Comparative Genomics

Ross Hardison, Penn State University

Major collaborators: Webb Miller, Francesca Chiaromonte, Laura Elnitski, David King, et al.,

PSUJames Taylor: Courant Institute, New York

University David Haussler, Jim Kent, Univ. California at

Santa CruzIvan Ovcharenko, Lawrence Livermore National LabPSU Nov. 28, 2006

Major goals of comparative genomics

• Identify all DNA sequences in a genome that are functional– Selection to preserve function– Adaptive selection

• Determine the biological role of each functional sequence

• Elucidate the evolutionary history of each type of sequence

• Provide bioinformatic tools so that anyone can easily incorporate insights from comparative genomics into their research

Three major classes of evolution

• Neutral evolution– Acts on DNA with no function– Genetic drift allows some random mutations to become

fixed in a population

• Purifying (negative) selection– Acts on DNA with a conserved function– Signature: Rate of change is significantly slower than

that of neutral DNA– Sequences with a common function in the species

examined are under purifying (negative) selection

• Darwinian (positive) selection– Acts on DNA in which changes benefit an organism– Signature: Rate of change is significantly faster than

that of neutral DNA

Ideal case for interpretation

Similarity

Position along chromosome

Neutral DNA

Negative selection(purifying)

Positive selection(adaptive)

Exonic segments coding for regions of a polypeptide with common function in two species.

Exonic segments coding for regions of a polypeptide in which change is beneficial to one of the two species.

Taxonomic distribution of homologs of mouse proteins

Waterston et al.

Conservation in different parts of genes

Waterston et al, Mouse Genome, Nature

Average percent identity (black) or percent aligned (blue) for 10,000 orthologous genes

Levels of conservation (Human vs Mouse) in different types of proteins

Black: all orthologous proteins (Hum-mouse)12,845 1:1 gene pairs

Red: proteins with recognized domainsGray: proteins without recognized domains

Black: Nuclear proteinsRed: Cytoplasmic proteinsGray: Extracellular proteins; positive,

diversifying selection

KA= rate of nonsynonymous substitutionsKS= rate of synonymous substitutions

Waterston et al. Nature 2002

Rat-specific gene expansions

• Genes that have expanded in number in rats are enriched in– Immune function/ antigen recognition

• immunoglobulins, T-cell receptor alpha– Detoxification

• cytochrome P450– Reproduction

• alpha2u-globulin

– Olfaction and odorant detection• Olfactory receptors

• Also are rapidly evolving• Segmental duplications are enriched for the same

genes

Rat Genome SPC 2004 Nature

Adaptive remodeling of gene clusters

Figure 13 Adaptive remodeling of genomes and genes. a, Orthologous regions of rat, human and mouse genomes encoding pheromone-carrier proteins of the lipocalin family (a2u-globulins in rat and major urinary proteins in mouse) shown in brown. Zfp37-like zinc finger genes are shown in blue. Filled arrows represent likely genes, whereas striped arrows represent likely pseudogenes. Gene expansions are bracketed. Arrowhead orientation represents transcriptional direction. Flanking genes 1 and 2 are TSCOT and CTR1, respectively.

Rat Genome SPC 2004 Nature

DCODE.org Comparative Genomics: Align your own sequences

blastZ multiZ and TBA

zPicture interface for aligning sequences

Automated extraction of sequence and annotation

Pre-computed alignment of genomes

• blastZ for pairwise alignments• multiZ for multiple alignment

– Human, chimp, mouse, rat, chicken, dog– Also multiple fly, worm, yeast genomes– Organize local alignments: chains and

nets

• All against all comparisons– High sensitivity and specificity

• Computer cluster at UC Santa Cruz – 1024 cpus Pentium III – Job takes about half a day

• Results available at– UCSC Genome Browser

http://genome.ucsc.edu– Galaxy server: http://www.bx.psu.edu

Webb Miller

David Haussler

Jim Kent

Schwartz et al., 2003, blastZ, Genome ResearchBlanchette et al., 2004, TBA and multiZ, Genome Research

Net

Genome-wide local alignment chains

Mouse

blastZ: Each segment of human is given the opportunity to align with all mouse sequences.

Human: 2.9 Gb assembly. Mask interspersed repeats, break into 300 segments of 10 Mb.

Human

Run blastZ in parallel for all human segments. Collect all local alignments above threshold.

