title: An HMM posterior decoder for sequence feature prediction that includes homology information.
abstract: BACKGROUND
  When predicting sequence features like transmembrane topology, signal peptides, coil-coil structures, protein secondary structure or genes, extra support can be gained from homologs.
  
  
  RESULTS
  We present here a general hidden Markov model (HMM) decoding algorithm that combines probabilities for sequence features of homologs by considering the average of the posterior label probability of each position in a global sequence alignment. The algorithm is an extension of the previously described 'optimal accuracy' decoder, allowing homology information to be used. It was benchmarked using an HMM for transmembrane topology and signal peptide prediction, Phobius. We found that the performance was substantially increased when incorporating information from homologs.
  
  
  BACKGROUND
  A prediction server for transmembrane topology and signal peptides that uses the algorithm is available at http://phobius.cgb.ki.se/poly.html. An implementation of the algorithm is available on request from the authors.
authors.affiliation: Center for Genomics and Bioinformatics, Karolinska Institutet SE-17 177 Stockholm, Sweden.
authors.forename: Lukas
authors.initials: L
authors.lastname: Käll
authors.forename: Anders
authors.initials: A
authors.lastname: Krogh
authors.forename: Erik L L
authors.initials: EL
authors.lastname: Sonnhammer
mesh_terms: D000465:Algorithms; D001185:Artificial Intelligence; D019295:Computational Biology / Q000379:methods*; D030562:Databases, Protein; D020407:Internet; D008390:Markov Chains; D015233:Models, Statistical; D008962:Models, Theoretical; D011336:Probability; D021382:Protein Sorting Signals; D012984:Software
chemical_list: D021382:Protein Sorting Signals