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PSimScan: algorithm and utility for fast protein similarity search.
- Source :
- PLoS ONE, Vol 8, Iss 3, p e58505 (2013)
- Publication Year :
- 2013
- Publisher :
- Public Library of Science (PLoS), 2013.
-
Abstract
- In the era of metagenomics and diagnostics sequencing, the importance of protein comparison methods of boosted performance cannot be overstated. Here we present PSimScan (Protein Similarity Scanner), a flexible open source protein similarity search tool which provides a significant gain in speed compared to BLASTP at the price of controlled sensitivity loss. The PSimScan algorithm introduces a number of novel performance optimization methods that can be further used by the community to improve the speed and lower hardware requirements of bioinformatics software. The optimization starts at the lookup table construction, then the initial lookup table-based hits are passed through a pipeline of filtering and aggregation routines of increasing computational complexity. The first step in this pipeline is a novel algorithm that builds and selects 'similarity zones' aggregated from neighboring matches on small arrays of adjacent diagonals. PSimScan performs 5 to 100 times faster than the standard NCBI BLASTP, depending on chosen parameters, and runs on commodity hardware. Its sensitivity and selectivity at the slowest settings are comparable to the NCBI BLASTP's and decrease with the increase of speed, yet stay at the levels reasonable for many tasks. PSimScan is most advantageous when used on large collections of query sequences. Comparing the entire proteome of Streptocuccus pneumoniae (2,042 proteins) to the NCBI's non-redundant protein database of 16,971,855 records takes 6.5 hours on a moderately powerful PC, while the same task with the NCBI BLASTP takes over 66 hours. We describe innovations in the PSimScan algorithm in considerable detail to encourage bioinformaticians to improve on the tool and to use the innovations in their own software development.
Details
- Language :
- English
- ISSN :
- 19326203
- Volume :
- 8
- Issue :
- 3
- Database :
- Directory of Open Access Journals
- Journal :
- PLoS ONE
- Publication Type :
- Academic Journal
- Accession number :
- edsdoj.0d1ccef4886043c5b6a65ec58ac0190c
- Document Type :
- article
- Full Text :
- https://doi.org/10.1371/journal.pone.0058505