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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 (98th percentile)
  • High Attention Score compared to outputs of the same age and source (95th percentile)

Mentioned by

blogs
1 blog
twitter
109 tweeters
facebook
3 Facebook pages
googleplus
2 Google+ users

Readers on

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

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.

Twitter Demographics

The data shown below were collected from the profiles of 109 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 88 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
United States 5 6%
United Kingdom 3 3%
Taiwan 2 2%
Spain 1 1%
Germany 1 1%
Switzerland 1 1%
France 1 1%
Brazil 1 1%
Luxembourg 1 1%
Other 0 0%
Unknown 72 82%

Demographic breakdown

Readers by professional status Count As %
Researcher 37 42%
Student > Ph. D. Student 17 19%
Student > Master 8 9%
Student > Bachelor 7 8%
Other 6 7%
Other 13 15%
Readers by discipline Count As %
Agricultural and Biological Sciences 37 42%
Biochemistry, Genetics and Molecular Biology 20 23%
Computer Science 8 9%
Medicine and Dentistry 8 9%
Unspecified 5 6%
Other 10 11%

Attention Score in Context

This research output has an Altmetric Attention Score of 73. 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
#128,652
of 8,418,826 outputs
Outputs from PLoS Computational Biology
#188
of 4,090 outputs
Outputs of similar age
#4,352
of 228,155 outputs
Outputs of similar age from PLoS Computational Biology
#6
of 129 outputs
Altmetric has tracked 8,418,826 research outputs across all sources so far. Compared to these this one has done particularly well and is in the 98th percentile: it's in the top 5% of all research outputs ever tracked by Altmetric.
So far Altmetric has tracked 4,090 research outputs from this source. They typically receive a lot more attention than average, with a mean Attention Score of 17.3. This one has done particularly well, scoring higher than 95% 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 228,155 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 98% 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.