Gene Sequences Parallel Alignment Model Based on Multiple Inputs and Outputs
Abstract
Bioinformatics computing is a kind of big data processing problem, which usually has the characteristics of large data scale, large computational load and long computational time. Therefore, the use of big data technology in bioinformatics computing has gradually become a research hotspot, and using Hadoop for gene sequence alignment is one of it. It is a common way to use various tools to complete a job in the field of Biocomputing. In most studies of parallel alignment of gene sequences using Hadoop, third-party tools are also needed. However, there are few methods using Hadoop independently to complete gene sequences alignment. Adding data processing with other tools to Hadoop workflow not only affects the improvement of computing performance, but also complicates the application. In this paper, a parallel alignment model of gene sequences based on multiple inputs and outputs is proposed, which can independently complete parallel alignment of gene sequences in Hadoop platform without using other tools. This model not only simplifies the process flow of gene sequence alignment, but also improves the performance compared with other methods. This paper describes in detail the method of manipulating gene sequences with multiple inputs and outputs modes on Hadoop platform and the design of a computing model based on this method, and proves the superiority of this model through experiments.References
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https://doi.org/10.1093/bioinformatics/btv179
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[11] Li, H. (2013). Aligning sequence reads, clone sequences and assembly contigs with BWAMEM, Genomics, 1-3, 2013.
[12] Li, H. (2009). The Sequence Alignment / Map (SAM) Format, Bioinformatics, 25(1-2), 1653-1654, 2009.
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https://doi.org/10.1038/nrg2626
[14] Pandey, R.V.; Schlotterer, C. (2013). DistMap: A Toolkit for Distributed Short Read Mapping on a Hadoop Cluster, PLOS ONE, 8(8), e72614, 2013.
https://doi.org/10.1371/journal.pone.0072614
[15] Pireddu, L.; Leo, S.; Zanetti, G. (2011). SEAL: a distributed short read mapping and duplicate removal tool, Bioinformatics, 27(15), 2159-2160, 2011.
https://doi.org/10.1093/bioinformatics/btr325
[16] Schatz, M.C. (2009). CloudBurst: highly sensitive read mapping with MapReduce, Bioinformatics, 25(11), 1363-1369, 2009.
https://doi.org/10.1093/bioinformatics/btp236
[17] Taylor, R.C. (2010); An overview of the Hadoop/MapReduce/HBase framework and its current applications in bioinformatics, Bmc Bioinformatics, 11(S12), S1, 2010.
https://doi.org/10.1186/1471-2105-11-S12-S1
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https://doi.org/10.1126/science.2181665
[19] Zhang, J.; Wu, Y.Q.; Yi, H.C. (2018). Forward modelling of circular loop source and calculation of whole area apparent resistivity based on TEM, Traitement du Signal, 35(2), 183-198, 2018.
https://doi.org/10.3166/ts.35.183-198
[20] [Online]. Available: hadoop.apache.org/, Accesed on 20 June 2018.
Published
2019-04-14
How to Cite
FENG, Xiaolong; GAO, Jing.
Gene Sequences Parallel Alignment Model Based on Multiple Inputs and Outputs.
INTERNATIONAL JOURNAL OF COMPUTERS COMMUNICATIONS & CONTROL, [S.l.], v. 14, n. 2, p. 141-153, apr. 2019.
ISSN 1841-9844.
Available at: <http://univagora.ro/jour/index.php/ijccc/article/view/3539>. Date accessed: 02 july 2020.
doi: https://doi.org/10.15837/ijccc.2019.2.3539.
Section
Articles
Keywords
Multiple inputs and outputs, MapReduce, gene sequence alignment, short reads mapping, BWA (Burrows-Wheeler aligner), parallel computing
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