Architecture for interoperable software in biology
Identifieur interne : 000439 ( Pmc/Checkpoint ); précédent : 000438; suivant : 000440Architecture for interoperable software in biology
Auteurs : James Christopher Bare ; Nitin S. BaligaSource :
- Briefings in Bioinformatics [ 1467-5463 ] ; 2012.
Abstract
Understanding biological complexity demands a combination of high-throughput data and interdisciplinary skills. One way to bring to bear the necessary combination of data types and expertise is by encapsulating domain knowledge in software and composing that software to create a customized data analysis environment. To this end, simple flexible strategies are needed for interconnecting heterogeneous software tools and enabling data exchange between them. Drawing on our own work and that of others, we present several strategies for interoperability and their consequences, in particular, a set of simple data structures—list, matrix, network, table and tuple—that have proven sufficient to achieve a high degree of interoperability. We provide a few guidelines for the development of future software that will function as part of an interoperable community of software tools for biological data analysis and visualization.
Url:
DOI: 10.1093/bib/bbs074
PubMed: 23235920
PubMed Central: 4103535
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<record><TEI><teiHeader><fileDesc><titleStmt><title xml:lang="en">Architecture for interoperable software in biology</title><author><name sortKey="Bare, James Christopher" sort="Bare, James Christopher" uniqKey="Bare J" first="James Christopher" last="Bare">James Christopher Bare</name></author><author><name sortKey="Baliga, Nitin S" sort="Baliga, Nitin S" uniqKey="Baliga N" first="Nitin S." last="Baliga">Nitin S. Baliga</name></author></titleStmt><publicationStmt><idno type="wicri:source">PMC</idno><idno type="pmid">23235920</idno><idno type="pmc">4103535</idno><idno type="url">http://www.ncbi.nlm.nih.gov/pmc/articles/PMC4103535</idno><idno type="RBID">PMC:4103535</idno><idno type="doi">10.1093/bib/bbs074</idno><date when="2012">2012</date><idno type="wicri:Area/Pmc/Corpus">000510</idno><idno type="wicri:Area/Pmc/Curation">000510</idno><idno type="wicri:Area/Pmc/Checkpoint">000439</idno></publicationStmt><sourceDesc><biblStruct><analytic><title xml:lang="en" level="a" type="main">Architecture for interoperable software in biology</title><author><name sortKey="Bare, James Christopher" sort="Bare, James Christopher" uniqKey="Bare J" first="James Christopher" last="Bare">James Christopher Bare</name></author><author><name sortKey="Baliga, Nitin S" sort="Baliga, Nitin S" uniqKey="Baliga N" first="Nitin S." last="Baliga">Nitin S. Baliga</name></author></analytic><series><title level="j">Briefings in Bioinformatics</title><idno type="ISSN">1467-5463</idno><idno type="eISSN">1477-4054</idno><imprint><date when="2012">2012</date></imprint></series></biblStruct></sourceDesc></fileDesc><profileDesc><textClass></textClass></profileDesc></teiHeader><front><div type="abstract" xml:lang="en"><p>Understanding biological complexity demands a combination of high-throughput data and interdisciplinary skills. One way to bring to bear the necessary combination of data types and expertise is by encapsulating domain knowledge in software and composing that software to create a customized data analysis environment. To this end, simple flexible strategies are needed for interconnecting heterogeneous software tools and enabling data exchange between them. Drawing on our own work and that of others, we present several strategies for interoperability and their consequences, in particular, a set of simple data structures—list, matrix, network, table and tuple—that have proven sufficient to achieve a high degree of interoperability. We provide a few guidelines for the development of future software that will function as part of an interoperable community of software tools for biological data analysis and visualization.</p></div></front><back><div1 type="bibliography"><listBibl><biblStruct><analytic><author><name sortKey="Stein, Ld" uniqKey="Stein L">LD Stein</name></author></analytic></biblStruct><biblStruct><analytic><author><name sortKey="Stein, Ld" uniqKey="Stein L">LD Stein</name></author></analytic></biblStruct><biblStruct><analytic><author><name sortKey="Hood, L" uniqKey="Hood L">L Hood</name></author></analytic></biblStruct><biblStruct><analytic><author><name sortKey="Madhavan, J" uniqKey="Madhavan J">J Madhavan</name></author><author><name sortKey="Jeffery, S" uniqKey="Jeffery S">S Jeffery</name></author><author><name sortKey="Cohen, S" uniqKey="Cohen S">S Cohen</name></author></analytic></biblStruct><biblStruct><analytic><author><name