Transcript Conservation scores

Conservation Scores
BNFO 602/691
Biological 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
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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
Siepel A et al. Genome Res.
regions
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 nonconserved 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 Genomes
Identification 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