Organize local alignments into a set of chains based on position in assembly and orientation.

Level 1 chainLevel 2 chain

Comparative genomics to find functional sequences

Genome size

2,900

2,400

2,500

1,200

Human

Mouse Rat

All mammals1000 Mbp

Identify functional sequences: ~ 145 Mbp

million base pairs(Mbp)

Find common sequencesblastZ, multiZ

Also birds: 72Mb

Papers in Nature from mouse and rat and chicken genome consortia, 2002, 2004

Use measures of alignment quality to discriminate functional from

nonfunctional DNA• Compute a conservation score adjusted for the local neutral rate

• Score S for a 50 bp region R is the normalized fraction of aligned bases that are identical – Subtract mean for aligned ancestral repeats in the surrounding region

– Divide by standard deviation

p = fraction of aligned sites in R that areidentical between human and mouse

= average fraction of aligned sites that are identical in aligned ancestral repeats inthe surrounding region

n = number of aligned sites in RWaterston et al., Nature

Decomposition of conservation score into neutral and likely-selected

portions

Neutral DNA (ARs)All DNALikely selected DNAAt least 5-6%

S is the conservation score adjusted for variation in the local substitution rate.The frequency of the S score for all 50bp windows in the human genome is shown.From the distribution of S scores in ancestral repeats (mostly neutral DNA), can compute a probability that a given alignment could result from locally adjusted neutral rate.

Waterston et al., Nature

DNA sequences of mammalian genomes

• Human: 2.9 billion bp, “finished”– High quality, comprehensive sequence, very few gaps

• Mouse, rat, dog, oppossum, chicken, frog etc. etc etc.• About 40% of the human genome aligns with mouse

– This is conserved, but not all is under selection.

• About 5-6% of the human genome is under purifying selection since the rodent-primate divergence

• About 1.2% codes for protein• The 4 to 5% of the human genome that is under

selection but does not code for protein should have:– Regulatory sequences– Non-protein coding genes (UTRs and noncoding RNAs)– Other important sequences

Conservation score S

in different types of regions

Red: Ancestral repeats (mostly neutral)Blue: First class in labelGreen: Second class in label

Waterston et al., Nature

Leverage many

species to improve accuracy

and resolution of signals

for constraint

ENCODE multi-species alignment groupMargulies et al., 2007

Coverage of human by alignments with other vertebrates ranges from 1% to 91%

Human

0 20 40 60 80 100

Fugu

Tetraodon

Zebrafish

Frog

Chicken

Platypus

Opossum

Cow

Dog

Rat

Mouse

Chimp

Percent of human aligning with second species

5.4

9192

310

360

450

173

Millions ofyears

220

5%

0

10

20

30

40

50

60

70

80

90

100

0 100 200 300 400 500

Time of divergence from common ancestor to human, Myr ago

Distinctive divergence rates for different types of functional DNA

sequences

0

10

20

30

40

50

60

70

80

90

100

0 100 200 300 400 500

Time of divergence from common ancestor to human, Myr ago

GenomeCoding exonsUltraconserved (HM)Log. (Genome)

Large divergence in cis-regulatory modules from opossum to platypus

0

10

20

30

40

50

60

70

80

90

100

0 100 200 300 400 500

Time of divergence from common ancestor to human, Myr ago

GenomeKnown regulatory regionsCpG islandsFunctional promotersCoding exonsUltraconserved (HM)

cis-Regulatory modules conserved from human to fish

310

450

91

173

Millions ofyears

• About 20% of CRMs• Tend to regulate genes

whose products control transcription and development

cis-Regulatory modules conserved in eutherian mammals and marsupials

310

450

91

173

Millions ofyears

• Human-marsupial alignments capture about 60% of CRMs– Tend to occur close to genes

involved in aminoglycan synthesis, organelle biosynthesis

• Human-mouse alignments capture about 87% of CRMs– Tend to occur close to genes

involved in apoptosis, steroid hormone receptors, etc.

• Within aligned noncoding DNA of eutherians, need to distinguish constrained DNA (purifying selection) from neutral DNA.

