Using vertebrate genome comparisons to find gene regulatory regions Ross Hardison and James Taylor Cold Spring Harbor course on Computational Genomics Nov. 10, 2007
Jan 19, 2016
Using vertebrate genome comparisons to find gene
regulatory regions
Ross Hardison and James TaylorCold Spring Harbor course on Computational
GenomicsNov. 10, 2007
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
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?
Impact of whole-genome alignments
Guide to functional sequencesin the human genome.
Better gene predictions
Sequences under purifying selection
Conserved sequences
Sequences that look like elements that regulategene expression
Three modes of evolution
Negative and positive selection observed at different phylogenetic
distances:
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
Regional variation in divergence rates
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.
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
Conservation score S
in different types of regions
Red: Ancestral repeats (mostly neutral)Blue: First class in labelGreen: Second class in label
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 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)
Larger genomes have more of the constrained
DNA in noncoding regions
Siepel et al. 2005, Genome Research
Expected value if coverage by conserved elements is uniform
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
Going beyond stringent selection in noncoding sequence to find cis-regulatory modules
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.
phastCons can find some but not all gene regulatory regions
LCR HS1 HS2 HS3 HS4 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.
Two extremes of
constraint in CRMs
CRMs= cis-regulatory modules.DNA sequences needed in cis for regulation of expression, usually transcriptionE.g. promoters, enhancers, silencers
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%
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
cis-Regulatory modules conserved beyond mammals
310
450
91
173
Millions ofyears
• 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
cis-Regulatory modules conserved in eutherian mammals and marsupials
310
450
91
173
Millions ofyears
• 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.
Interferon beta Enhancer-Promoter
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 http://www.gene-regulation.com/– JASPAR http://jaspar.cgb.ki.se/cgi-bin/jaspar_db.pl– TESS http://www.cbil.upenn.edu/cgi-bin/tess/tess– MOTIF (GenomeNet) http://motif.genome.jp/– MatInspector http://www.genomatix.de/
Finding known motifs in a query sequence
MatInspector at http://www.genomatix.de/K. Cartharius et al. (2006) MatInspector and beyond: promoter analysis based on transcription factor binding sites. Bioinformatics 21:2933-2942. Genomatix Software GmbH, Munchen, Germany
Query: a UCE in SOX61356 bp
About 1 in 4 bp is the start of a TFBS match!
Conservation of TFBSs between species
• Servers to find conserved matches to factor binding sites– Comparative genomics at Lawrence Livermore http://www.dcode.org/
• zPicture and rVista• Mulan and multiTF• ECR browser
– Consite http://mordor.cgb.ki.se/cgi-bin/CONSITE/consite• Conserved TFBSs are available for some assemblies of human genome at
UCSC Genome Browser
Binding site for GATA-1
Clusters of conserved TFBSs: PReMods
Blanchette et al. (2006) Genome Research
http://genomequebec.mcgill.ca/PReMod/
ESPERREvolutionary and Sequence Pattern Extraction through
Reduced Representation
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
Objective of ESPERR
ESPERR overview
Represent columns with ancestral distributions
Group columns using evolutionary similarity and frequency
distribution
An agglomerative algorithm
Searching for encodings
Evaluate “merit” of candidate mappings
Iterate until convergence
Search convergence behavior
Regulatory potential (RP) to distinguish functional classes
Variable order Markov models for discrimination
Use ESPERR to compute Regulatory Potential
Good performance of ESPERR for gene regulatory regions (RP)
-1
Experimental tests of predicted cis-regulatory
modules
GATA-1 is required for erythroid maturation
Aria Rad, 2007 http://commons.wikimedia.org/wiki/Image:Hematopoiesis_(human)_diagram.png
MEP Hematopoietic stem cell
Commonmyeloidprogenitor
Myeloblast
Basophil
Commonlymphoidprogenitor
Neutrophil
Eosinophil
Monocyte, macrophage
GATA-1G1E cells
G1E-ER4 cells
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
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
• 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.– 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