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(Referência obtida automaticamente do Web of Science, por meio da informação sobre o financiamento pela FAPESP e o número do processo correspondente, incluída na publicação pelos autores.)

SIFTER-T: A scalable and optimized framework for the SIFTER phylogenomic method of probabilistic protein domain annotation

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Autor(es):
Almeida-e-Silva, Danillo C. [1] ; Vencio, Ricardo Z. N. [1]
Número total de Autores: 2
Afiliação do(s) autor(es):
[1] Univ Sao Paulo, Dept Comp & Math, FFCLRP USP, BR-14049 Ribeirao Preto - Brazil
Número total de Afiliações: 1
Tipo de documento: Artigo Científico
Fonte: BIOTECHNIQUES; v. 58, n. 3, p. 140-142, MAR 2015.
Citações Web of Science: 0
Resumo

Statistical Inference of Function Through Evolutionary Relationships (SIFTER) is a powerful computational platform for probabilistic protein domain annotation. Nevertheless, SIFTER is not widely used, likely due to usability and scalability issues. Here we present SIFTER-T (SIFTER Throughput-optimized), a substantial improvement over SIFTER's original proof-of-principle implementation. SIFTER-T is optimized for better performance, allowing it to be used at the genome-wide scale. Compared to SIFTER 2.0, SIFTER-T achieved an 87-fold performance improvement using published test data sets for the known annotations recovering module and a 72.3% speed increase for the gene tree generation module in quad-core machines, as well as a major decrease in memory usage during the realignment phase. Memory optimization allowed an expanded set of proteins to be handled by SIFTER's probabilistic method. The improvement in performance and automation that we achieved allowed us to build a web server to bring the power of Bayesian phylogenomic inference to the genomics community. SIFTER-T and its online interface are freely available under GNU license at http://labpib.fmrp.usp.br/methods/SIFTER-t/ and https://github.com/dcasbioinfo/SIFTER-t. (AU)

Processo FAPESP: 09/53161-6 - Tecnologia da Informação aplicada a genômica para bioenergia: anotação probabilística usando inteligência artificial
Beneficiário:Ricardo Zorzetto Nicoliello Vêncio
Modalidade de apoio: Auxílio à Pesquisa - Programa BIOEN - Regular