Bioinformatics lectures at Rice University

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