Description: 

At the core of our most advanced trans-membrane predictors lies our implementation of the sequence-profile Hidden Markov Models developed by Pier Luigi "Gigi" Martelli et al. [Bioinformatics 18 (2002) S46-S53] , that devised how to compare a protein sequence-profile to a sequence-profile based HMM. 99HMMers takes advantage of profiles generated by multiple sequence alignments, instead of a single-sequence, as tools such as SAM or HMMER do. This improvement allows evolutionary information about the query sequence to be exploited by the HMM machinery boosting the HMM sensitivity and accuracy. We call it the 99HMMer . The 99HMMers is our growing collections of family-specific sequence-profile HMM models, devoted to the detection and the analysis of trans-membrane protein sequences, such as GPCRs.

License: BioDec