DIDA: Distributed Indexing Dispatched Alignment

One essential application in bioinformatics that is affected by the high-throughput sequencing data deluge is the sequence alignment problem, where nucleotide or amino acid sequences are queried against targets to find regions of close similarity. When queries are too many and/or targets are too lar...

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Published in:PloS one Vol. 10; no. 4; p. e0126409
Main Authors: Mohamadi, Hamid, Vandervalk, Benjamin P, Raymond, Anthony, Jackman, Shaun D, Chu, Justin, Breshears, Clay P, Birol, Inanc
Format: Journal Article
Language:English
Published: United States Public Library of Science 29.04.2015
Public Library of Science (PLoS)
Subjects:
Alignment
Amino acid sequence
Amino acids
Assembly
Bioinformatics
Cancer
Computational biology
Computational Biology - methods
Computer programs
Data processing
Databases, Genetic
DNA sequencing
Genomes
Humans
Indexing
Indexing (Content analysis)
Methods
Next-generation sequencing
Nucleotide sequence
Preprocessing
Queries
Sequence Alignment - methods
Software
Workflow
Canada
United States
ISSN:1932-6203, 1932-6203
Online Access:Get full text
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Summary:One essential application in bioinformatics that is affected by the high-throughput sequencing data deluge is the sequence alignment problem, where nucleotide or amino acid sequences are queried against targets to find regions of close similarity. When queries are too many and/or targets are too large, the alignment process becomes computationally challenging. This is usually addressed by preprocessing techniques, where the queries and/or targets are indexed for easy access while searching for matches. When the target is static, such as in an established reference genome, the cost of indexing is amortized by reusing the generated index. However, when the targets are non-static, such as contigs in the intermediate steps of a de novo assembly process, a new index must be computed for each run. To address such scalability problems, we present DIDA, a novel framework that distributes the indexing and alignment tasks into smaller subtasks over a cluster of compute nodes. It provides a workflow beyond the common practice of embarrassingly parallel implementations. DIDA is a cost-effective, scalable and modular framework for the sequence alignment problem in terms of memory usage and runtime. It can be employed in large-scale alignments to draft genomes and intermediate stages of de novo assembly runs. The DIDA source code, sample files and user manual are available through http://www.bcgsc.ca/platform/bioinfo/software/dida. The software is released under the British Columbia Cancer Agency License (BCCA), and is free for academic use.
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Competing Interests: Clay P. Breshears is employed by Intel Corporation, Health and Life Sciences group. Intel had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript. This does not alter the authors’ adherence to PLOS ONE policies on sharing data and materials.
Conceived and designed the experiments: HM BV AR IB. Performed the experiments: HM BV AR. Analyzed the data: HM BV AR. Contributed reagents/materials/analysis tools: HM BV AR SJ JC CB IB. Wrote the paper: HM BV AR SJ JC CB IB. Designed the parallel and distributed algorithm: HM IB. Developed, implemented, and improved the software tool: HM BV AR JC CB. Modified the tool, performed the new experiments for revised version, and addressed the reviewers’ comments: HM BV IB.
ISSN:1932-6203
1932-6203
DOI:10.1371/journal.pone.0126409