1. Using vertebrate genome comparisons to find gene regulatory regions
Ross Hardison and James Taylor
Cold Spring Harbor course on Computational Genomics
Nov. 10, 2007

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. Types of sequences in mammalian genomes
About 1.5-2% codes for protein
Almost all shows a sign for purifying selection since the primate-rodent divergence
Does not preclude positive selection acting on smaller regions or in specific lineages
About 45% is interspersed repeats
22% in ancestral repeats
Good model for neutral DNA
23% in lineage-specific repeats
About 53% is noncoding, nonrepetitive
Minimum of 4% of genome is under purifying selection for a function common to mammals, but does NOT code for protein
Regulatory sequences
Non-protein coding genes
Other important sequences
About 49% under no obvious selection: no conserved function?

4. Impact of whole-genome alignments
Guide to functional sequences
in the human genome.
Conserved sequences
Sequences under purifying selection
Better gene predictions
Sequences that look like
elements that regulate
gene expression

5. Three modes of evolution

6. Negative and positive selection observed at different phylogenetic distances:

7. Genome-wide local alignment chains
Human: 2.9 Gb assembly. Mask interspersed repeats, break into 300 segments of 10 Mb.
Human
blastZ: Each segment of human is given the opportunity to align with all mouse sequences.
Mouse
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
Net
Level 2 chain

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

9. Regional variation in divergence rates

10. Implications of co-variation in divergence
Large regions (megabase sized) are changing relatively fast or slow for (almost) all types of divergence
Neutral substitution, insertions (except SINEs), deletion, recombination
This is a consistent property of each region of genomic DNA
See similar patterns for orthologous regions on independent lineages to mouse, rat and human
An aligned segment with a given similarity score in a fast-changing region is MORE significant than an aligned segments with the same similarity score in a slow-changing region.
Must take the differential rate into account in searching for functional DNA = DNA under selection.

11. 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
n = number of aligned sites in R
Waterston et al., Nature

12. 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

13. Conservation score S in different types of regions
Red: Ancestral repeats (mostly neutral)
Blue: First class in label
Green: Second class in label

14. phastCons: Likelihood of being constrained
Phylogenetic Hidden Markov Model
Posterior probability that a site is among the 10% 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:

15. Larger genomes have more of the constrained DNA in noncoding regions
Expected value if coverage by conserved elements is uniform
Siepel et al. 2005, Genome Research

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

17. 3’UTRs can be highly constrained over large distances
3’ UTRs contain RNA processing signals, miRNA targets,
other regions subject to constraints
Siepel et al. 2005, Genome Research

18. Ultraconserved elements = UCEs
At least 200 bp with no interspecies differences
Bejerano et al. (2004) Science 304:
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

19. 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
Developmental regulators
Bejerano et al. (2004) Science

20. Intronic UCE in SOX6 enhances expression in melanocytes in transgenic mice
UCEs
Pennacchio et al.,
Tested UCEs

21. 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

22. Going beyond stringent selection in noncoding sequence to find cis-regulatory modules

23. Constraint in noncoding sequences
Used to predict gene regulatory regions with some success
Some sequences conserved between humans and mouse show no apparent function
Is constraint revealing many false positives?
Sequences regulating gene expression in restricted lineages are not constrained across mammals
Is pan-mammalian constraint missing many functional sequences?
Tree from Margulies et al. (2007) Genome Res.

24. phastCons can find some but not all gene regulatory regions
LCR
HS HS HS HS HS5
phastCons
Locus control region, or LCR, is the major distal enhancer fo HBB and related, linked genes. It has 5 DNase hypersensitive sites covering about 20 kb.

