↓ Skip to main content

Michigan Publishing

Article Metrics

Characterizing reduced coverage regions through comparison of exome and genome sequencing data across 10 centers

Overview of attention for article published in Genetics in Medicine, November 2017
Altmetric Badge

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 (94th percentile)
  • High Attention Score compared to outputs of the same age and source (83rd percentile)

Mentioned by

twitter
65 tweeters
facebook
5 Facebook pages

Citations

dimensions_citation
2 Dimensions

Readers on

mendeley
16 Mendeley
citeulike
1 CiteULike
Title
Characterizing reduced coverage regions through comparison of exome and genome sequencing data across 10 centers
Published in
Genetics in Medicine, November 2017
DOI 10.1038/gim.2017.192
Pubmed ID
Authors

Rashesh V Sanghvi, Christian J Buhay, Bradford C Powell, Ellen A Tsai, Michael O Dorschner, Celine S Hong, Matthew S Lebo, Ariella Sasson, David S Hanna, Sean McGee, Kevin M Bowling, Gregory M Cooper, David E Gray, Robert J Lonigro, Andrew Dunford, Christine A Brennan, Carrie Cibulskis, Kimberly Walker, Mauricio O Carneiro, Joshua Sailsbery, Lucia A Hindorff, Dan R Robinson, Avni Santani, Mahdi Sarmady, Heidi L Rehm, Leslie G Biesecker, Deborah A Nickerson, Carolyn M Hutter, Levi Garraway, Donna M Muzny, Nikhil Wagle, Sanghvi, Rashesh V, Buhay, Christian J, Powell, Bradford C, Tsai, Ellen A, Dorschner, Michael O, Hong, Celine S, Lebo, Matthew S, Sasson, Ariella, Hanna, David S, McGee, Sean, Bowling, Kevin M, Cooper, Gregory M, Gray, David E, Lonigro, Robert J, Dunford, Andrew, Brennan, Christine A, Cibulskis, Carrie, Walker, Kimberly, Carneiro, Mauricio O, Sailsbery, Joshua, Hindorff, Lucia A, Robinson, Dan R, Santani, Avni, Sarmady, Mahdi, Rehm, Heidi L, Biesecker, Leslie G, Nickerson, Deborah A, Hutter, Carolyn M, Garraway, Levi, Muzny, Donna M, Wagle, Nikhil

Abstract

PurposeAs massively parallel sequencing is increasingly being used for clinical decision making, it has become critical to understand parameters that affect sequencing quality and to establish methods for measuring and reporting clinical sequencing standards. In this report, we propose a definition for reduced coverage regions and describe a set of standards for variant calling in clinical sequencing applications.MethodsTo enable sequencing centers to assess the regions of poor sequencing quality in their own data, we optimized and used a tool (ExCID) to identify reduced coverage loci within genes or regions of particular interest. We used this framework to examine sequencing data from 500 patients generated in 10 projects at sequencing centers in the National Human Genome Research Institute/National Cancer Institute Clinical Sequencing Exploratory Research Consortium.ResultsThis approach identified reduced coverage regions in clinically relevant genes, including known clinically relevant loci that were uniquely missed at individual centers, in multiple centers, and in all centers.ConclusionThis report provides a process road map for clinical sequencing centers looking to perform similar analyses on their data.Genetics in Medicine advance online publication, 16 November 2017; doi:10.1038/gim.2017.192.

Twitter Demographics

The data shown below were collected from the profiles of 65 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 16 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
Unknown 16 100%

Demographic breakdown

Readers by professional status Count As %
Researcher 8 50%
Other 3 19%
Unspecified 2 13%
Student > Bachelor 1 6%
Student > Ph. D. Student 1 6%
Other 1 6%
Readers by discipline Count As %
Biochemistry, Genetics and Molecular Biology 6 38%
Agricultural and Biological Sciences 6 38%
Unspecified 2 13%
Business, Management and Accounting 1 6%
Medicine and Dentistry 1 6%
Other 0 0%

Attention Score in Context

This research output has an Altmetric Attention Score of 41. 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 13 September 2018.
All research outputs
#326,452
of 11,799,729 outputs
Outputs from Genetics in Medicine
#138
of 1,588 outputs
Outputs of similar age
#14,613
of 251,327 outputs
Outputs of similar age from Genetics in Medicine
#11
of 68 outputs
Altmetric has tracked 11,799,729 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 1,588 research outputs from this source. They typically receive a lot more attention than average, with a mean Attention Score of 16.2. This one has done particularly well, scoring higher than 91% 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 251,327 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 94% of its contemporaries.
We're also able to compare this research output to 68 others from the same source and published within six weeks on either side of this one. This one has done well, scoring higher than 83% of its contemporaries.