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Genome Modeling System: A Knowledge Management Platform for Genomics

Overview of attention for article published in PLoS Computational Biology, July 2015
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About this Attention Score

  • In the top 5% of all research outputs scored by Altmetric
  • High Attention Score compared to outputs of the same age (97th percentile)
  • High Attention Score compared to outputs of the same age and source (95th percentile)

Mentioned by

blogs
1 blog
twitter
91 X users
facebook
3 Facebook pages
googleplus
2 Google+ users

Citations

dimensions_citation
83 Dimensions

Readers on

mendeley
154 Mendeley
citeulike
4 CiteULike
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Title
Genome Modeling System: A Knowledge Management Platform for Genomics
Published in
PLoS Computational Biology, July 2015
DOI 10.1371/journal.pcbi.1004274
Pubmed ID
Authors

Malachi Griffith, Obi L. Griffith, Scott M. Smith, Avinash Ramu, Matthew B. Callaway, Anthony M. Brummett, Michael J. Kiwala, Adam C. Coffman, Allison A. Regier, Ben J. Oberkfell, Gabriel E. Sanderson, Thomas P. Mooney, Nathaniel G. Nutter, Edward A. Belter, Feiyu Du, Robert L. Long, Travis E. Abbott, Ian T. Ferguson, David L. Morton, Mark M. Burnett, James V. Weible, Joshua B. Peck, Adam Dukes, Joshua F. McMichael, Justin T. Lolofie, Brian R. Derickson, Jasreet Hundal, Zachary L. Skidmore, Benjamin J. Ainscough, Nathan D. Dees, William S. Schierding, Cyriac Kandoth, Kyung H. Kim, Charles Lu, Christopher C. Harris, Nicole Maher, Christopher A. Maher, Vincent J. Magrini, Benjamin S. Abbott, Ken Chen, Eric Clark, Indraniel Das, Xian Fan, Amy E. Hawkins, Todd G. Hepler, Todd N. Wylie, Shawn M. Leonard, William E. Schroeder, Xiaoqi Shi, Lynn K. Carmichael, Matthew R. Weil, Richard W. Wohlstadter, Gary Stiehr, Michael D. McLellan, Craig S. Pohl, Christopher A. Miller, Daniel C. Koboldt, Jason R. Walker, James M. Eldred, David E. Larson, David J. Dooling, Li Ding, Elaine R. Mardis, Richard K. Wilson

Abstract

In this work, we present the Genome Modeling System (GMS), an analysis information management system capable of executing automated genome analysis pipelines at a massive scale. The GMS framework provides detailed tracking of samples and data coupled with reliable and repeatable analysis pipelines. The GMS also serves as a platform for bioinformatics development, allowing a large team to collaborate on data analysis, or an individual researcher to leverage the work of others effectively within its data management system. Rather than separating ad-hoc analysis from rigorous, reproducible pipelines, the GMS promotes systematic integration between the two. As a demonstration of the GMS, we performed an integrated analysis of whole genome, exome and transcriptome sequencing data from a breast cancer cell line (HCC1395) and matched lymphoblastoid line (HCC1395BL). These data are available for users to test the software, complete tutorials and develop novel GMS pipeline configurations. The GMS is available at https://github.com/genome/gms.

X Demographics

X Demographics

The data shown below were collected from the profiles of 91 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 154 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
United States 4 3%
United Kingdom 3 2%
Taiwan 2 1%
Switzerland 1 <1%
Brazil 1 <1%
Germany 1 <1%
Spain 1 <1%
Luxembourg 1 <1%
Unknown 140 91%

Demographic breakdown

Readers by professional status Count As %
Researcher 48 31%
Student > Ph. D. Student 24 16%
Student > Master 16 10%
Student > Bachelor 13 8%
Other 9 6%
Other 23 15%
Unknown 21 14%
Readers by discipline Count As %
Agricultural and Biological Sciences 44 29%
Biochemistry, Genetics and Molecular Biology 28 18%
Computer Science 16 10%
Medicine and Dentistry 13 8%
Engineering 5 3%
Other 21 14%
Unknown 27 18%
Attention Score in Context

Attention Score in Context

This research output has an Altmetric Attention Score of 59. 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 24 June 2017.
All research outputs
#716,461
of 25,394,764 outputs
Outputs from PLoS Computational Biology
#519
of 8,964 outputs
Outputs of similar age
#8,177
of 276,089 outputs
Outputs of similar age from PLoS Computational Biology
#6
of 129 outputs
Altmetric has tracked 25,394,764 research outputs across all sources so far. Compared to these this one has done particularly well and is in the 97th percentile: it's in the top 5% of all research outputs ever tracked by Altmetric.
So far Altmetric has tracked 8,964 research outputs from this source. They typically receive a lot more attention than average, with a mean Attention Score of 20.4. This one has done particularly well, scoring higher than 94% 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 276,089 tracked outputs that were published within six weeks on either side of this one in any source. This one has done particularly well, scoring higher than 97% of its contemporaries.
We're also able to compare this research output to 129 others from the same source and published within six weeks on either side of this one. This one has done particularly well, scoring higher than 95% of its contemporaries.