1. 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

2. 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

3. 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

4. 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.

5. Taxonomic distribution of homologs of mouse proteins Waterston et al.

6. 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

7. 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 Black: Nuclear proteins Red: Cytoplasmic proteins Gray: Extracellular proteins; positive, diversifying selection K A = rate of nonsynonymous substitutions K S = rate of synonymous substitutions Waterston et al. Nature 2002

8. 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 alpha 2u -globulin –Olfaction and odorant detection Olfactory receptors Also are rapidly evolving Segmental duplications are enriched for the same genes Rat Genome SPC 2004 Nature

9. 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

10. DCODE.org Comparative Genomics: Align your own sequences blastZmultiZ and TBA

11. zPicture interface for aligning sequences

12. Automated extraction of sequence and annotation

13. 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 Research Blanchette et al., 2004, TBA and multiZ, Genome Research

14. 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 chain Level 2 chain

15. Comparative genomics to find functional sequences Genome size 2,900 2,400 2,500 1,200 Human MouseRat All mammals 1000 Mbp Identify functional sequences: ~ 145 Mbp million base pairs (Mbp) Find common sequences blastZ, multiZ Also birds: 72Mb Papers in Nature from mouse and rat and chicken genome consortia, 2002, 2004

16. 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  = average fraction of aligned sites that are identical in aligned ancestral repeats in the surrounding region n = number of aligned sites in R Waterston et al., Nature

17. 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

18. 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

19. 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

20. Leverage many species to improve accuracy and resolution of signals for constraint ENCODE multi-species alignment group Margulies et al., 2007

21. Coverage of human by alignments with other vertebrates ranges from 1% to 91% Human 5.4 91 92 310 360 450 173 Millions of years 220 5%

22. Distinctive divergence rates for different types of functional DNA sequences

23. Large divergence in cis-regulatory modules from opossum to platypus

24. cis-Regulatory modules conserved from human to fish 310 450 91 173 Millions of years About 20% of CRMs Tend to regulate genes whose products control transcription and development

25. cis-Regulatory modules conserved in eutherian mammals and marsupials 310 450 91 173 Millions of years 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.

26. 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

27. 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)

28. Larger genomes have more of the constrained DNA in noncoding regions Siepel et al. 2005, Genome Research

29. Some constrained introns are editing complementary regions:GRIA2 Siepel et al. 2005, Genome Research

30. 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

31. 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

32. 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

33. Intronic UCE in SOX6 enhances expression in melanocytes in transgenic mice Pennacchio et al., http://enhancer.lbl.gov/ UCEs Tested UCEs

34. 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

35. 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.

36. 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 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. 5/10 1/6 = 3 1/4 2/8 = 1 1/4 3/6 = 0.5

37. Regulatory potential (RP) to distinguish functional classes

38. Good performance of regulatory potential (RP) for finding cis-regulatory modules Taylor et al. (2006) Genome Research, in press (October or November)

39. 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

40. Predicted cis-Regulatory Modules (preCRMs) Around Erythroid Genes

41. Conservation of predicted binding sites for transcription factors Binding site for GATA-1 See poster from Yuepin Zhou, Yong Cheng, Hao Wang et al.

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

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

44. Examples of validated preCRMs

45. Correlation of Enhancer Activity with RP Score

46. Validation status for 99 tested fragments

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

48. 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/

49. 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

50. Regulatory Potential (RP) features VG AP M GC M AT M GC TTTG AP MAT, MGC, V, T, GAP M AT -M AT -M AT -M AT -M AT * * * * * M AT -M AT -M AT -M AT -M GC * * * * *. M AT -T -T -M GC -V * * * * 0.001. Positive Training set-93 known CRMs MAT, MGC, V, T, GAP M AT -M AT -M AT -M AT -M AT * * * * * M AT -M AT -M AT -M AT -M GC * * * * *. M AT -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, GAP M AT -M AT -M AT -M AT -M AT * * * * * M AT -M AT -M AT -M AT -M GC * * * * *. M AT -T -T -M GC -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 summed and normalized for the length of the alignments A score matrix is formed by taking log-odds ratio

51. Finding and analyzing genome data NCBI Entrez http://www.ncbi.nlm.nih.gov Ensembl/BioMart http://www.ensembl.org UCSC Table Browser http://genome.ucsc.edu Galaxy http://www.bx.psu.edu

52. 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

53. Retrieve all the protein-coding exons in humans

54. 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

55. Galaxy via Table Browser: coding exons

56. Retrieve human mutations

57. Find exons with human mutations: Intersection

58. Compute length using “expression”

59. Statistics on exon lengths

60. Plot a histogram of exon lengths

61. Distribution of (human mutation) exon lengths

62. What is that really long exon? Sort by length

63. SACS has an 11kb exon