Hidden Markov models of biological primary sequence information.
Hidden Markov model (HMM) techniques are used to model families of biological sequences. A smooth and convergent algorithm is introduced to iteratively adapt the transition and emission parameters of the models from the examples in a given family. The HMM approach is applied to three protein families: globins, immunoglobulins, and kinases. In all cases, the models derived capture the important statistical characteristics of the family and can be used for a number of tasks, including multiple alignments, motif detection, and classification. For K sequences of average length N, this approach yields an effective multiple-alignment algorithm which requires O(KN2) operations, linear in the number of sequences.
Baldi
P
P
Division of Biology, California Institute of Technology, Pasadena 91125.
Chauvin
Y
Y
Hunkapiller
T
T
McClure
M A
MA
D000465:Algorithms; D000595:Amino Acid Sequence; D000818:Animals; D005914:Globins / Q000235:genetics; D006801:Humans; D007136:Immunoglobulins / Q000235:genetics; D008390:Markov Chains; D008957:Models, Genetic; D008969:Molecular Sequence Data; D011494:Protein Kinases / Q000235:genetics; D011506:Proteins / Q000235:genetics*; D016415:Sequence Alignment / Q000379:methods* / Q000706:statistics & numerical data; D017386:Sequence Homology, Amino Acid
D007136:Immunoglobulins; D011506:Proteins; D005914:Globins; D011494:Protein Kinases