title: HBLAST: Parallelised sequence similarity--A Hadoop MapReducable basic local alignment search tool.
abstract: The recent exponential growth of genomic databases has resulted in the common task of sequence alignment becoming one of the major bottlenecks in the field of computational biology. It is typical for these large datasets and complex computations to require cost prohibitive High Performance Computing (HPC) to function. As such, parallelised solutions have been proposed but many exhibit scalability limitations and are incapable of effectively processing "Big Data" - the name attributed to datasets that are extremely large, complex and require rapid processing. The Hadoop framework, comprised of distributed storage and a parallelised programming framework known as MapReduce, is specifically designed to work with such datasets but it is not trivial to efficiently redesign and implement bioinformatics algorithms according to this paradigm. The parallelisation strategy of "divide and conquer" for alignment algorithms can be applied to both data sets and input query sequences. However, scalability is still an issue due to memory constraints or large databases, with very large database segmentation leading to additional performance decline. Herein, we present Hadoop Blast (HBlast), a parallelised BLAST algorithm that proposes a flexible method to partition both databases and input query sequences using "virtual partitioning". HBlast presents improved scalability over existing solutions and well balanced computational work load while keeping database segmentation and recompilation to a minimum. Enhanced BLAST search performance on cheap memory constrained hardware has significant implications for in field clinical diagnostic testing; enabling faster and more accurate identification of pathogenic DNA in human blood or tissue samples.
authors.affiliation: Department of Computing, Cork Institute of Technology, Rossa Avenue, Bishopstown, Cork, Ireland. Electronic address: aisling.odriscoll@cit.ie.
authors.forename: Aisling
authors.initials: A
authors.lastname: O'Driscoll
authors.affiliation: Department of Computing, Cork Institute of Technology, Rossa Avenue, Bishopstown, Cork, Ireland.
authors.forename: Vladislav
authors.initials: V
authors.lastname: Belogrudov
authors.affiliation: NSilico Lifescience Ltd., Rubicon Centre, Bishopstown, Cork, Ireland.
authors.forename: John
authors.initials: J
authors.lastname: Carroll
authors.affiliation: Division of Pathway Medicine, University of Edinburgh Medical School, The Chancellor's Building, 49 Little France Crescent, Edinburgh EH16 4SB, United Kingdom.
authors.forename: Kai
authors.initials: K
authors.lastname: Kropp
authors.affiliation: NSilico Lifescience Ltd., Rubicon Centre, Bishopstown, Cork, Ireland.
authors.forename: Paul
authors.initials: P
authors.lastname: Walsh
authors.affiliation: Division of Pathway Medicine, University of Edinburgh Medical School, The Chancellor's Building, 49 Little France Crescent, Edinburgh EH16 4SB, United Kingdom.
authors.forename: Peter
authors.initials: P
authors.lastname: Ghazal
authors.affiliation: Department of Biological Sciences, Cork Institute of Technology, Rossa Avenue, Bishopstown, Cork, Ireland.
authors.forename: Roy D
authors.initials: RD
authors.lastname: Sleator
mesh_terms: D000465:Algorithms; D003205:Computing Methodologies; D016208:Databases, Factual; D030541:Databases, Genetic; D005821:Genetic Techniques; D023281:Genomics / Q000379:methods*; D006801:Humans; D020407:Internet; D016415:Sequence Alignment / Q000379:methods*; D012984:Software
keywords: Big data; Bioinformatics; Cloud computing; Genomics; Hadoop; Sequence alignment