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DIDA: Distributed Indexing Dispatched Alignment.

Mohamadi H, Vandervalk BP, Raymond A, Jackman SD, Chu J, Breshears CP, Birol I - PLoS ONE (2015)

Bottom Line: 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.

View Article: PubMed Central - PubMed

Affiliation: Genome Sciences Centre, British Columbia Cancer Agency, Vancouver, BC, Canada; Department of Bioinformatics, University of British Columbia, Vancouver, BC, Canada; Intel Health and Life Sciences, Intel Corporation, Hillsboro, OR, US.

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.

No MeSH data available.


Related in: MedlinePlus

Scalability of different aligners using DIDA for human draft assembly.
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pone.0126409.g003: Scalability of different aligners using DIDA for human draft assembly.

Mentions: Fig 3 shows the result on the human draft assembly data. Compared to the smaller datasets, for human genome we see better runtime and memory usage scalability, illustrating that DIDA shows better performance on large data due to the overhead of distributed paradigm. That means the overhead of dispatch and merge steps are compensated for large-scale indexing and alignment applications. We have also evaluated the performance of DIDA on the human reference genome (hg19) as a static target set. Table 2 shows the scalability of wall-clock time and indexing peak memory usage of different aligners, except Novoalign (due to its long runtime). As expected, the scalabilities for runtime and memory are similar to the case of non-static target set.


DIDA: Distributed Indexing Dispatched Alignment.

Mohamadi H, Vandervalk BP, Raymond A, Jackman SD, Chu J, Breshears CP, Birol I - PLoS ONE (2015)

Scalability of different aligners using DIDA for human draft assembly.
© Copyright Policy
Related In: Results  -  Collection

License
Show All Figures
getmorefigures.php?uid=PMC4414605&req=5

pone.0126409.g003: Scalability of different aligners using DIDA for human draft assembly.
Mentions: Fig 3 shows the result on the human draft assembly data. Compared to the smaller datasets, for human genome we see better runtime and memory usage scalability, illustrating that DIDA shows better performance on large data due to the overhead of distributed paradigm. That means the overhead of dispatch and merge steps are compensated for large-scale indexing and alignment applications. We have also evaluated the performance of DIDA on the human reference genome (hg19) as a static target set. Table 2 shows the scalability of wall-clock time and indexing peak memory usage of different aligners, except Novoalign (due to its long runtime). As expected, the scalabilities for runtime and memory are similar to the case of non-static target set.

Bottom Line: 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.

View Article: PubMed Central - PubMed

Affiliation: Genome Sciences Centre, British Columbia Cancer Agency, Vancouver, BC, Canada; Department of Bioinformatics, University of British Columbia, Vancouver, BC, Canada; Intel Health and Life Sciences, Intel Corporation, Hillsboro, OR, US.

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.

No MeSH data available.


Related in: MedlinePlus