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VDJML: a file format with tools for capturing the results of inferring immune receptor rearrangements

Overview of attention for article published in BMC Bioinformatics, October 2016
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  • In the top 25% of all research outputs scored by Altmetric
  • High Attention Score compared to outputs of the same age (83rd percentile)
  • High Attention Score compared to outputs of the same age and source (86th percentile)

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1 news outlet
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3 X users

Citations

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11 Dimensions

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47 Mendeley
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Title
VDJML: a file format with tools for capturing the results of inferring immune receptor rearrangements
Published in
BMC Bioinformatics, October 2016
DOI 10.1186/s12859-016-1214-3
Pubmed ID
Authors

Inimary T. Toby, Mikhail K. Levin, Edward A. Salinas, Scott Christley, Sanchita Bhattacharya, Felix Breden, Adam Buntzman, Brian Corrie, John Fonner, Namita T. Gupta, Uri Hershberg, Nishanth Marthandan, Aaron Rosenfeld, William Rounds, Florian Rubelt, Walter Scarborough, Jamie K. Scott, Mohamed Uduman, Jason A. Vander Heiden, Richard H. Scheuermann, Nancy Monson, Steven H. Kleinstein, Lindsay G. Cowell

Abstract

The genes that produce antibodies and the immune receptors expressed on lymphocytes are not germline encoded; rather, they are somatically generated in each developing lymphocyte by a process called V(D)J recombination, which assembles specific, independent gene segments into mature composite genes. The full set of composite genes in an individual at a single point in time is referred to as the immune repertoire. V(D)J recombination is the distinguishing feature of adaptive immunity and enables effective immune responses against an essentially infinite array of antigens. Characterization of immune repertoires is critical in both basic research and clinical contexts. Recent technological advances in repertoire profiling via high-throughput sequencing have resulted in an explosion of research activity in the field. This has been accompanied by a proliferation of software tools for analysis of repertoire sequencing data. Despite the widespread use of immune repertoire profiling and analysis software, there is currently no standardized format for output files from V(D)J analysis. Researchers utilize software such as IgBLAST and IMGT/High V-QUEST to perform V(D)J analysis and infer the structure of germline rearrangements. However, each of these software tools produces results in a different file format, and can annotate the same result using different labels. These differences make it challenging for users to perform additional downstream analyses. To help address this problem, we propose a standardized file format for representing V(D)J analysis results. The proposed format, VDJML, provides a common standardized format for different V(D)J analysis applications to facilitate downstream processing of the results in an application-agnostic manner. The VDJML file format specification is accompanied by a support library, written in C++ and Python, for reading and writing the VDJML file format. The VDJML suite will allow users to streamline their V(D)J analysis and facilitate the sharing of scientific knowledge within the community. The VDJML suite and documentation are available from https://vdjserver.org/vdjml/ . We welcome participation from the community in developing the file format standard, as well as code contributions.

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Mendeley readers

Mendeley readers

The data shown below were compiled from readership statistics for 47 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
Unknown 47 100%

Demographic breakdown

Readers by professional status Count As %
Researcher 15 32%
Student > Master 6 13%
Professor > Associate Professor 5 11%
Professor 4 9%
Student > Postgraduate 3 6%
Other 9 19%
Unknown 5 11%
Readers by discipline Count As %
Biochemistry, Genetics and Molecular Biology 13 28%
Immunology and Microbiology 10 21%
Agricultural and Biological Sciences 9 19%
Engineering 3 6%
Computer Science 2 4%
Other 6 13%
Unknown 4 9%
Attention Score in Context

Attention Score in Context

This research output has an Altmetric Attention Score of 11. 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 26 August 2017.
All research outputs
#2,890,390
of 22,893,031 outputs
Outputs from BMC Bioinformatics
#1,005
of 7,299 outputs
Outputs of similar age
#51,297
of 319,894 outputs
Outputs of similar age from BMC Bioinformatics
#18
of 132 outputs
Altmetric has tracked 22,893,031 research outputs across all sources so far. Compared to these this one has done well and is in the 87th percentile: it's in the top 25% of all research outputs ever tracked by Altmetric.
So far Altmetric has tracked 7,299 research outputs from this source. They typically receive a little more attention than average, with a mean Attention Score of 5.4. This one has done well, scoring higher than 86% 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 319,894 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 83% of its contemporaries.
We're also able to compare this research output to 132 others from the same source and published within six weeks on either side of this one. This one has done well, scoring higher than 86% of its contemporaries.