Score multi-species alignments for features associated with function

• Multiple alignment scores – Margulies et al. (2003) Genome Research 13: 2505-2518

– Binomial, parsimony

• PhastCons – Siepel et al. (2005) Genome Research 15:1034-1050

– Phylogenetic Hidden Markov Model– Posterior probability that a site is among the most highly conserved sites

• GERP– Cooper et al. (2005) Genome Research 15:901-913– Genomic Evolutionary Rate Profiling– Measures constraint as rejected substitutions = nucleotide substitution deficits

phastCons: Likelihood of being constrained

Siepel et al. (2005) Genome Research 15:1034-1050

• Phylogenetic Hidden Markov Model

• Posterior probability that a site is among the most highly conserved sites

• Allows for variation in rates along lineages

c is “conserved” (constrained)n is “nonconserved” (aligns but is not clearly subject to purifying selection)

Larger genomes have more of the constrained

DNA in noncoding regions

Siepel et al. 2005, Genome Research

Some constrained introns are editing complementary regions:GRIA2

Siepel et al. 2005, Genome Research

3’UTRs can be highly constrained over large distances

Siepel et al. 2005, Genome Research

3’ UTRs contain RNA processing signals, miRNA targets,other regions subject to constraints

Ultraconserved elements = UCEs

• At least 200 bp with no interspecies differences– Bejerano et al. (2004) Science 304:1321-1325 – 481 UCEs with no changes among human, mouse and rat– Also conserved between out to dog and chicken– More highly conserved than vast majority of coding regions

• Most do not code for protein – Only 111 out of 481overlap with protein-coding exons– Some are developmental enhancers.– Nonexonic UCEs tend to cluster in introns or in vicinity of genes encoding transcription factors regulating development

– 88 are more than 100 kb away from an annotated gene; may be distal enhancers

GO category analysis of UCE-associated genes

• Genes in which a coding exon overlaps a UCE– 91 Type I genes– RNA binding and modification

– Transcriptional regulation

• Genes in the vicinity of a UCE (no overlap of coding exons)– 211 Type II genes– Transcriptional regulation

– Developmental regulators

Bejerano et al. (2004) Science

Intronic UCE in SOX6 enhances expression in melanocytes in

transgenic mice

Pennacchio et al., http://enhancer.lbl.gov/

UCEsTested UCEs

The most stringently conserved sequences in eukaryotes are

mysteries • Yeast MATa2 locus

– Most conserved region in 4 species of yeast– 100% identity over 357 bp– Role is not clear

• Vertebrate UCEs– More constrained than exons in vertebrates– Noncoding UCEs are not detectable outside chordates,

whereas coding regions are• Were they fast-evolving prior to vertebrate/invertebrate divergence?

• Are they chordate innovations? Where did they come from?– Role of many is not clear; need for 100% identity over

200 bp is not obvious for any• What molecular process requires strict invariance for at least 200

nucleotides?• One possibility: Multiple, overlapping functions

Use measures of alignment texture to discriminate functional classes of

DNA• Mouse Cons track (L-scores) are measures of

alignment quality.– Match > Mismatch > Gap

• Alternatively, can analyze the patterns within alignments (texture) to try to distinguish among functional classes– Regulatory regions vs bulk DNA– Patterns are short strings of matches, mismatches,

gaps– Find frequencies for each string using training

sets• 93 known regulatory regions• 200 ancestral repeats (neutral)

• Regulatory potential genome-wide– Elnitski et al. (2003) Genome Research 13: 64-72.

1. Collapse the alignment to a small alphabet, e.g.Match involving G or C = S Transition = I Gap = GMatch involving A or T = W Transversion = V

Alignment seq1 G T A C C T A C T A C G C A seq2 G T G T C G - - A G C C C ACollapsed alphabet S W I I S V G G V I S V S W

Evaluate patterns in alignments to discriminate functional classes of DNA

2. Is a pattern, e.g., SWIIS followed by V found more frequently in alignments of

known cis-regulatory modules (set of 93) or neutral DNA (200 ancestral repeats)?

3. The regulatory potential for any alignment is a log-likelihood estimate of the extent to which its patterns are more like those in regulatory regions than in neutral DNA.

