Compression of Large genomic datasets using COMRAD on Parallel Computing Platform
Identifieur interne : 000559 ( Pmc/Checkpoint ); précédent : 000558; suivant : 000560Compression of Large genomic datasets using COMRAD on Parallel Computing Platform
Auteurs : Christopher Leela Biji ; Manu K. Madhu ; Vineetha Vishnu ; Satheesh Kumar K ; Achuthsankar S. NairSource :
- Bioinformation [ 0973-8894 ] ; 2015.
Abstract
The big data storage is a challenge in a post genome era. Hence, there is a need for high performance computing solutions for managing large genomic data. Therefore, it is of interest to describe a parallel-computing approach using message-passing library for distributing the different compression stages in clusters. The genomic compression helps to reduce the on disk“foot print” of large data volumes of sequences. This supports the computational infrastructure for a more efficient archiving. The approach was shown to find utility in 21 Eukaryotic genomes using stratified sampling in this report. The method achieves an average of 6-fold disk space reduction with three times better compression time than COMRAD.
The source codes are written in C using message passing libraries and are available at
Url:
DOI: 10.6026/97320630011267
PubMed: 26124572
PubMed Central: 4464544
Affiliations:
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<author><name sortKey="Biji, Christopher Leela" sort="Biji, Christopher Leela" uniqKey="Biji C" first="Christopher Leela" last="Biji">Christopher Leela Biji</name>
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<series><title level="j">Bioinformation</title>
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<front><div type="abstract" xml:lang="en"><p>The big data storage is a challenge in a post genome era. Hence, there is a need for high performance computing solutions for
managing large genomic data. Therefore, it is of interest to describe a parallel-computing approach using message-passing library
for distributing the different compression stages in clusters. The genomic compression helps to reduce the on disk“foot print” of
large data volumes of sequences. This supports the computational infrastructure for a more efficient archiving. The approach was
shown to find utility in 21 Eukaryotic genomes using stratified sampling in this report. The method achieves an average of 6-fold
disk space reduction with three times better compression time than COMRAD.</p>
<sec id="sb1e"><title>Availability</title>
<p>The source codes are written in C using message passing libraries and are available at <ext-link ext-link-type="uri" xlink:href="https://sourceforge.net/projects/comradmpi/files/COMRADMPI/">https:// sourceforge.net/ projects/ comradmpi/files / COMRADMPI/</ext-link>
</p>
</sec>
</div>
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<pmc article-type="research-article"><pmc-dir>properties open_access</pmc-dir>
<front><journal-meta><journal-id journal-id-type="nlm-ta">Bioinformation</journal-id>
<journal-id journal-id-type="iso-abbrev">Bioinformation</journal-id>
<journal-id journal-id-type="publisher-id">Bioinformation</journal-id>
<journal-title-group><journal-title>Bioinformation</journal-title>
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<issn pub-type="ppub">0973-8894</issn>
<issn pub-type="epub">0973-2063</issn>
<publisher><publisher-name>Biomedical Informatics</publisher-name>
</publisher>
</journal-meta>
<article-meta><article-id pub-id-type="pmid">26124572</article-id>
<article-id pub-id-type="pmc">4464544</article-id>
<article-id pub-id-type="publisher-id">97320630011267</article-id>
<article-id pub-id-type="doi">10.6026/97320630011267</article-id>
<article-categories><subj-group subj-group-type="heading"><subject>Software</subject>
</subj-group>
</article-categories>
<title-group><article-title>Compression of Large genomic datasets using COMRAD on Parallel Computing Platform</article-title>
</title-group>
<contrib-group><contrib contrib-type="author"><name><surname>Biji</surname>
<given-names>Christopher Leela</given-names>
</name>
<xref ref-type="aff" rid="A1">1</xref>
</contrib>
<contrib contrib-type="author"><name><surname>Madhu</surname>
<given-names>Manu K</given-names>
</name>
<xref ref-type="aff" rid="A2">2</xref>
</contrib>
<contrib contrib-type="author"><name><surname>Vishnu</surname>
<given-names>Vineetha</given-names>
</name>
<xref ref-type="aff" rid="A3">3</xref>
</contrib>
<contrib contrib-type="author"><name><surname>K</surname>
<given-names>Satheesh Kumar</given-names>
</name>
<xref ref-type="aff" rid="A4">4</xref>
</contrib>
<contrib contrib-type="author"><name><surname>Vijayakumar</surname>
<given-names></given-names>
