Comparative Genomics

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Comparative Genomics Annotation
The Foundation of Comparative Genomics Non-Compara
tive Annotation Three methodological tasks of CG
Annotation Protein Gene Finding RNA
Structure Prediction Signal
Finding Challenges Empirical Investigations
Genes Signals Functional Stories
Positive Selection Open Questions
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Hidden Markov Models in Bioinformatics

• Definition
• Three Key Algorithms
• Summing over Unknown States
• Most Probable Unknown States
• Marginalizing Unknown States
• Key Bioinformatic Applications
• Pedigree Analysis
• Profile HMM Alignment
• Fast/Slowly Evolving States
• Statistical Alignment

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Further Examples
Isochore Churchill,1989,92
Lp(C)Lp(G)0.1, Lp(A)Lp(T)0.4,
Lr(C)Lr(G)0.4, Lr(A)Lr(T)0.1
Likelihood Recursions
Likelihood Initialisations
Simple Eukaryotic
Gene Finding Burge and Karlin, 1996
Simple Prokaryotic
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Further Examples
Secondary Structure Elements Goldman, 1996
a ? L
a .909 .0005 .091
? .005 .881 .184
L .062 .086 .852
.325 .212 .462
HMM for SSEs
Adding Evolution
SSE Prediction
Profile HMM Alignment Krogh et al.,1994
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Grammars Finite Set of Rules for Generating
Strings
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Simple String Generators Terminals (capital)
--- Non-Terminals (small) i. Start with S
S --gt aT bS T
--gt aS bT ? One sentence odd of as S-gt
aT -gt aaS gt aabS -gt aabaT -gt aaba ii. ?S--gt
aSa bSb aa bb One sentence (even length
palindromes) S--gt aSa --gt abSba --gt abaaba
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Stochastic Grammars
The grammars above classify all string as
belonging to the language or not.
All variables has a finite set of substitution
rules. Assigning probabilities to the use of
each rule will assign probabilities to the
strings in the language.
If there is a 1-1 derivation (creation) of a
string, the probability of a string can be
obtained as the product probability of the
applied rules.
i. Start with S. S --gt (0.3)aT (0.7)bS
T --gt (0.2)aS (0.4)bT (0.2)?
0.2
0.7
0.3
0.3
S -gt aT -gt aaS gt aabS -gt aabaT -gt aaba
0.2
ii. ?S--gt (0.3)aSa (0.5)bSb (0.1)aa (0.1)bb
0.1
0.3
0.5
S -gt aSa -gt abSba -gt abaaba
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Finding Regulatory Signals in Genomes
The Computational Problem Non-homologous/homo
logous sequences Known/unknown signal
1 common signal/complex signals/additional
information Combinations
Regulatory signals know from molecular biology
Different Kinds of Signals
Promotors Enhancers
Splicing Signals
a-globins in humans
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Weight Matrices Sequence Logos
Wasserman and Sandelin (2004) Applied
Bioinformatics for the Identification of
Regulatory Elements Nature Review Genetics
5.4.276
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Motifs in Biological Sequences 1990 Lawrence
Reilly An Expectation Maximisation (EM)
Algorithm for the identification and
Characterization of Common Sites in Unaligned
Biopolymer Sequences Proteins 7.41-51. 1992
Cardon and Stormo Expectation Maximisation
Algorithm for Identifying Protein-binding sites
with variable lengths from Unaligned DNA
Fragments L.Mol.Biol. 223.159-170 1993 Lawrence
Liu Detecting subtle sequence signals a Gibbs
sampling strategy for multiple alignment Science
262, 208-214.
Q(q1,A,,qw,T) probability of different bases
in the window
A(a1,..,aK) positions of the windows
q0(qA,..,qT) background frequencies of
nucleotides.
Priors A has uniform prior Qj
has Dirichlet(N0a) prior a base frequency in
genome. N0 is pseudocounts
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The Gibbs Sampler
For i1,..,d Draw xi(t1) from conditional
distribution p(.x-i(t)) and leave remaining
components unchanged, i.e. x-i (t1) x-i
(t)
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The Gibbs sampler
Gibbs iteration
From Lawrence, C. et al.(1993) Detecting Subtle
Sequence Signals A Gibbs Sampler approach to
Multiple Alignment. Science 262.208-
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The Gibbs sampler example
From Lawrence, C. et al.(1993) Detecting Subtle
Sequence Signals A Gibbs Sampler approach to
Multiple Alignment. Science 262.208-
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Natural Extensions to Basic Model I
Modified from Liu
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Natural Extensions to Basic Model II
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Combining Signals and other Data
Modified from Liu
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MEME- Multiple EM for Motif Elicitation
Motif nucleotide distribution Mp,q, where p -
position, q-nucleotide. Background
distribution Bq, l is probability that a Zi,j
1
Find M,B, l, Z that maximize Pr (X, Z M, B,
l) Expectation Maximization to find a local
maximum Iteration t Expectation-step Z(t)
E (Z X, (M, B, l) (t) )
Maximization-step Find (M, B, l) (t1) that
maximizesPr (X, Z(t) (M, B, l) (t1))
Bailey, T. L. and C. Elkan (1994). "Fitting a
mixture model by expectation maximization to
discover motifs in biopolymers." Proc Int Conf
Intell Syst Mol Biol 2 28-36.
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Phylogenetic Footprinting (homologous detection)
Blanchette and Tompa (2003) FootPrinter a
program designed for phylogenetic footprinting
NAR 31.13.3840-
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(No Transcript)
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Statistical Alignment and Footprinting.
Solution Cartesian Product of HMMs
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Structure does not stem from an evolutionary
model

