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| Content Provider | ACM Digital Library |
|---|---|
| Author | Sun, Yanni Buhler, Jeremy |
| Abstract | Profile HMMs are powerful tools for modeling conserved motifs in proteins. They are widely used by search tools to classify new protein sequences into families based on domain architecture. However, the proliferation of known motifs and new proteomic sequence data poses a computational challenge for search, requiring days of CPU time to annotate an organism's proteome. It is highly desirable to speed up HMM search in large databases. We design PROSITE-like patterns and short profiles that are used as filters to rapidly eliminate protein-motif pairs for which a full profile HMM comparison does not yield a significant match. The design of the pattern-based filters is formulated as a multichoice knapsack problem. Profile-based filters with high sensitivity are extracted from a profile HMM based on their theoretical sensitivity and false positive rate. Experiments show that our profile-based filters achieve high sensitivity (near 100 percent) while keeping around 20\times speedup with respect to the unfiltered search program. Pattern-based filters typically retain at least 90 percent of the sensitivity of the source HMM with 30-40\times speedup. The profile-based filters have sensitivity comparable to the multistage filtering strategy HMMERHEAD [15] and are faster in most of our experiments. |
| Starting Page | 232 |
| Ending Page | 243 |
| Page Count | 12 |
| File Format | |
| ISSN | 15455963 |
| DOI | 10.1109/TCBB.2008.14 |
| Volume Number | 6 |
| Issue Number | 2 |
| Journal | IEEE/ACM Transactions on Computational Biology and Bioinformatics (TCBB) |
| Language | English |
| Publisher | Association for Computing Machinery (ACM) |
| Publisher Date | 2009-04-01 |
| Access Restriction | One Nation One Subscription (ONOS) |
| Subject Keyword | Biology and genetics, bioinformatics databases, sequence similarity search, hidden Markov models. |
| Content Type | Text |
| Resource Type | Article |
| Subject | Genetics Biotechnology Applied Mathematics |
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