Recent development of strategies using multiple sequence alignments (MSA) or profiles to detect remote homologies between proteins has led to a significant increase in the number of proteins whose structures can be generated by comparative modeling methods. However, prediction of the optimal alignment between these highly divergent homologous proteins remains a difficult issue. We present a tool based on a generalized Viterbi algorithm that generates optimal and sub-optimal alignments between a sequence and a Hidden Markov Model. The tool is implemented as a new function within the HMMER package called hmmkalign.
Download full-text PDF |
Source |
---|---|
http://dx.doi.org/10.1093/bioinformatics/btm492 | DOI Listing |
Enter search terms and have AI summaries delivered each week - change queries or unsubscribe any time!