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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
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About this Attention Score

  • In the top 25% of all research outputs scored by Altmetric
  • Good Attention Score compared to outputs of the same age (75th percentile)
  • Above-average Attention Score compared to outputs of the same age and source (64th percentile)

Mentioned by

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9 tweeters

Citations

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4 Dimensions

Readers on

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34 Mendeley
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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.

Twitter Demographics

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

Mendeley readers

The data shown below were compiled from readership statistics for 34 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 29 85%

Demographic breakdown

Readers by professional status Count As %
Researcher 12 35%
Student > Ph. D. Student 6 18%
Student > Master 5 15%
Student > Bachelor 4 12%
Other 2 6%
Other 4 12%
Unknown 1 3%
Readers by discipline Count As %
Agricultural and Biological Sciences 14 41%
Biochemistry, Genetics and Molecular Biology 8 24%
Computer Science 5 15%
Engineering 2 6%
Immunology and Microbiology 1 3%
Other 2 6%
Unknown 2 6%

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
#2,632,714
of 11,309,368 outputs
Outputs from BMC Bioinformatics
#1,198
of 4,195 outputs
Outputs of similar age
#84,576
of 342,086 outputs
Outputs of similar age from BMC Bioinformatics
#52
of 145 outputs
Altmetric has tracked 11,309,368 research outputs across all sources so far. Compared to these this one has done well and is in the 76th percentile: it's in the top 25% of all research outputs ever tracked by Altmetric.
So far Altmetric has tracked 4,195 research outputs from this source. They receive a mean Attention Score of 4.9. This one has gotten more attention than average, scoring higher than 70% 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 342,086 tracked outputs that were published within six weeks on either side of this one in any source. This one has done well, scoring higher than 75% of its contemporaries.
We're also able to compare this research output to 145 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 64% of its contemporaries.