Bioinformatics lectures at Rice University

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Oct 2, 2013 (3 years and 10 months ago)

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Bioinformatics lectures at Rice
University

Li Zhang

Lecture 8: HMM models and application in DNA
sequence analysis
-
2

http://odin.mdacc.tmc.edu/~llzhang/RiceCourse

Markov chain and Hidden Markov
Model


Markov chain:


A Markov chain is a sequence of

random variables
X
1
,

X
2
,

X
3
, ... with the
Markov property, namely that, given the present state, the future and
past states are independent.

HMM (Hidden Markov Model):

Probabilistic parameters of a hidden Markov model (example)

x



states

y



possible observations

a



state transition probabilities

b



output probabilities

The E. Coli gene model


The complex gene model

A lot of information is encoded in a gene sequence

Shipping address

(SRP binding site)

Expiration time
(ubiquitin binding site)

microRNA

binding site

Enhancers

Suppressors

Promoter region

Prediction of pos
-
translational
modification

Prediction of subcellular location

Predict processing, degradation
and antigen presentation

Protein translocation


You tube link:


http://www.youtube.com/watch?v=PUy_Em5dXmc&feature=related


G
-
protein coupled receptors

Training and testing the HMM

HMM
vs.

hydropathy plot