The effects of ordered-series-of-motifs anchoring and sub-class modeling on the generation of HMMs representing highly divergent protein sequences.
Hidden Markov Models (HMMs) provide a flexible method for representing protein sequence data. Highly divergent data require a more complex approach to HMM generation than previously demonstrated. We describe a strategy of motif anchoring and sub-class modeling that aids in the construction of more informative HMMs as determined by a new algorithm called a stability measure.
Mcclure
M A
MA
Department of Biological Sciences, UNLV, Las Vegas, NV 89129, USA.
Kowalski
J
J
D000465:Algorithms; D000595:Amino Acid Sequence; D019295:Computational Biology / Q000379:methods; D003198:Computer Simulation; D016208:Databases, Factual; D008390:Markov Chains; D011506:Proteins / Q000737:chemistry* / Q000235:genetics*; D018626:Retroelements; D016415:Sequence Alignment; D017386:Sequence Homology, Amino Acid; D012984:Software
D011506:Proteins; D018626:Retroelements