1. Comparative Genomics I: Tools for comparative genomics
Ross Hardison, Penn State University
James Taylor: Courant Institute, New York University
Major collaborators: Webb Miller, Francesca Chiaromonte, Laura Elnitski, David King, et al., PSU
David Haussler, Jim Kent, Univ. California at Santa Cruz
Ivan Ovcharenko, Lawrence Livermore National Lab
CSH Nov. 11, 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
Negative selection
(purifying)
Positive selection
(adaptive)
Similarity
Neutral DNA
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.

5. DCODE.org Comparative Genomics: Align your own sequences
blastZ
multiZ and TBA

6. zPicture interface for aligning sequences

7. Automated extraction of sequence and annotation

8. 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
Galaxy server:
Webb Miller
Jim Kent
Schwartz et al., 2003, blastZ, Genome Research
Blanchette et al., 2004, TBA and multiZ, Genome Research
David Haussler

9. 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

10. 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

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. 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

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

15. Score multi-species alignments for features associated with function
Multiple alignment scores
Margulies et al. (2003) Genome Research 13:
Binomial, parsimony
PhastCons
Siepel et al. (2005) Genome Research 15:
Phylogenetic Hidden Markov Model
Posterior probability that a site is among the most highly conserved sites
GERP
Cooper et al. (2005) Genome Research 15:
Genomic Evolutionary Rate Profiling
Measures constraint as rejected substitutions = nucleotide substitution deficits

16. 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:

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

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

19. 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

20. 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

21. 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

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

23. 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

24. Finding and analyzing genome data
NCBI Entrez
Ensembl/BioMart UCSC Table Browser
Galaxy

25. 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

26. Retrieve all the protein-coding exons in humans

27. 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

28. Galaxy via Table Browser: coding exons

29. Retrieve human mutations

30. Find exons with human mutations: Intersection

31. Compute length using “expression”

32. Statistics on exon lengths

33. Plot a histogram of exon lengths

34. Distribution of (human mutation) exon lengths

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

36. SACS has an 11kb exon