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Extracting patterns of database and software usage from the bioinformatics literature.
Duck, Geraint; Nenadic, Goran; Brass, Andy; Robertson, David L; Stevens, Robert.
Afiliação
  • Duck G; School of Computer Science, Manchester Institute of Biotechnology and Computational and Evolutionary Biology, Faculty of Life Sciences, The University of Manchester, Manchester M13 9PL, UK.
  • Nenadic G; School of Computer Science, Manchester Institute of Biotechnology and Computational and Evolutionary Biology, Faculty of Life Sciences, The University of Manchester, Manchester M13 9PL, UK School of Computer Science, Manchester Institute of Biotechnology and Computational and Evolutionary Biology, F
  • Brass A; School of Computer Science, Manchester Institute of Biotechnology and Computational and Evolutionary Biology, Faculty of Life Sciences, The University of Manchester, Manchester M13 9PL, UK School of Computer Science, Manchester Institute of Biotechnology and Computational and Evolutionary Biology, F
  • Robertson DL; School of Computer Science, Manchester Institute of Biotechnology and Computational and Evolutionary Biology, Faculty of Life Sciences, The University of Manchester, Manchester M13 9PL, UK.
  • Stevens R; School of Computer Science, Manchester Institute of Biotechnology and Computational and Evolutionary Biology, Faculty of Life Sciences, The University of Manchester, Manchester M13 9PL, UK.
Bioinformatics ; 30(17): i601-8, 2014 Sep 01.
Article em En | MEDLINE | ID: mdl-25161253
ABSTRACT
MOTIVATION As a natural consequence of being a computer-based discipline, bioinformatics has a strong focus on database and software development, but the volume and variety of resources are growing at unprecedented rates. An audit of database and software usage patterns could help provide an overview of developments in bioinformatics and community common practice, and comparing the links between resources through time could demonstrate both the persistence of existing software and the emergence of new tools.

RESULTS:

We study the connections between bioinformatics resources and construct networks of database and software usage patterns, based on resource co-occurrence, that correspond to snapshots of common practice in the bioinformatics community. We apply our approach to pairings of phylogenetics software reported in the literature and argue that these could provide a stepping stone into the identification of scientific best practice. AVAILABILITY AND IMPLEMENTATION The extracted resource data, the scripts used for network generation and the resulting networks are available at http//bionerds.sourceforge.net/networks/.
Assuntos

Texto completo: 1 Coleções: 01-internacional Base de dados: MEDLINE Assunto principal: Software / Bases de Dados Factuais / Biologia Computacional Tipo de estudo: Guideline Idioma: En Ano de publicação: 2014 Tipo de documento: Article

Texto completo: 1 Coleções: 01-internacional Base de dados: MEDLINE Assunto principal: Software / Bases de Dados Factuais / Biologia Computacional Tipo de estudo: Guideline Idioma: En Ano de publicação: 2014 Tipo de documento: Article