Abstract

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.

Highlights

  • Performing fast and accurate alignments of reads generated by modern sequencing technologies represents an active field of research

  • In order to assess the performance of DIDA for each aligner on non-static targets, we assembled the reads from each dataset using ABySS 1.3.7, and used the assembly graph in intermediate stages to guide partitioning

  • We have evaluated the performance of DIDA for each aligner on human genome reference as a static target

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Summary

Introduction

Performing fast and accurate alignments of reads generated by modern sequencing technologies represents an active field of research. Most modern algorithms in this domain work by first constructing an index of the target and/or the query sequences This index may be in the form of a suffix tree [1, 2], suffix array [3, 4], hash table [5,6,7,8,9,10,11,12,13], or full-text minute-space index (FM-index) [14,15,16,17,18,19,20]. This pre-processing step introduces an PLOS ONE | DOI:10.1371/journal.pone.0126409 April 29, 2015

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