrix of genes versus samples

T-test: • for each gene, calculate the mean expression valuein 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 berejected with a particular cutoff value (e.g. p < 0.05)

• Correct the p value for multiple comparisons (e.g. if youmeasure 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

diseasevs normal

Error

t-test to determine statistical significance

difference between mean of disease and normalt statistic =

variation due to error

Error

Error

Tissue type

ANOVA partitions total data variability

variation between DS and normalF ratio =

variation due to error

Before partitioning After partitioning

Subjectdiseasevs normal

diseasevs normal

Matrix of genes versus samples

Metric (define distance)

supervised,unsupervised

analyses

clusteringtrees(hierarchical,k-means)

self-organizing

maps

principalcomponentsanalysis

Page 180

Stage 5: MIAME

Page 182

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

Page 182

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

Page 182

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

Fig. 6.21Page 183

• Stanford Microarray Databasehttp://www.dnachip.org

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

Microarrays: web resources

Database Referencing of Array Genes Online(DRAGON)

Database Referencing of Array Genes Online(DRAGON)

Credit: Christopher BoutonCarlo ColantuoniGeorge Henry

Paste accession numbers into DRAGON here

DRAGON relates genesto KEGG pathways

For Friday’s lab, try UniGene’s digital differential displaytool and try digital SAGE.

Wednesday we will discuss microarray data analysis,and that will be the main focus of Friday’s lab.

Next this week