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
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