↓ Skip to main content

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

Overview of attention for article published in BMC Bioinformatics, January 2016
Altmetric Badge

About this Attention Score

  • Good Attention Score compared to outputs of the same age (74th percentile)
  • Above-average Attention Score compared to outputs of the same age and source (62nd percentile)

Mentioned by

twitter
9 X users

Citations

dimensions_citation
5 Dimensions

Readers on

mendeley
35 Mendeley
You are seeing a free-to-access but limited selection of the activity Altmetric has collected about this research output. Click here to find out more.
Title
Protein Sequence Annotation Tool (PSAT): a centralized web-based meta-server for high-throughput sequence annotations
Published in
BMC Bioinformatics, January 2016
DOI 10.1186/s12859-016-0887-y
Pubmed ID
Authors

Elo Leung, Amy Huang, Eithon Cadag, Aldrin Montana, Jan Lorenz Soliman, Carol L. Ecale Zhou

Abstract

Here 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. PSAT 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.

X Demographics

X Demographics

The data shown below were collected from the profiles of 9 X users who shared this research output. Click here to find out more about how the information was compiled.
Mendeley readers

Mendeley readers

The data shown below were compiled from readership statistics for 35 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
Germany 1 3%
France 1 3%
Norway 1 3%
Spain 1 3%
United States 1 3%
Unknown 30 86%

Demographic breakdown

Readers by professional status Count As %
Researcher 11 31%
Student > Ph. D. Student 6 17%
Student > Master 5 14%
Student > Bachelor 4 11%
Other 2 6%
Other 5 14%
Unknown 2 6%
Readers by discipline Count As %
Agricultural and Biological Sciences 14 40%
Biochemistry, Genetics and Molecular Biology 7 20%
Computer Science 6 17%
Engineering 2 6%
Pharmacology, Toxicology and Pharmaceutical Science 1 3%
Other 2 6%
Unknown 3 9%
Attention Score in Context

Attention Score in Context

This research output has an Altmetric Attention Score of 5. This is our high-level measure of the quality and quantity of online attention that it has received. This Attention Score, as well as the ranking and number of research outputs shown below, was calculated when the research output was last mentioned on 05 February 2016.
All research outputs
#6,597,121
of 24,063,285 outputs
Outputs from BMC Bioinformatics
#2,405
of 7,497 outputs
Outputs of similar age
#102,686
of 402,788 outputs
Outputs of similar age from BMC Bioinformatics
#54
of 146 outputs
Altmetric has tracked 24,063,285 research outputs across all sources so far. This one has received more attention than most of these and is in the 72nd percentile.
So far Altmetric has tracked 7,497 research outputs from this source. They typically receive a little more attention than average, with a mean Attention Score of 5.5. This one has gotten more attention than average, scoring higher than 67% of its peers.
Older research outputs will score higher simply because they've had more time to accumulate mentions. To account for age we can compare this Altmetric Attention Score to the 402,788 tracked outputs that were published within six weeks on either side of this one in any source. This one has gotten more attention than average, scoring higher than 74% of its contemporaries.
We're also able to compare this research output to 146 others from the same source and published within six weeks on either side of this one. This one has gotten more attention than average, scoring higher than 62% of its contemporaries.