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Comparative Genomics Ross Hardison, Penn State University Major collaborators: Webb Miller, Francesca Chiaromonte, Laura Elnitski, David King, et al., PSU James Taylor: Courant Institute, New York University David Haussler, Jim Kent, Univ. California at Santa Cruz Ivan Ovcharenko, Lawrence Livermore National Lab PSU 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 Negative selection (purifying) Similarity Neutral DNA Positive selection (adaptive) Position along chromosome 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 Average percent identity (black) or percent aligned (blue) for 10,000 orthologous genes Waterston et al, Mouse Genome, Nature 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 domains Gray: proteins without recognized domains Waterston et al. Nature 2002 Black: Nuclear proteins Red: Cytoplasmic proteins Gray: Extracellular proteins; positive, diversifying selection KA= rate of nonsynonymous substitutions KS= rate of synonymous substitutions 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 Webb Miller • All against all comparisons – High sensitivity and specificity • Computer cluster at UC Santa Cruz – 1024 cpus Pentium III – Job takes about half a day Jim Kent • Results available at – UCSC Genome Browser http://genome.ucsc.edu – Galaxy server: http://www.bx.psu.edu Schwartz et al., 2003, blastZ, Genome Research Blanchette et al., 2004, TBA and multiZ, Genome Research David Haussler Genome-wide local alignment chains Human: 2.9 Gb assembly. Mask interspersed repeats, break into 300 segments of 10 Mb. Human Mouse blastZ: Each segment of human is given the opportunity to align with all mouse sequences. 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 chain Level 2 chain Net Comparative genomics to find functional sequences Genome size 2,900 Find common sequences blastZ, multiZ 2,400 Human Identify functional sequences: ~ 145 Mbp All mammals 1000 Mbp 2,500 Mouse Rat 1,200 million base pairs (Mbp) 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 are identical between human and mouse m = average fraction of aligned sites that are identical in aligned ancestral repeats in the surrounding region Waterston et al., Nature n = number of aligned sites in R Decomposition of conservation score into neutral and likely-selected portions Neutral DNA (ARs) All DNA Likely selected DNA At 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 label Green: Second class in label Waterston et al., Nature Leverage many species to improve accuracy and resolution of signals for constraint ENCODE multi-species alignment group Margulies et al., 2007 Coverage of human by alignments with other vertebrates ranges from 1% to 91% 5.4 Millions of years Human 91 92 173 220 310 360 450 5% Distinctive divergence rates for different types of functional DNA sequences Percentofofregions human not genome not in Percent in alignments alignments 100 100 90 90 80 80 70 70 Genome Coding exons Ultraconserved (HM) Log. (Genome) 60 60 50 50 40 40 30 30 20 20 10 10 00 00 100 200 300 400 500 100 200 300 400 500 Time of divergence from common ancestor to Time of divergence from Myr common human, ago ancestor to human, Myr ago Large divergence in cis-regulatory modules from opossum to platypus cis-Regulatory modules conserved from human to fish • • Millions of years 91 173 310 450 About 20% of CRMs Tend to regulate genes whose products control transcription and development cis-Regulatory modules conserved in eutherian mammals and marsupials • Human-marsupial alignments capture about 60% of CRMs Millions of years 91 173 310 450 – 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 • 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) Siepel et al. (2005) Genome Research 15:1034-1050 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 UCEs Tested UCEs Pennacchio et al., http://enhancer.lbl.gov/ 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. Evaluate patterns in alignments to discriminate functional classes of DNA 1. Collapse the alignment to a small alphabet, e.g. Match involving G or C = S Transition = I Gap = G Match 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 A Collapsed alphabet S W I I S V G G V I S V S W 5/10 = 3 1/6 1/4 = 1 2/8 1/4 = 0.5 3/6 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 loglikelihood estimate of the extent to which its patterns are more like those in regulatory regions than in neutral DNA. 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-1 Rylski et al., Mol Cell Biol. 2003 Predicted cis-Regulatory Modules (preCRMs) Around Erythroid Genes Conservation of predicted binding sites for transcription factors Binding 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 Alignments, chains, nets, browsers, ideas, … Webb Miller, Jim Kent, David Haussler PSU Database crew: Belinda Giardine, Cathy Riemer, Yi Zhang, Anton Nekrutenko 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 MGC, V, T, GAP5th order Markov model Computation ofM2-way RP score MAT, using 5-symbol, AT-MAT-MAT-MAT-MAT * * * * * Alignment MAT-MAT-MAT-MAT-MGC * * . Hum G T A C C T . MAT-TG -TT -MGC Mus G -V T * C *G . . MGC MAT T T MGC V * * * A * - C T A *-ln(10) A G GAP GAP MAT T C C C C C C A A MGC MGC MGC MAT Negative Training set-200 ancestral repeats A set-93 score known matrixCRMs is formed by taking log-odds ratio Positive Training MAT, MGC, V, T, GAP MAT, MGC, V, T, GAP To measure how more isATregulatory as compared MAT-M AT-MAT-MAT -MATmuch * * *likely * an * alignment MAT-M -MAT-MAT-MAT * * * with * netural, * M AT-M AT-MAT-MAT -MGC for * * * * *over the entire MAT-MATlength -MAT-MAT GC alignments * * * are * summed * the log-odds ratios each symbol of-Mthe . . and normalized for the length of the alignments . MAT-T . . -T -MGC-V * * * * 0.001 . MAT-T . . -T -MGC-V * * * * 0.0001 Finding and analyzing genome data NCBI Entrez Ensembl/BioMart UCSC Table Browser Galaxy http://www.ncbi.nlm.nih.gov http://www.ensembl.org http://genome.ucsc.edu 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