SCBI_MapReduce, a New Ruby Task-Farm Skeleton for Automated Parallelisation and Distribution in Chunks of Sequences: The Implementation of a Boosted Blast+. (27th October 2013)

• Record Type: Journal Article
• Authors: Guerrero-Fernández, Darío; Falgueras, Juan; Claros, M. Gonzalo
• Other Names: Merelli Ivan Academic Editor.
• Is Part Of: Computational biology journal. Volume 2013(2013)
• Publication Date: 2013-10-27
• Subjects: Computational biology -- Periodicals; Computational biology; Biología -- Publicaciones periódicas; Ciencias -- Publicaciones periódicas; Periodicals; Electronic journals; 570.285
• DOI: 10.1155/2013/707540
• Languages: English
• ISSNs: 2314-4165

Abstract

Abstract : Current genomic analyses often require the managing and comparison of big data using desktop bioinformatic software that was not developed regarding multicore distribution. The task-farm SCBI_Map Reduce is intended to simplify the trivial parallelisation and distribution of new and legacy software and scripts for biologists who are interested in using computers but are not skilled programmers. In the case of legacy applications, there is no need of modification or rewriting the source code. It can be used from multicore workstations to heterogeneous grids. Tests have demonstrated that speed-up scales almost linearly and that distribution in small chunks increases it. It is also shown that SCBI_Map Reduce takes advantage of shared storage when necessary, is fault-tolerant, allows for resuming aborted jobs, does not need special hardware or virtual machine support, and provides the same results than a parallelised, legacy software. The same is true for interrupted and relaunched jobs. As proof-of-concept, distribution of a compiled version of Blast+ in the SCBI_Distributed _Blast gem is given, indicating that other blast binaries can be used while maintaining the same SCBI_Distributed _Blast code. Therefore, SCBI_Map Reduce suits most parallelisation and distribution needs in, for example, gene and genome studies.