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Max-Planck-Institut für molekulare Genetik

EBSV06

Martin VingronMax-Planck-Institut für molekulare Genetik

Reporting on work mainly byTobias Müller, Antje Krause, Hannes Luz

Family Specific Rates of Protein Evolutionor, more general

Markov models in protein evolution: Resolvent method, Systers, FSR

Max-Planck-Institut für molekulare Genetik

EBSV06

Amino Acid Replacement

Max-Planck-Institut für molekulare Genetik

EBSV06

Degree of Divergence

Max-Planck-Institut für molekulare Genetik

EBSV06

Markov Assumption

Max-Planck-Institut für molekulare Genetik

EBSV06

Exponential function

p(t) = exp(qt)

P(t) = exp(Qt)

The same works with matrices:

Infinitesimal generator, Rate matrix

Transition probabilites

A

C

R

K

FL

Max-Planck-Institut für molekulare Genetik

EBSV06

The Model Variables

Max-Planck-Institut für molekulare Genetik

EBSV06

The Problem

Max-Planck-Institut für molekulare Genetik

EBSV06

Dayhoff‘s Estimation Procedure

Max-Planck-Institut für molekulare Genetik

EBSV06

Linear Approximation

Max-Planck-Institut für molekulare Genetik

EBSV06

Shortcomings

Max-Planck-Institut für molekulare Genetik

EBSV06

The Problem (revisited)

Homologous Sequences

Evolutionary Distance

Input Data Rates

Max-Planck-Institut für molekulare Genetik

EBSV06

An alternative Representation

The întergral is called Resolvent of the transition probabilities.

Max-Planck-Institut für molekulare Genetik

EBSV06

Resolvent Estimation Procedure

Max-Planck-Institut für molekulare Genetik

EBSV06

ML Estimation of alignment distance

Max-Planck-Institut für molekulare Genetik

EBSV06

Resolvent function

theoreticalestimated

theoreticalestimated

ij

Max-Planck-Institut für molekulare Genetik

EBSV06

Amino Acid Score Matrix

Max-Planck-Institut für molekulare Genetik

EBSV06

Here we are…

VT160VT160

Max-Planck-Institut für molekulare Genetik

EBSV06

Proportion of true relations found

The proof of the pudding….… is in the eating!

Nu

mb

er

of

fals

e p

osi

tive

m

atc

hes

Nu

mb

er

of

fals

e p

osi

tive

matc

hes

Proportion of true relations found

Many false positives, few true homologs

Few false positives,many true homologs

References: [1] Gavin A. Price , Gavin E. Crooks , Richard E. Green , and Steven E. Brenner 2005. Statistical evaluation of pairwise proteinsequence comparison with the Bayesian bootstrap.Bioinformatics, 21:3824-3831. [2] Gavin E. Crooks, and Steven E. Brenner 2005. An alternative model of amino acid replacement, 7,21, 975-980.

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