• The equilibrium annotation
• does not follow a Markov Chain

• Each alignment in from the Alignment HMM
• is annotated by the Structure HMM

• No ideal way of simulating

using the HMM at the alignment will give other
distributions on the leaves
using the HMM at the root will give other
distributions on the leaves
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(Homologous Non-homologous) detection
Wang and Stormo (2003) Combining phylogenetic
data with co-regulated genes to identify
regulatory motifs Bioinformatics 19.18.2369-80
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Regulatory Signals in Humans
Transcription in Eukaryotes is done by RNA
Polymerase II. 1850 DNA-binding proteins in the
human genome.

• Transcription Start Site - TSS
• Core Promoter - within 100 bp of TSS
• Proximal Promoter Elements - 1kb TSS
• Locus Control Region - LCR
• Insulator
• Silencer
• Enhancer

Sourece Transcriptional Regulatory Elements in
the Human GenomeGlenn A. Maston, Sara K. Evans,
Michael R. GreenAnnual Review of Genomics and
Human Genetics. Volume 7, Sep 2006
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Sourece Transcriptional Regulatory Elements in
the Human GenomeGlenn A. Maston, Sara K. Evans,
Michael R. GreenAnnual Review of Genomics and
Human Genetics. Volume 7, Sep 2006
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a-globins
Multispecies Conserved Sequences - MCSs Analyzed
238kb in 22 species Found 24 MCSs Programs use
GUMBY - VISTA - MULTIPIPMAKER MULTILAGAN -
CLUSTALW - DIALIGN TRANSFAC 6.0 - TRES -
Experimental Knowledge of the region
Hypersensitive sites (DHSs) DNA
Methylation Region lies in CG rich, gene rich
region close to the telomeres. It is not easy
to align CG-islands.
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Promoters in a-globins

• 94.273-114.273 vista illus.
• 5 MCSs
• Divergence relative to human

1. Promoters MCSs - 11
2. Regulatory MCSs - 4
3. Intronics MCSs - 2
4. Exonic MCSs - 4
5. Unknown - 3

Sourece Hughes et al.(2005) Annotation of
cis-regulatory elements by identification,
subclassification, and functional assessment of
multispecies conserved sequences PNAS 2005 102
9830-9835
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Regulatory Protein-DNA Complexes
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Challenges
Open Problems