Conservation Scores

BNFO 602/691Biological Sequence Analysis

Mark Reimers, VIPBG

Conservation and Function: what kinds of DNA regions get conserved?

• Core coding regions are usually conserved across hundreds of millions of years (Myr)

• Active sites of enzymes and crucial structural elements of proteins are highly conserved

• Untranslated regions of genes are conserved over tens but not over hundreds of Myr

• Some regulatory regions evolve ‘quickly’ – over a time scale of tens of Myr

Conservation and Function: what kinds of DNA regions get conserved?

• Many splice sites and splice regulators are conserved between mouse and human

• Most promoters (70%) conserved between mouse and human

• Majority (~70%) of enhancers not conserved, but a significant minority are highly conserved

Approaches to Scoring Conservation

• Base-wise: PhyloP, GERP• Small regions: PhastCons• Small regions, tracking bias: SiPhy• Regulatory conservation within exons may be

detected by any of these methods• Key regulatory regions are harder to see

DEMO: UCSC Alignment & Conservation Tracks

Genomic Alignment• Alignment is crucial (and not trivial)

– Common alignment algorithms may misplace ambiguous bases, leading to artifactual gaps

– Inversions are often badly handled• Issue: incomplete alignments are not reflected in

scores of any current algorithm– Conservation scores computed on aligned genomes only

• Alignments of 46 placental mammals to human genome in MultiZ format at UCSC– Subset of primate alignments also

Alignment Issues

• When studying protein-coding regions, substitutions are most common

• Most genome evolution happens through insertions or deletions– Human chimp alignable genome is 97% identical– Only 91% of genome is alignable

• Regions may acquire regulatory function in some lineages but have no function in most

UCSC Alignment Symbols

• Single line ‘-’: No bases in the aligned species.– May reflect insertion in the human genome or deletion

in the aligning species.• Double line ‘=‘: Aligning species has unalignable

bases in the gap region. – Many mutations or independent indels in between the

aligned blocks in both species.• Pale yellow coloring: Aligning species has Ns in the

gap region.– Sequencing problems in aligning species

Conservation Across Mammals Differs from Conservation Across Primates

• Many regions conserved across mammals are also conserved across primates – a few appear not to be

• Some regions appear to be conserved (insofar as can be measured) in primates but not across all mammals

• What is the diagonal? Are these regions conserved?

Genomic Evolutionary Rate Profiling(GERP) Measures Base Conservation

• Estimates mean number of substitutions in each aligned genome to estimate neutral evolution rate

• Original score is “rejected substitutions”: the number of substitutions expected under ‘neutrality’ minus the number of substitutions observed at each aligned position

• New scores based on ML fit of substitution rate at base• Positive scores (fewer than expected) indicate that a site is

under evolutionary constraint. – Negative scores may be weak evidence of accelerated rates of

evolution

PhyloP Assigns Conservation P-values• Estimates mean number of substitutions in each

aligned genome to estimate neutral evolution rate estimated from non-coding data (conservative)

• Compares probability of observed substitutions under hypothesis of neutral evolutionary rate

• Scores reflect either conservation (positive scores) or selection (negative scores)

• Score defined as –log10(P) where P is p-value for test of number of substitutions following (uniform) neutral rate inferred from all sites in alignment

NB PhyloP may also refer to a suite of tools

PhastCons Fits a Hidden Markov Model

• PhastCons fits HMM with states ‘conserved’ and ‘not conserved’

• Neutral substitution rates estimated from data as for PhyloP

• Tunable parameter m represents inverse of expected length of ‘conserved’ regions

• Parameter n sets proportion of conserved regions

Siepel A et al. Genome Res. 2005;15:1034-1050

PhastCons Fits a Hidden Markov Model

• Scaling parameter ρ (0 ≤ ρ ≤ 1) represents the average rate of substitution in conserved regions relative to average rate in non-conserved regions and is estimated from data

• Originally developed to detect moderate-sized sequences such as non-coding RNA

• Can be adapted to shorter sequences but not as powerful

SiPhy

• SiPhy models the pattern of substitutions, rather than just the rate, as do most others. – Biased substitutions (e.g. conserved lysine: AAA <-> AAG only) will be identified as constrained– Some TFBS have similar degeneracy in evolution– This is a more refined approach than rate models, but

requires a fairly deep (or wide) phylogeny• SiPhy uses a Bayesian approach and needs two

parameters like PhastCons: the fraction of sequence conserved, and the typical length of a conserved region.

SiPhy Applied to Mammalian GenomesIdentification of four NRSF-binding sites in NPAS4.

K Lindblad-Toh et al. Nature (2011)

Comparison of Methods

• PhyloP, PhastCons, and GERP give fairly similar results over deep phylogenies (e.g. vertebrates)

• Differ substantially over bushes (e.g. primates)• SiPhy is more sensitive over moderately deep

phylogenies (e.g. mammals)– Cannot be implemented for primates because of

insufficient substitutions

Issues With Conservation Scores

• Most scores are misleading about gaps in alignments: they don’t distinguish between contig gaps (incomplete genomes) and inserted or deleted regions– This information is often available but inconvenient to

use• Each model was devised with a particular kind of

conservation in mind, and may not be adaptable to all kinds

• Broken sequences – e.g. ZNF TFBS are not captured well by any current method