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Practical applications of HMMs : ChromHMM. Sushmita Roy Nov 5th. Chromatin organization and gene expression. http:// www.youtube.com/watch?v =eYrQ0EhVCYA. ChIP-seq to measure histone data. Adapted from Dewey lecture and Peter Park Nature Genetics Review.

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Practical applications of hmms chromhmm

Practical applications of HMMs: ChromHMM

Sushmita Roy

Nov 5th


Chromatin organization and gene expression
Chromatin organization and gene expression

http://www.youtube.com/watch?v=eYrQ0EhVCYA


Chip seq to measure histone data
ChIP-seq to measure histone data

Adapted from Dewey lecture and Peter Park Nature Genetics Review


Chip seq data for multiple marks
ChIP-seq data for multiple marks

Chromatin state: A specific combinations of mark values.

Important because it can be used to segment the genome into biologically

meaningful units.


Problem definition
Problem definition

  • Given

    • A collection of genome-wide measurements of chromatin marks

  • Do

    • Segment the genome into N chromatin states


An hmm for segmenting genomes using chromatin marks
An HMM for segmenting genomes using chromatin marks

  • HMM

    • State: chromatin state

    • Emission->multiple chromatin marks

    • Need a multi-variate HMM


Binarizing the chromatin data
Binarizing the chromatin data

  • Each mark is represented by a binary variable vt,m:

    • 1: mark is present

    • 0: mark is absent

Observed

Marks

..

Genomic sequence

..

..

t

t+1

t+2

t+3

..


Chromhmm with 3 states
ChromHMM with 3 states

Begin

1

3

2


Chromhmm notation
ChromHMM notation

  • pk,mdenotes the probability of mark mbeing ON in state k

  • Emission probability of M marks per state is a product of M bernoulli random variables.

  • bk,l denotes the probability of transitioning from state i to state j

  • ak: initial probability of state k


Learning the chromhmm
Learning the ChromHMM

  • Need to figure out the number of states

  • Learn HMMs for K=2 to 80 states with a penalty factor to penalize the number of parameters

  • State transitions: start with the fully connected HMM, and if set parameters to zero if <10-10

  • Final model had 51 states


Learned emission parameters
Learned Emission parameters

Emission parameters for state 5

States


Example output around capza2 gene from chromhmm
Example output around CAPZA2 gene from ChromHMM

Input chromatin marks

Inferred state sequences


Posterior probability distributions of all 51 states around capza gene
Posterior probability distributions of all 51 states around CAPZA gene

Max posterior state

Posterior probability values of each state


Summary
Summary CAPZA gene

  • HMMs are powerful models to capture sequential data

  • Very popular in computational biology

    • Gene annotation

    • Representation of a profile: protein domain finding

    • Genome segmentation