Performance of an iterated T-HMM for homology detection.
BACKGROUND
Much information about new protein sequences is derived from identifying homologous proteins. Such tasks are difficult when the evolutionary relationships are distant. Some modern methods achieve better results by building a model of a set of related sequences, and then identifying new proteins that fit the model. A further advance was the development of iterative methods that refine the model as more homologs are discovered. These methods are generally limited by ad hoc methods of sequence weighting, neglect of underlying evolutionary relationships and the representation of the set with a single one-size-fits-all model. These limitations are avoided through the use of a Tree hidden Markov model (T-HMM) approach. Our previous work described how a non-iterative version of the T-HMM method could identify distant homologs with superior performance compared with other non-iterated approaches, and described how this method was particularly appropriate for being implemented as an iterative algorithm.


RESULTS
We describe an iterative version of the T-HMM algorithm, and evaluate its performance for the detection of distant homologs. Significant improvement over other commonly used methods is found.


BACKGROUND
The software (C++, Perl) is available from the corresponding author.
Qian
Bin
B
Biophysics Research Division, University of Michigan, Ann Arbor, MI 48109-1055, USA.
Goldstein
Richard A
RA
D000465:Algorithms; D000595:Amino Acid Sequence; D019143:Evolution, Molecular; D008390:Markov Chains; D008956:Models, Chemical; D015233:Models, Statistical; D008969:Molecular Sequence Data; D011506:Proteins / Q000737:chemistry* / Q000145:classification; D015203:Reproducibility of Results; D012680:Sensitivity and Specificity; D016415:Sequence Alignment / Q000379:methods*; D020539:Sequence Analysis, Protein / Q000379:methods*; D017386:Sequence Homology, Amino Acid; D012984:Software
D011506:Proteins