Name
Abstract | ||
|---|---|---|
BLAST is the most popular bioinformatics tool and is used to run millions of queries each day. However, evaluating such queries is slow, taking typically minutes on modern workstations. Therefore, continuing evolution of BLAST-by improving its algorithms and optimizations-is essential to improve search times in the face of exponentially increasing collection sizes. We present an optimization to the first stage of the BLAST algorithm specifically designed for protein search. It produces the same results as NCBI-BLAST but in around 59% of the time on Intel-based platforms; we also present results for other popular architectures. Overall, this is a saving of around 15% of the total typical BLAST search time. Our approach uses a deterministic finite automaton (DFA), inspired by the original scheme used in the 1990 BLAST algorithm. The techniques are optimized for modern hardware, making careful use of cache-conscious approaches to improve speed. Our optimized DFA approach has been integrated into a new version of BLAST that is freely available for download at http://www.fsa-blast.org/. |
Year | DOI | Venue |
|---|---|---|
2006 | 10.1089/cmb.2006.13.965 | JOURNAL OF COMPUTATIONAL BIOLOGY |
Keywords | Field | DocType |
BLAST,homology search,sequence alignment | Computer science,Deterministic finite automaton,Workstation,Artificial intelligence,Bioinformatics,Machine learning | Journal |
Volume | Issue | ISSN |
13.0 | 4 | 1066-5277 |
Citations | PageRank | References |
18 | 3.26 | 10 |
Authors | ||
3 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Michael Cameron | 1 | 18 | 3.26 |
| Hugh E. Williams | 2 | 1048 | 93.45 |
| Adam Cannane | 3 | 88 | 7.88 |