title: A profile hidden Markov model for signal peptides generated by HMMER.
abstract: CONCLUSIONS
  Although the HMMER package is widely used to produce profile hidden Markov models (profile HMMs) for protein domains, it has been difficult to create a profile HMM for signal peptides. Here we describe an approach for building a complex model of eukaryotic signal peptides by the standard HMMER package. Signal peptide prediction with this model gives a 95.6% sensitivity and 95.7% specificity.
  
  
  BACKGROUND
  The profile HMM for signal peptides, data sets, and the scripts for analyzing data are available for non-commercial use at http://share.gene.com/.
authors.affiliation: Department of Bioinformatics, Genentech Inc., South San Francisco, CA 94080, USA. zemin@gene.com
authors.forename: Zemin
authors.initials: Z
authors.lastname: Zhang
authors.forename: William I
authors.initials: WI
authors.lastname: Wood
mesh_terms: D000595:Amino Acid Sequence; D000818:Animals; D005189:False Positive Reactions; D006801:Humans; D008390:Markov Chains; D051379:Mice; D008956:Models, Chemical; D008957:Models, Genetic; D015233:Models, Statistical; D008969:Molecular Sequence Data; D010455:Peptides / Q000737:chemistry; D011487:Protein Conformation; D021382:Protein Sorting Signals; D017434:Protein Structure, Tertiary; D012680:Sensitivity and Specificity; D016415:Sequence Alignment / Q000379:methods*; D020539:Sequence Analysis, Protein / Q000379:methods*
chemical_list: D010455:Peptides; D021382:Protein Sorting Signals