</name>
<xref ref-type="aff" rid="A2">2</xref>
</contrib>
<contrib contrib-type="author"><name><surname>Nair</surname>
<given-names>Achuthsankar S</given-names>
</name>
<xref ref-type="aff" rid="A1">1</xref>
<xref ref-type="corresp" rid="COR1">*</xref>
</contrib>
<aff id="A1"><label>1</label>
Department of Computational Biology and Bioinformatics, University of Kerala, Thiruvananthapuram</aff>
<aff id="A2"><label>2</label>
School of Computer Science, Mahathma Gandhi University, Kottayam</aff>
<aff id="A3"><label>3</label>
Infosys Technologies, Trivandrum</aff>
<aff id="A4"><label>4</label>
Department of Future Studies, University of Kerala, Thiruvananthapuram</aff>
</contrib-group>
<author-notes><corresp id="COR1"><label>*</label>
Achuthsankar: <email>sankar.achuth@gmail.com</email>
</corresp>
</author-notes>
<pub-date pub-type="collection"><year>2015</year>
</pub-date>
<pub-date pub-type="epub"><day>28</day>
<month>5</month>
<year>2015</year>
</pub-date>
<volume>11</volume>
<issue>5</issue>
<fpage>267</fpage>
<lpage>271</lpage>
<history><date date-type="received"><day>04</day>
<month>5</month>
<year>2015</year>
</date>
<date date-type="rev-recd"><day>06</day>
<month>5</month>
<year>2015</year>
</date>
<date date-type="accepted"><day>06</day>
<month>5</month>
<year>2015</year>
</date>
</history>
<permissions><copyright-statement>© 2015 Biomedical Informatics</copyright-statement>
<copyright-year>2015</copyright-year>
<license license-type="open-access"><license-p>This is an open-access article, which permits unrestricted use, distribution, and reproduction in any medium,
for non-commercial purposes, provided the original author and source are credited.</license-p>
</license>
</permissions>
<abstract><p>The big data storage is a challenge in a post genome era. Hence, there is a need for high performance computing solutions for
managing large genomic data. Therefore, it is of interest to describe a parallel-computing approach using message-passing library
for distributing the different compression stages in clusters. The genomic compression helps to reduce the on disk“foot print” of
large data volumes of sequences. This supports the computational infrastructure for a more efficient archiving. The approach was
shown to find utility in 21 Eukaryotic genomes using stratified sampling in this report. The method achieves an average of 6-fold
disk space reduction with three times better compression time than COMRAD.</p>
<sec id="sb1e"><title>Availability</title>
<p>The source codes are written in C using message passing libraries and are available at <ext-link ext-link-type="uri" xlink:href="https://sourceforge.net/projects/comradmpi/files/COMRADMPI/">https:// sourceforge.net/ projects/ comradmpi/files / COMRADMPI/</ext-link>
</p>
</sec>
</abstract>
<kwd-group><kwd>Genome compression</kwd>
<kwd>Sequence analysis</kwd>
<kwd>Parallel Computing</kwd>
<kwd>Big data storage</kwd>
<kwd>Genome Analysis</kwd>
</kwd-group>
</article-meta>
</front>
<floats-group><fig id="F1" position="float"><label>Figure 1</label>
<caption><p> Flow Chart of Compression of Large Genome Dataset
using COMRAD on Parallel Computing Platform</p>
</caption>
<graphic xlink:href="97320630011267F1"></graphic>
</fig>
<fig id="F2" position="float"><label>Figure 2</label>
<caption><p>An example showing the code book generation for an
input string with string length, L=2 and threshold frequency
F=2</p>
</caption>
<graphic xlink:href="97320630011267F2"></graphic>
</fig>
<fig id="F3" position="float"><label>Figure 3</label>
<caption><p>Compression time and speedups for Compression of
Large Genomic Datasets using COMRAD on Parallel
Computing Platform for selected sample data set with size in
MB as a function of number of processors.</p>
</caption>
<graphic xlink:href="97320630011267F3"></graphic>
</fig>
</floats-group>
</pmc>
<affiliations><list></list>
<tree><noCountry><name sortKey="Biji, Christopher Leela" sort="Biji, Christopher Leela" uniqKey="Biji C" first="Christopher Leela" last="Biji">Christopher Leela Biji</name>
<name sortKey="K, Satheesh Kumar" sort="K, Satheesh Kumar" uniqKey="K S" first="Satheesh Kumar" last="K">Satheesh Kumar K</name>
<name sortKey="Madhu, Manu K" sort="Madhu, Manu K" uniqKey="Madhu M" first="Manu K" last="Madhu">Manu K. Madhu</name>
<name sortKey="Nair, Achuthsankar S" sort="Nair, Achuthsankar S" uniqKey="Nair A" first="Achuthsankar S" last="Nair">Achuthsankar S. Nair</name>
<name sortKey="Vishnu, Vineetha" sort="Vishnu, Vineetha" uniqKey="Vishnu V" first="Vineetha" last="Vishnu">Vineetha Vishnu</name>
</noCountry>
</tree>
</affiliations>
</record>
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