Protein Sequence Annotation Tool (PSAT): a centralized web-based meta-server for high-throughput sequence annotations

BMC Bioinformatics

Software

Carol L. Ecale Zhou¹  Amy Huang¹  Eithon Cadag²  Jan Lorenz Soliman³  Elo Leung⁴  Aldrin Montana⁴ 

[1] Computing Applications and Research, Global Security Computing Applications Division, Lawrence Livermore National Security, 94550, Livermore, CA, USA;Computing Applications and Research, Global Security Computing Applications Division, Lawrence Livermore National Security, 94550, Livermore, CA, USA;Capella Biosciences, Palo Alto, CA, USA;Computing Applications and Research, Global Security Computing Applications Division, Lawrence Livermore National Security, 94550, Livermore, CA, USA;LinkedIn, 94043, Mountain View, CA, USA;Computing Applications and Research, Global Security Computing Applications Division, Lawrence Livermore National Security, 94550, Livermore, CA, USA;Personalis, 94025, Menlo Park, CA, USA;

关键词: Enzyme Commission;    Enzyme Commission Number;    Naphthalene Degradation;    Strain RV1423;    Page Delay;   

DOI  :  10.1186/s12859-016-0887-y

 received in 2015-02-20, accepted in 2016-01-11,  发布年份 2016

来源: Springer

【 摘 要 】

BackgroundHere we introduce the Protein Sequence Annotation Tool (PSAT), a web-based, sequence annotation meta-server for performing integrated, high-throughput, genome-wide sequence analyses. Our goals in building PSAT were to (1) create an extensible platform for integration of multiple sequence-based bioinformatics tools, (2) enable functional annotations and enzyme predictions over large input protein fasta data sets, and (3) provide a web interface for convenient execution of the tools.ResultsIn this paper, we demonstrate the utility of PSAT by annotating the predicted peptide gene products of Herbaspirillum sp. strain RV1423, importing the results of PSAT into EC2KEGG, and using the resulting functional comparisons to identify a putative catabolic pathway, thereby distinguishing RV1423 from a well annotated Herbaspirillum species. This analysis demonstrates that high-throughput enzyme predictions, provided by PSAT processing, can be used to identify metabolic potential in an otherwise poorly annotated genome.ConclusionsPSAT is a meta server that combines the results from several sequence-based annotation and function prediction codes, and is available at http://psat.llnl.gov/psat/. PSAT stands apart from other sequence-based genome annotation systems in providing a high-throughput platform for rapid de novo enzyme predictions and sequence annotations over large input protein sequence data sets in FASTA. PSAT is most appropriately applied in annotation of large protein FASTA sets that may or may not be associated with a single genome.

【 授权许可】

CC BY   
© Leung et al. 2016