Evidence map›Paper›PMID 26731399›Full record

ArticlePloS one2016

Parallel and Scalable Short-Read Alignment on Multi-Core Clusters Using UPC+.

Jorge González-Domínguez, Yongchao Liu, Bertil Schmidt

Abstract read
In one paragraph

Article in PloS one, 2016. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

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0citing papers in PubMed
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1 · What the graph read from it

What it found

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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

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3 · Its place in the literature

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0 citing papers in PubMed.

No citing paper in PubMed yet.

4 · The record

Corrections and comments

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5 · Who and what money

Authors and funding

3 authors.

Jorge González-DomínguezParallel and Distributed Architectures Group, Johannes Gutenberg University Mainz, Mainz, Germany.
Yongchao LiuSchool of Computational Science & Engineering, Georgia Institute of Technology, Atlanta, Georgia, United States of America.
Bertil SchmidtParallel and Distributed Architectures Group, Johannes Gutenberg University Mainz, Mainz, Germany.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

The growth of next-generation sequencing (NGS) datasets poses a challenge to the alignment of reads to reference genomes in terms of alignment quality and execution speed. Some available aligners have been shown to obtain high quality mappings at the expense of long execution times. Finding fast yet accurate software solutions is of high importance to research, since availability and size of NGS datasets continue to increase. In this work we present an efficient parallelization approach for NGS short-read alignment on multi-core clusters. Our approach takes advantage of a distributed shared memory programming model based on the new UPC++ language. Experimental results using the CUSHAW3 aligner show that our implementation based on dynamic scheduling obtains good scalability on multi-core clusters. Through our evaluation, we are able to complete the single-end and paired-end alignments of 246 million reads of length 150 base-pairs in 11.54 and 16.64 minutes, respectively, using 32 nodes with four AMD Opteron 6272 16-core CPUs per node. In contrast, the multi-threaded original tool needs 2.77 and 5.54 hours to perform the same alignments on the 64 cores of one node. The source code of our parallel implementation is publicly available at the CUSHAW3 homepage (http://cushaw3.sourceforge.net).

Indexed as

Programming LanguagesAlgorithmsComputational BiologyGenome, HumanHigh-Throughput Nucleotide SequencingHumansInternetReproducibility of ResultsSequence Alignment

Identifiers

PMID26731399
PMCPMC4711716

What OpenQuestion holds

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LicenceCC BY
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Registered trials

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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.