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

In this study, 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. In 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. Lastly, PSAT is a meta server that combines the results from several sequence-based annotation and function prediction codes, and is available at PSAT stands apart from other sequencebased 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 appropriatelymore » applied in annotation of large protein FASTA sets that may or may not be associated with a single genome.« less
 [1] ;  [2] ;  [3] ;  [1] ;  [4] ;  [2]
  1. Lawrence Livermore National Security, Livermore, CA (United States); Personalis, Menlo Park, CA (United States)
  2. Lawrence Livermore National Security, Livermore, CA (United States)
  3. Lawrence Livermore National Security, Livermore, CA (United States); Capella Biosciences, Palo Alto, CA (United States)
  4. Lawrence Livermore National Security, Livermore, CA (United States); LinkedIn, Mountain View, CA (United States)
Publication Date:
OSTI Identifier:
Grant/Contract Number:
AC52-07NA27344; PE0603384BP-B0946791; SCW1039
Accepted Manuscript
Journal Name:
BMC Bioinformatics
Additional Journal Information:
Journal Volume: 17; Journal Issue: 1; Journal ID: ISSN 1471-2105
BioMed Central
Research Org:
Lawrence Livermore National Security
Sponsoring Org:
Country of Publication:
United States