25. Two extremes of constraint in CRMs
CRMs= cis-regulatory modules.
DNA sequences needed in cis for regulation of expression, usually transcription
E.g. promoters, enhancers, silencers

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

27. Distinctive divergence rates for different types of functional DNA sequences
pTRRs: putative transcriptional regulatory region; likely CRMs
Sites identified as occupied by sequence-specific transcription factors based on high-throughput chromatin immunoprecipitation assayed by hybridization to high density tiling arrays of genomic DNA= ChIP-chip

28. cis-Regulatory modules conserved beyond mammals
Human-chicken alignment capture about 6% of pTRRs (likely CRMs)
Human-fish alignments capture about 3% of pTRRs.
The pan-vertebrate CRMs tend to regulate genes whose products control transcription and development
Millions of
years 91
173
310
450

29. cis-Regulatory modules conserved in eutherian mammals and marsupials
Human-marsupial alignments capture about 32% of CRMs (pTRRs)
Tend to occur close to genes involved in aminoglycan synthesis, organelle biosynthesis
Human-mouse alignments capture about 75% of CRMs (pTRRs)
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.
Millions of
years 91
173
310
450

30. Interferon beta Enhancer-Promoter

31. Expected properties of gene regulatory regions
Can be almost anywhere
5’ or 3’ to gene
Within introns
Close or far away
Conserved between species (sometimes)
Examine interspecies alignments, noncoding regions
Evaluate likelihood of being under purifying selection, e.g. phastCons score
Some regulatory regions are deeply conserved, others are lineage-specific
Enhancers and promoters: clusters of binding sites for transcription factors (TFBSs)
Resources and servers for finding TFBSs
TRANSFAC
JASPAR
TESS
MOTIF (GenomeNet)
MatInspector

32. Finding known motifs in a query sequence
MatInspector at
K. Cartharius et al. (2006) MatInspector and beyond: promoter analysis based on transcription factor
binding sites. Bioinformatics 21: Genomatix Software GmbH, Munchen, Germany
Query: a UCE
in SOX6
1356 bp
About 1 in 4 bp is the start of a TFBS match!

33. Conservation of TFBSs between species
Servers to find conserved matches to factor binding sites
Comparative genomics at Lawrence Livermore
zPicture and rVista
Mulan and multiTF
ECR browser
Consite
Conserved TFBSs are available for some assemblies of human genome at UCSC Genome Browser
Binding site for GATA-1

34. Clusters of conserved TFBSs: PReMods
Blanchette et al. (2006) Genome Research

35. ESPERR Evolutionary and Sequence Pattern Extraction through Reduced Representation

36. ESPERR: a different approach
Don’t assume a database of known binding motifs
Don’t assume strict conservation of the important sequence signals
Instead, use alignments of validated examples to learn sequence and evolutionary patterns that characterize a class of elements

37. Objective of ESPERR

38. ESPERR overview

39. Represent columns with ancestral distributions

40. Group columns using evolutionary similarity and frequency distribution

41. An agglomerative algorithm

42. Searching for encodings

43. Evaluate “merit” of candidate mappings

44. Iterate until convergence

45. Search convergence behavior

46. Regulatory potential (RP) to distinguish functional classes

47. Variable order Markov models for discrimination

48. Use ESPERR to compute Regulatory Potential

49. Good performance of ESPERR for gene regulatory regions (RP)-1

50. Experimental tests of predicted cis-regulatory modules

51. GATA-1 is required for erythroid maturation
Common
myeloid
progenitor
Hematopoietic
stem cell
MEP
G1E cells
GATA-1
Common
lymphoid
progenitor
Myeloblast
G1E-ER4 cells
Eosinophil
Basophil
Monocyte,
macrophage
Neutrophil
Aria Rad,

52. 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

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

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

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

56. Examples of validated preCRMs

57. Correlation of Enhancer Activity with RP Score

58. Validation status for 99 tested fragments

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

60. Conclusions
Particular types of functional DNA sequences are conserved over distinctive evolutionary distances.
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.

61. Many thanks …
PSU Database crew: Belinda Giardine, Cathy Riemer, Yi Zhang, Anton Nekrutenko
Wet Lab: Yuepin Zhou, Hao Wang, Ying Zhang, Yong Cheng, David King
RP scores and other bioinformatic input:
Francesca Chiaromonte, James Taylor, Shan Yang, Diana Kolbe, Laura Elnitski
Alignments, chains, nets, browsers, ideas, …
Webb Miller, Jim Kent, David Haussler
Funding from NIDDK, NHGRI, Huck Institutes of Life Sciences at PSU