sortKey="Bonneau, R" uniqKey="Bonneau R">R 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<front><journal-meta><journal-id journal-id-type="nlm-ta">Brief Bioinform</journal-id><journal-id journal-id-type="iso-abbrev">Brief. Bioinformatics</journal-id><journal-id journal-id-type="publisher-id">bib</journal-id><journal-id journal-id-type="hwp">bib</journal-id><journal-title-group><journal-title>Briefings in Bioinformatics</journal-title></journal-title-group><issn pub-type="ppub">1467-5463</issn><issn pub-type="epub">1477-4054</issn><publisher><publisher-name>Oxford University Press</publisher-name></publisher></journal-meta><article-meta><article-id pub-id-type="pmid">23235920</article-id><article-id pub-id-type="pmc">4103535</article-id><article-id pub-id-type="doi">10.1093/bib/bbs074</article-id><article-id pub-id-type="publisher-id">bbs074</article-id><article-categories><subj-group subj-group-type="heading"><subject>Papers</subject></subj-group></article-categories><title-group><article-title>Architecture for interoperable software in biology</article-title></title-group><contrib-group><contrib contrib-type="author" corresp="yes"><name><surname>Bare</surname><given-names>James Christopher</given-names></name><xref ref-type="bio" rid="d35e36">*</xref></contrib><contrib contrib-type="author"><name><surname>Baliga</surname><given-names>Nitin S.</given-names></name><xref ref-type="bio" rid="d35e47">*</xref></contrib></contrib-group><author-notes><corresp>Corresponding author. Nitin S. Baliga, Professor & Director, Institute for Systems Biology, 401 Terry Ave N, Seattle, WA 98109. Tel.: <phone>+206 732 1266</phone>; Fax: <fax>+206 732 1299</fax>; E-mail: <email>nbaliga@systemsbiology.org</email></corresp></author-notes><pub-date pub-type="ppub"><month>7</month><year>2014</year></pub-date><pub-date pub-type="epub"><day>11</day><month>12</month><year>2012</year></pub-date><pub-date pub-type="pmc-release"><day>11</day><month>12</month><year>2012</year></pub-date><pmc-comment> PMC Release delay is 0 months and 0 days and was based on the
<volume>15</volume><issue>4</issue><fpage>626</fpage><lpage>636</lpage><history><date date-type="received"><day>28</day><month>8</month><year>2011</year></date><date date-type="accepted"><day>11</day><month>10</month><year>2011</year></date></history><permissions><copyright-statement>© The Author 2012. Published by Oxford University Press.</copyright-statement><copyright-year>2012</copyright-year><license license-type="creative-commons" xlink:href="http://creativecommons.org/licenses/by-nc/3.0"><license-p>This is an Open Access article distributed under the terms of the Creative Commons Attribution Non-Commercial License (<ext-link ext-link-type="uri" xlink:href="http://creativecommons.org/licenses/by-nc/3.0/">http://creativecommons.org/licenses/by-nc/3.0/</ext-link>), which permits unrestricted non-commercial use, distribution, and reproduction in any medium, provided the original work is properly cited.</license-p></license></permissions><abstract><p>Understanding biological complexity demands a combination of high-throughput data and interdisciplinary skills. One way to bring to bear the necessary combination of data types and expertise is by encapsulating domain knowledge in software and composing that software to create a customized data analysis environment. To this end, simple flexible strategies are needed for interconnecting heterogeneous software tools and enabling data exchange between them. Drawing on our own work and that of others, we present several strategies for interoperability and their consequences, in particular, a set of simple data structures—list, matrix, network, table and tuple—that have proven sufficient to achieve a high degree of interoperability. We provide a few guidelines for the development of future software that will function as part of an interoperable community of software tools for biological data analysis and visualization.</p></abstract><kwd-group><kwd>interoperability</kwd><kwd>software engineering</kwd><kwd>bioinformatics</kwd><kwd>integration</kwd><kwd>systems biology</kwd><kwd>data analysis</kwd></kwd-group><counts><page-count count="1"></page-count></counts></article-meta></front></pmc><affiliations><list></list><tree><noCountry><name sortKey="Baliga, Nitin S" sort="Baliga, Nitin S" uniqKey="Baliga N" first="Nitin S." last="Baliga">Nitin S. Baliga</name><name sortKey="Bare, James Christopher" sort="Bare, James Christopher" uniqKey="Bare J" first="James Christopher" last="Bare">James Christopher Bare</name></noCountry></tree></affiliations></record>Pour manipuler ce document sous Unix (Dilib)
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