RepMaestro: scalable repeat detection on disk-based genome sequences
AuthorAskitis, N; Sinha, R
PublisherOXFORD UNIV PRESS
AffiliationFaculty of Engineering, Computer Science and Software Engineering
Document TypeJournal Article
CitationsAskitis, N. & Sinha, R. (2010). RepMaestro: scalable repeat detection on disk-based genome sequences. BIOINFORMATICS, 26 (19), pp.2368-2374. https://doi.org/10.1093/bioinformatics/btq433.
Access StatusThis item is currently not available from this repository
MOTIVATION: We investigate the problem of exact repeat detection on large genomic sequences. Most existing approaches based on suffix trees and suffix arrays (SAs) are limited either to small sequences or those that are memory resident. We introduce RepMaestro, a software that adapts existing in-memory-enhanced SA algorithms to enable them to scale efficiently to large sequences that are disk resident. Supermaximal repeats, maximal unique matches (MuMs) and pairwise branching tandem repeats have been used to demonstrate the practicality of our approach; the first such study to use an enhanced SA to detect these repeats in large genome sequences. RESULTS: The detection of supermaximal repeats was observed to be up to two times faster than Vmatch, but more importantly, was shown to scale efficiently to large genome sequences that Vmatch could not process due to memory constraints (4 GB). Similar results were observed for the detection of MuMs, with RepMaestro shown to scale well and also perform up to six times faster than Vmatch. For tandem repeats, RepMaestro was found to be slower but could nonetheless scale to large disk-resident sequences. These results are a significant advance in the quest of scalable repeat detection. Software availability: RepMaestro is available at http://www.naskitis.com.
Keywordsgenome analysis; repeats; supermaximal repeat; tandem repeat; maximal unique matches; enhanced suffix array
- Click on "Export Reference in RIS Format" and choose "open with... Endnote".
- Click on "Export Reference in RIS Format". Login to Refworks, go to References => Import References