Unformatted text preview: 5
1.0 E 0.1 A = 0.05
G = 0.95
T =0 5 A = 0.4
C = 0.1
G = 0.1
T = 0.4 1.0 0.9
State path: End I 0.1 0.9 C T T C A TG TG A A AG C AG AC G T A A G T C A
EE EEEEEEEEEEEEEEEE 5 I I I I I I I log P
–41.71 Parsing: 46%
11% Figure 1 A toy HMM for 5′ splice site recognition. See text for explanation. ing an intuitive picture. They are at the heart
of a diverse range of programs, including
genefinding, profile searches, multiple
sequence alignment and regulatory site
identification. HMMs are the Legos of computational sequence analysis.
A toy HMM: 5′ splice site recognition
As a simple example, imagine the following
caricature of a 5′ splice-site recognition
problem. Assume we are given a DNA
sequence that begins in an exon, contains
one 5′ splice site and ends in an intron.
The problem is to identify where the switch
from exon to intron occurred—where the
5′ splice site (5′SS) is.
For us to guess intelligent...
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- Spring '09
- DNA, Probability theory, Markov chain, Hidden Markov model, Markov models, state path