CISC 667 Intro to Bioinformatics (Fall 2005) Hidden Markov Models (IV)

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Presentation transcript:

CISC 667 Intro to Bioinformatics (Fall 2005) Hidden Markov Models (IV) Profile HMMs GeneScan TMMOD CISC667, F05, Lec13, Liao

CISC667, F05, Lec13, Liao

CISC667, F05, Lec13, Liao

CISC667, F05, Lec13, Liao

CISC667, F05, Lec13, Liao

CISC667, F05, Lec13, Liao

CISC667, F05, Lec13, Liao

CISC667, F05, Lec13, Liao

CISC667, F05, Lec13, Liao

CISC667, F05, Lec13, Liao

CISC667, F05, Lec13, Liao

CISC667, F05, Lec13, Liao

GENSCAN (generalized HMMs) Chris Burge, PhD Thesis ’97, Stanford http://genes.mit.edu/GENSCAN.html Four components A vector π of initial probabilities A matrix T of state transition probabilities A set of length distribution f A set of sequence generating models P Generalized HMMs: at each state, emission is not symbols (or residues), rather, it is a fragment of sequence. Modified viterbi algorithm CISC667, F05, Lec13, Liao

CISC667, F05, Lec13, Liao

Initial state probabilities As frequency for each functional unit to occur in actual genomic data. E.g., as ~ 80% portion are non-coding intergenic regions, the initial probability for state N is 0.80 Transition probabilities State length distributions CISC667, F05, Lec13, Liao

Training data 2.5 Mb human genomic sequences 380 genes, 142 single-exon genes, 1492 exons and 1254 introns 1619 cDNAs CISC667, F05, Lec13, Liao

Open areas for research Model building Integration of domain knowledge, such as structural information, into profile HMMs Meta learning? Biological mechanism DNA replication Hybrid models Generalized HMM … CISC667, F05, Lec13, Liao

TMMOD: An improved hidden Markov model for predicting transmembrane topology CISC667, F05, Lec13, Liao

TMHMM by Krogh, A. et al JMB 305(2001)567-580 Non-cytoplasmic side Cytoplasmic side membrane Cap cyt Helix core Cap Non-cyt Long loop Non-cyt globular globular Loop cyt Long loop Non-cyt globular Cap cyt Helix core Cap Non-cyt Accuracy of prediction for topology: 78% CISC667, F05, Lec13, Liao

CISC667, F05, Lec13, Liao

CISC667, F05, Lec13, Liao Mod. Reg. Data set Correct topology location Sens- itivity Speci- ficity TMMOD 1 (a) (b) (c) S-83 65 (78.3%) 51 (61.4%) 64 (77.1%) 67 (80.7%) 52 (62.7%) 97.4% 71.3% 97.1% TMMOD 2 61 (73.5%) 54 (65.1%) 66 (79.5%) 99.4% 93.8% 99.7% TMMOD 3 70 (84.3%) 74 (89.2%) 71 (85.5%) 98.2% 95.3% 99.1% TMHMM 69 (83.1%) 96.2% PHDtm (85.5%) (88.0%) 98.8% 95.2% S-160 117 (73.1%) 92 (57.5%) 128 (80.0%) 103 (64.4%) 126 (78.8%) 77.4% 96.1% 97.0% 80.8% 96.7% 120 (75.0%) 97 (60.6%) 118 (73.8%) 132 (82.5%) 121 (75.6%) 135 (84.4%) 98.4% 97.7% 97.2% 95.6% 110 (68.8%) 135 (84.4%) 133 (83.1%) 124 (77.5%) 143 (89.4%) 97.8% 94.5% 98.3% 97.6% 98.1% 123 (76.9%) 134 (83.8%) CISC667, F05, Lec13, Liao