Architecture for interoperable software in biology
Identifieur interne : 000527 ( Main/Exploration ); précédent : 000526; suivant : 000528Architecture 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
Affiliations:
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<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>
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