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Title: Combining Hadoop with MPI to Solve Metagenomics Problems that are both Data- and Compute-intensive

Abstract

Metagenomics, the study of all microbial species cohabitants in an environment, usually produces large amount of sequence data varying from several GBs to a few TBs. Analyzing metagenomics data includes both data-intensive and compute-intensive steps, making the entire process hard to scale. Here we aim to optimize a metagenomics application that partitions the shortgun metagenomics sequences based on their species of origin. Our solution combines MapReduce-based BioPig analytic toolkit with MPI to provide scalability in respective to both data and compute. We also made some improvements to the existing BioPig toolkit by using simplified data types and compressed k-mer storage. These optimizations leads up to 193× speedup for the computing-intensive step and 9.6× speedup over the entire pipeline. Our optimized application is also capable of processing datasets that are 16 times larger on the same hardware platform. These conclusions indicate integrating heterogeneous technologies such as Hadoop and MPI is quite efficient to solve large genomics problems that are both data-intensive and compute-intensive.

Authors:
ORCiD logo [1];  [1];  [2];  [1];  [2];  [1];  [1];  [1];  [1]
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  1. Univ. of Science and Technology of China, Hefei (China)
  2. Lawrence Berkeley National Lab. (LBNL), Berkeley, CA (United States)
Publication Date:
Research Org.:
Lawrence Berkeley National Lab. (LBNL), Berkeley, CA (United States)
Sponsoring Org.:
USDOE Office of Science (SC); National Key Research and Development Program of China
OSTI Identifier:
1532334
Grant/Contract Number:  
AC02-05CH11231
Resource Type:
Accepted Manuscript
Journal Name:
International Journal of Parallel Programming
Additional Journal Information:
Journal Volume: 46; Journal Issue: 4; Journal ID: ISSN 0885-7458
Publisher:
Springer
Country of Publication:
United States
Language:
English
Subject:
97 MATHEMATICS AND COMPUTING; Metagenomics; Hadoop; MPI; Optimization; Pig Latin; BioPig; Big data; Data-intensive; Compute-intensive

Citation Formats

Lin, Han, Su, Zhichao, Meng, Xiandong, Jin, Xu, Wang, Zhong, Han, Wenting, An, Hong, Chi, Mengxian, and Wu, Zheng. Combining Hadoop with MPI to Solve Metagenomics Problems that are both Data- and Compute-intensive. United States: N. p., 2017. Web. doi:10.1007/s10766-017-0524-z.
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Lin, Han, Su, Zhichao, Meng, Xiandong, Jin, Xu, Wang, Zhong, Han, Wenting, An, Hong, Chi, Mengxian, & Wu, Zheng. Combining Hadoop with MPI to Solve Metagenomics Problems that are both Data- and Compute-intensive. United States. https://doi.org/10.1007/s10766-017-0524-z
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Lin, Han, Su, Zhichao, Meng, Xiandong, Jin, Xu, Wang, Zhong, Han, Wenting, An, Hong, Chi, Mengxian, and Wu, Zheng. Sat . "Combining Hadoop with MPI to Solve Metagenomics Problems that are both Data- and Compute-intensive". United States. https://doi.org/10.1007/s10766-017-0524-z. https://www.osti.gov/servlets/purl/1532334.
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@article{osti_1532334,
title = {Combining Hadoop with MPI to Solve Metagenomics Problems that are both Data- and Compute-intensive},
author = {Lin, Han and Su, Zhichao and Meng, Xiandong and Jin, Xu and Wang, Zhong and Han, Wenting and An, Hong and Chi, Mengxian and Wu, Zheng},
abstractNote = {Metagenomics, the study of all microbial species cohabitants in an environment, usually produces large amount of sequence data varying from several GBs to a few TBs. Analyzing metagenomics data includes both data-intensive and compute-intensive steps, making the entire process hard to scale. Here we aim to optimize a metagenomics application that partitions the shortgun metagenomics sequences based on their species of origin. Our solution combines MapReduce-based BioPig analytic toolkit with MPI to provide scalability in respective to both data and compute. We also made some improvements to the existing BioPig toolkit by using simplified data types and compressed k-mer storage. These optimizations leads up to 193× speedup for the computing-intensive step and 9.6× speedup over the entire pipeline. Our optimized application is also capable of processing datasets that are 16 times larger on the same hardware platform. These conclusions indicate integrating heterogeneous technologies such as Hadoop and MPI is quite efficient to solve large genomics problems that are both data-intensive and compute-intensive.},
doi = {10.1007/s10766-017-0524-z},
journal = {International Journal of Parallel Programming},
number = 4,
volume = 46,
place = {United States},
year = {2017},
month = {10}
}
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Journal Article:
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Publisher's Version of Record
https://doi.org/10.1007/s10766-017-0524-z
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