Name
Title | ||
|---|---|---|
The effectiveness of position- and composition-specific gap costs for protein similarity searches. |
Abstract | ||
|---|---|---|
The flexibility in gap cost enjoyed by hidden Markov models (HMMs) is expected to afford them better retrieval accuracy than position-specific scoring matrices (PSSMs). We attempt to quantify the effect of more general gap parameters by separately examining the influence of position- and composition-specific gap scores, as well as by comparing the retrieval accuracy of the PSSMs constructed using an iterative procedure to that of the HMMs provided by Pfam and SUPERFAMILY, curated ensembles of multiple alignments.We found that position-specific gap penalties have an advantage over uniform gap costs. We did not explore optimizing distinct uniform gap costs for each query. For Pfam, PSSMs iteratively constructed from seeds based on HMM consensus sequences perform equivalently to HMMs that were adjusted to have constant gap transition probabilities, albeit with much greater variance. We observed no effect of composition-specific gap costs on retrieval performance. These results suggest possible improvements to the PSI-BLAST protein database search program.The scripts for performing evaluations are available upon request from the authors. |
Year | DOI | Venue |
|---|---|---|
2008 | 10.1093/bioinformatics/btn171 | ISMB |
Keywords | Field | DocType |
composition-specific gap cost,distinct uniform gap cost,retrieval accuracy,constant gap transition probability,retrieval performance,protein similarity search,position-and composition-specific gap cost,position-specific gap penalty,uniform gap cost,better retrieval accuracy,position-and composition-specific gap score,general gap parameter,hidden markov model,proteins,artificial intelligence,amino acid sequence,multiple alignment,algorithms,sequence alignment,similarity search,markov chains,database search,quantitative method,transition probability | Data mining,SUPERFAMILY,Computer science,Markov chain,Artificial intelligence,Bioinformatics,Hidden Markov model,Machine learning | Conference |
Volume | Issue | ISSN |
24 | 13 | 1367-4811 |
Citations | PageRank | References |
3 | 0.43 | 19 |
Authors | ||
4 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Aleksandar Stojmirović | 1 | 85 | 7.85 |
| E. Michael Gertz | 2 | 204 | 27.45 |
| Stephen F Altschul | 3 | 180 | 26.55 |
| Yi-Kuo Yu | 4 | 140 | 14.43 |