A profile hidden Markov model for signal peptides generated by HMMER.
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/.
Zhang
Zemin
Z
Department of Bioinformatics, Genentech Inc., South San Francisco, CA 94080, USA. zemin@gene.com
Wood
William I
WI
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*
D010455:Peptides; D021382:Protein Sorting Signals