22 4390 4345 4394 4258 4171 parsing 46 posterior

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Unformatted text preview: 5 Start 1.0 E 0.1 A = 0.05 C=0 G = 0.95 T =0 5 A = 0.4 C = 0.1 G = 0.1 T = 0.4 1.0 0.9 Sequence: 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 –41.22 –43.90 –43.45 –43.94 –42.58 –41.71 Parsing: 46% Posterior decoding: 28% 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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