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Pathogen metadata platform: software for accessing and analyzing pathogen strain information

Overview of attention for article published in BMC Bioinformatics, September 2016
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About this Attention Score

  • Above-average Attention Score compared to outputs of the same age (62nd percentile)
  • Good Attention Score compared to outputs of the same age and source (75th percentile)

Mentioned by

twitter
7 tweeters

Citations

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4 Dimensions

Readers on

mendeley
19 Mendeley
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Title
Pathogen metadata platform: software for accessing and analyzing pathogen strain information
Published in
BMC Bioinformatics, September 2016
DOI 10.1186/s12859-016-1231-2
Pubmed ID
Authors

Wenling E. Chang, Matthew W. Peterson, Christopher D. Garay, Tonia Korves

Abstract

Pathogen metadata includes information about where and when a pathogen was collected and the type of environment it came from. Along with genomic nucleotide sequence data, this metadata is growing rapidly and becoming a valuable resource not only for research but for biosurveillance and public health. However, current freely available tools for analyzing this data are geared towards bioinformaticians and/or do not provide summaries and visualizations needed to readily interpret results. We designed a platform to easily access and summarize data about pathogen samples. The software includes a PostgreSQL database that captures metadata useful for disease outbreak investigations, and scripts for downloading and parsing data from NCBI BioSample and BioProject into the database. The software provides a user interface to query metadata and obtain standardized results in an exportable, tab-delimited format. To visually summarize results, the user interface provides a 2D histogram for user-selected metadata types and mapping of geolocated entries. The software is built on the LabKey data platform, an open-source data management platform, which enables developers to add functionalities. We demonstrate the use of the software in querying for a pathogen serovar and for genome sequence identifiers. This software enables users to create a local database for pathogen metadata, populate it with data from NCBI, easily query the data, and obtain visual summaries. Some of the components, such as the database, are modular and can be incorporated into other data platforms. The source code is freely available for download at https://github.com/wchangmitre/bioattribution .

Twitter Demographics

The data shown below were collected from the profiles of 7 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 19 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
United States 1 5%
France 1 5%
Unknown 17 89%

Demographic breakdown

Readers by professional status Count As %
Student > Ph. D. Student 6 32%
Researcher 5 26%
Student > Bachelor 2 11%
Other 2 11%
Student > Postgraduate 1 5%
Other 0 0%
Unknown 3 16%
Readers by discipline Count As %
Agricultural and Biological Sciences 7 37%
Computer Science 4 21%
Biochemistry, Genetics and Molecular Biology 2 11%
Immunology and Microbiology 1 5%
Social Sciences 1 5%
Other 1 5%
Unknown 3 16%

Attention Score in Context

This research output has an Altmetric Attention Score of 3. 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 17 September 2016.
All research outputs
#6,973,660
of 13,727,342 outputs
Outputs from BMC Bioinformatics
#2,418
of 5,107 outputs
Outputs of similar age
#97,904
of 263,347 outputs
Outputs of similar age from BMC Bioinformatics
#5
of 29 outputs
Altmetric has tracked 13,727,342 research outputs across all sources so far. This one is in the 48th percentile – i.e., 48% of other outputs scored the same or lower than it.
So far Altmetric has tracked 5,107 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 51% 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 263,347 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 62% of its contemporaries.
We're also able to compare this research output to 29 others from the same source and published within six weeks on either side of this one. This one has done well, scoring higher than 75% of its contemporaries.