BIOINFORMATICS

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BIOINFORMATICS
Gene Finding With A Hidden Markov model Of
Genomic Structure and Evolution.
Jakob Skou Pedersen and Jotun Hein
Deepak Verghese CS 6890
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Number of models have incorprated evolutionary
information in them

• GPHMM
• CONSERVED Exon method
• 2 step GLASS n ROSETTA
• TWINSCAN which extends GENESCAN
• etc

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• Do not exploit all information in evolutionary
  pattern
• Not easily extended to multiple genome sequences.

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(EHMM)
EVOLUTIONARY HIDDEN MARKOV MODEL
A Probabilistic model of both Genome Structure
and Evolution

• Composed of
• Hidden Markov Model (HMM)
• Phylogenetic Tree

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ADVANTAGES

• Can handle any number of sequences in an
  alignment.
• Can have properties of higher order HMMs
• Can handle variability in the sequences along the
  alignment
• State of art evolutionary models can be
  incorporated later
• Evolutionary events between different genomes are
  not treated independently

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MODEL

• SCOPE
• Not to compete with the existing finding methods
• on performance but to illustrate the power of
  this approach.
• Relies on a pre produced alignment.

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MARKOV CHAINS

• A set of states
• The transitions from one state to all other
  states, including itself, are governed by a
  probability distribution
• First order Markov chain the probabilities
  depend solely on the current state
• n-th order Markov chain n previous states

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HIDDEN MARKOV MODEL
5 Components

• A set of states
• Matrix of transition probabilities ( A )
• Set of alphabets ( C )
• Set of emission distribution (e)
• Initial state distribution ( B )

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Example of hidden Markov model

• A C A - - - A T G
• T C A A C T A T C
• A C A C - - A G C
• A G A - - - A T C
• A C C G - - A T C

NO 11 correspondence between states and
symbols Why the name Hidden ?
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Components

• State k
• Emits symbols (observables) C
• PROBABILISTIC MODEL
• Emission Distribution e
• Initial state
  distribution B
• Transition Probabilities
  A

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Path ?

• Different paths possible for same sequence

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In EHMM

• Emission distribution
• e specified by
• Evolutionary model Ek
• Phylogenetic tree T

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PHYLOGENETIC TREES
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Motivation The problem of explaining the
evolutionary history of today's species

• In Phylogenetic trees
• Leaves represent present day species
• Character states of inner nodes are missing data
• Interior nodes represent hypothesized ancestors
• The length of the brances of a tree represent the
  evolutionary difference.

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Evolution is often modeled by continuous markov
chains Here evolution along the branches
of the phylogenetic tree is modelled by
Ek Transition probability Pk ( t ) For a branch
length t P k ( t ) exp ( t Q k
) Increasing the number of sequences is
increasing the amount of evolutionary
information. THE ALIGNMENT COLUMN CORRESPONDS
TO THE STATE OF ELOVUTION AT THE LEAVES OF THE
PHYLOGENETIC TREE
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THE PEOPABILITY OF GENERATING AN ALIGNMENT
COLUMN IN STATE K EQUALS PROBABILITY OF
OBSERVING A GIVEN CHARACTER PATTERN ON THE
LEAVES OF T WHEN GIVEN E k
Phylogenetic tree of the entries of the 3
alignment columns
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• Codon based evolutionary model used to calculate
• emission probability of columns of A
• Nucleotide Based evolutionary model used to
  calculate
• emission probability of column B
• Emission probability of C is got from the
  equilibrium distribution
• of the the relevant evolutionary model

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Parameter Estimation

• Parameters of HMM are estimated by a combination
  of
• Baum Welch
• Powell
• Evolutionary model E
• divided into
• E equ
• E evo

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Initial State Distribution B can be estimated by
Baum-Welch but It is generally set to 0.000 01
for all states except the intergenic . The
expectation step of Baum-Welch estimates
the number of nucleotides emitted from each
state the expected number of state
transitions Expected number of times a state is
used. Powell another optimization method
estimates E evo
phylogenetic tree T Baum Welch method is used
to estimate E equ
A
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Therefore Likelihood of an alignment ( x ) given
a parameterization of the EHMM Can be found by
the equation
Here we are summing over all possible paths This
can be done in linear time by Dynamic Programming
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EHMM is fully probabilistic and can be used to
simulate data and find genes.

• EUKARYOTIC GENOME MODEL can be used to generate
  alignments.
• Reduced model produces only inner exons.

eukaryotic EHMM
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Results

• Benefits of modeling evolution with a EHMM
• using a data set of orthologous
  mouse/human gene pair
• Benefit will depend on divergence between
  sequences compared
• Key parameter for modelling the difference
  between exons and introns is the dN/dS ratio.

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(No Transcript)
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Moreover we see that Evolutionary model shows a
distinct difference between the intergenic
/intron state and the codon state
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Evaluations were performed on both single and
aligned sequences
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Graphical Representation
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Simple model used now not comparable to state of
art methods
Any number of aligned sequences can be handled
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• Extensions of the model

• GENESCAN can be extended into HMM
• Splice site finders
• Models of ribosome binding site and promoter
  regions
• Non geometric length distributions of exons
• Pseudo higher order EHMM can be constructed.
• Idea of pair HMM to multiple sequences

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Disadvantages in present model

• Existing frame work does not model gaps but
  treats it as missing data.
• Optimal data for EHMM is a multiple alignment of
  full length genome.
• Challenge in constructions of the alignment is to
  reduce the noise per signal ratio.
• BUT ..