Name
Abstract | ||
|---|---|---|
Motivation: Profile hidden Markov models provide a sensitive method for performing sequence database search and aligning multiple sequences. One of the drawbacks of the hidden Markov model is that the conserved amino acids are not emphasized, but signal and noise are treated equally. For this reason, the number of estimated emission parameters is often enormous. Focusing the analysis on conserved residues only should increase the accuracy of sequence database search. Results: We address this issue with a new method for efficient emission probability (EEP) estimation, in which amino acids are divided into effective and ineffective residues at each conserved alignment position. A practical study with 20 protein families demonstrated that the EEP method is capable of detecting family members from other proteins with sensitivity of 98% and specificity of 99% on the average, even if the number of free emission parameters was decreased to 15% of the original. In the database search for TIM barrel sequences, EEP recognizes the family members nearly as accurately as HMMER or Blast, but the number of false positive sequences was significantly less than that obtained with the other methods. |
Year | DOI | Venue |
|---|---|---|
2003 | 10.1093/bioinformatics/btg328 | BIOINFORMATICS |
Keywords | Field | DocType |
amino acid,protein methods,database search,hidden markov model,sequence analysis | Protein family,Sequence database,Protein methods,Computer science,Database search engine,TIM barrel,Bioinformatics,Hidden Markov model,Sequence analysis | Journal |
Volume | Issue | ISSN |
19 | 18.0 | 1367-4803 |
Citations | PageRank | References |
1 | 0.38 | 9 |
Authors | ||
4 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Virpi Ahola | 1 | 38 | 1.66 |
| Tero Aittokallio | 2 | 500 | 34.92 |
| Esa Uusipaikka | 3 | 39 | 2.14 |
| Mauno Vihinen | 4 | 145 | 26.73 |