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PFP/ESG: automated protein function prediction servers enhanced with Gene Ontology visualization tool.
Khan, Ishita K; Wei, Qing; Chitale, Meghana; Kihara, Daisuke.
Afiliación
  • Khan IK; Department of Computer Science and Department of Biological Science, Purdue University, West Lafayette, IN 47907, USA.
  • Wei Q; Department of Computer Science and Department of Biological Science, Purdue University, West Lafayette, IN 47907, USA.
  • Chitale M; Department of Computer Science and Department of Biological Science, Purdue University, West Lafayette, IN 47907, USA.
  • Kihara D; Department of Computer Science and Department of Biological Science, Purdue University, West Lafayette, IN 47907, USA Department of Computer Science and Department of Biological Science, Purdue University, West Lafayette, IN 47907, USA.
Bioinformatics ; 31(2): 271-2, 2015 Jan 15.
Article en En | MEDLINE | ID: mdl-25273111
UNLABELLED: Protein function prediction (PFP) is an automated function prediction method that predicts Gene Ontology (GO) annotations for a protein sequence using distantly related sequences and contextual associations of GO terms. Extended similarity group (ESG) is another GO prediction algorithm that makes predictions based on iterative sequence database searches. Here, we provide interactive web servers for the PFP and ESG algorithms that are equipped with an effective visualization of the GO predictions in a hierarchical topology. AVAILABILITY: PFP/ESG servers are freely available at http://kiharalab.org/web/pfp.php and http://kiharalab.org/web/esg.php, or access both at http://kiharalab.org/pfp_esg.php. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.
Asunto(s)

Texto completo: 1 Colección: 01-internacional Base de datos: MEDLINE Asunto principal: Algoritmos / Gráficos por Computador / Proteínas / Biología Computacional / Análisis de Secuencia de Proteína / Anotación de Secuencia Molecular / Ontología de Genes Tipo de estudio: Prognostic_studies / Risk_factors_studies Límite: Humans Idioma: En Revista: Bioinformatics Asunto de la revista: INFORMATICA MEDICA Año: 2015 Tipo del documento: Article País de afiliación: Estados Unidos Pais de publicación: Reino Unido

Texto completo: 1 Colección: 01-internacional Base de datos: MEDLINE Asunto principal: Algoritmos / Gráficos por Computador / Proteínas / Biología Computacional / Análisis de Secuencia de Proteína / Anotación de Secuencia Molecular / Ontología de Genes Tipo de estudio: Prognostic_studies / Risk_factors_studies Límite: Humans Idioma: En Revista: Bioinformatics Asunto de la revista: INFORMATICA MEDICA Año: 2015 Tipo del documento: Article País de afiliación: Estados Unidos Pais de publicación: Reino Unido