5/101/6

= 31/42/8

= 1 1/43/6

= 0.5

Regulatory potential (RP) to distinguish functional classes

Good performance of regulatory potential (RP) for finding cis-

regulatory modules

Taylor et al. (2006) Genome Research, in press (October or November)

Genes Co-expressed in Late Erythroid Maturation

G1E-ER cells: proerythroblast line lacking the transcription factor GATA-1. Can rescue by expressing an estrogen-responsive form of GATA-1Rylski et al., Mol Cell Biol. 2003

Predicted cis-Regulatory Modules (preCRMs) Around Erythroid Genes

Conservation of predicted binding sites for

transcription factorsBinding site for GATA-1

See poster from Yuepin Zhou, Yong Cheng, Hao Wang et al.

preCRMs with conserved consensus GATA-1 BS tend to be active on transfected

plasmids

preCRMs with conserved consensus GATA-1 BS tend to be active after integration into a chromosome

Examples of validated preCRMs

Correlation of Enhancer Activity with RP Score

Validation status for 99 tested fragments

preCRMs with High RP and Conserved Consensus GATA-1 Tend To Be

Validated

Conclusions

• Multispecies alignments can be used to predict whether a sequence is functional (signature of purifying selection).

• Patterns in alignments and conservation of some TFBSs can be used to predict some cis-regulatory elements.

• The predictions of cis-regulatory elements for erythroid genes are validated at a good rate.

• Databases and servers such as the UCSC Table Browser, Galaxy, and others provide access to these data.– http://genome.ucsc.edu/– http://www.bx.psu.edu/

Many thanks …

Wet Lab: Yuepin Zhou, Hao Wang, Ying Zhang, Yong Cheng, David King

PSU Database crew: Belinda Giardine, Cathy Riemer, Yi Zhang, Anton Nekrutenko

Alignments, chains, nets, browsers, ideas, …Webb Miller, Jim Kent, David Haussler

RP scores and other bioinformatic input:Francesca Chiaromonte, James Taylor, Shan Yang, Diana Kolbe, Laura Elnitski

Funding from NIDDK, NHGRI, Huck Institutes of Life Sciences at PSU

Regulatory Potential (RP) features

V GAP MGC MGC MGCMGC MAT MATMATMGC T T TGAP

MAT, MGC, V, T, GAPMAT-MAT-MAT-MAT-MAT * * * * *MAT-MAT-MAT-MAT-MGC * * * * *..MAT-T -T -MGC-V * * * * 0.001..

Positive Training set-93 known CRMs MAT, MGC, V, T, GAPMAT-MAT-MAT-MAT-MAT * * * * *MAT-MAT-MAT-MAT-MGC * * * * *..MAT-T -T -MGC-V * * * * 0.0001..

Negative Training set-200 ancestral repeats

Alignment Hum G T A C C T A C T A C C C A Mus G T G T C G - - A G C C C A

Computation of 2-way RP score using 5-symbol, 5th order Markov model MAT, MGC, V, T, GAPMAT-MAT-MAT-MAT-MAT * * * * *MAT-MAT-MAT-MAT-MGC * * * * *..MAT-T -T -MGC-V * * * * ln(10)..

To measure how much more likely an alignment is regulatory as compared with netural, the log-odds ratios for each symbol over the entire length of the alignments are summedand normalized for the length of the alignments

A score matrix is formed by taking log-odds ratio

Finding and analyzing genome data

NCBI Entrez http://www.ncbi.nlm.nih.govEnsembl/BioMart http://www.ensembl.org UCSC Table Browser http://genome.ucsc.eduGalaxy http://www.bx.psu.edu

Browsers vs Data Retrieval

• Browsers are designed to show selected information on one locus or region at a time.– UCSC Genome Browser– Ensembl

• Run on top of databases that record vast amounts of information.

• Sometimes need to retrieve one type of information for many genomics intervals or genome-wide.

• Access this by querying on the tables in the databases or “data marts”– UCSC Table Browser– EnsMart or BioMart– Entrez at NCBI

Retrieve all the protein-coding exons in humans

Galaxy: Data retrieval and analysis

• Data can be retrieved from multiple external sources, or uploaded from user’s computer

• Hundreds of computational tools– Data editing– File conversion– Operations: union,

intersection, complement …– Compute functions on data– Statistics– EMBOSS tools for sequence

analysis– PHYLIP tools for molecular

evolutionary analysis– PAML to compute

substitutions per site

• Add your own tools

Galaxy via Table Browser: coding exons

Retrieve human mutations

Find exons with human mutations: Intersection

Compute length using “expression”

Statistics on exon lengths

Plot a histogram of exon lengths

Distribution of (human mutation) exon lengths

What is that really long exon? Sort by length

SACS has an 11kb exon