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Sherloc: a comprehensive refinement of the ACMG–AMP variant classification criteria

Overview of attention for article published in Genetics in Medicine, May 2017
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  • In the top 5% of all research outputs scored by Altmetric
  • One of the highest-scoring outputs from this source (#10 of 2,978)
  • High Attention Score compared to outputs of the same age (99th percentile)
  • High Attention Score compared to outputs of the same age and source (98th percentile)

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49 news outlets
blogs
1 blog
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25 X users
facebook
2 Facebook pages

Citations

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550 Dimensions

Readers on

mendeley
873 Mendeley
citeulike
1 CiteULike
Title
Sherloc: a comprehensive refinement of the ACMG–AMP variant classification criteria
Published in
Genetics in Medicine, May 2017
DOI 10.1038/gim.2017.37
Pubmed ID
Authors

Keith Nykamp, Michael Anderson, Martin Powers, John Garcia, Blanca Herrera, Yuan-Yuan Ho, Yuya Kobayashi, Nila Patil, Janita Thusberg, Marjorie Westbrook, Scott Topper

Abstract

PurposeThe 2015 American College of Medical Genetics and Genomics-Association for Molecular Pathology (ACMG-AMP) guidelines were a major step toward establishing a common framework for variant classification. In practice, however, several aspects of the guidelines lack specificity, are subject to varied interpretations, or fail to capture relevant aspects of clinical molecular genetics. A simple implementation of the guidelines in their current form is insufficient for consistent and comprehensive variant classification.MethodsWe undertook an iterative process of refining the ACMG-AMP guidelines. We used the guidelines to classify more than 40,000 clinically observed variants, assessed the outcome, and refined the classification criteria to capture exceptions and edge cases. During this process, the criteria evolved through eight major and minor revisions.ResultsOur implementation: (i) separated ambiguous ACMG-AMP criteria into a set of discrete but related rules with refined weights; (ii) grouped certain criteria to protect against the overcounting of conceptually related evidence; and (iii) replaced the "clinical criteria" style of the guidelines with additive, semiquantitative criteria.ConclusionSherloc builds on the strong framework of 33 rules established by the ACMG-AMP guidelines and introduces 108 detailed refinements, which support a more consistent and transparent approach to variant classification.GENETICS in MEDICINE advance online publication, 11 May 2017; doi:10.1038/gim.2017.37.

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Mendeley readers

Mendeley readers

The data shown below were compiled from readership statistics for 873 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
Korea, Republic of 1 <1%
Unknown 872 100%

Demographic breakdown

Readers by professional status Count As %
Researcher 168 19%
Student > Ph. D. Student 110 13%
Student > Master 100 11%
Other 84 10%
Student > Bachelor 80 9%
Other 132 15%
Unknown 199 23%
Readers by discipline Count As %
Biochemistry, Genetics and Molecular Biology 262 30%
Medicine and Dentistry 163 19%
Agricultural and Biological Sciences 114 13%
Neuroscience 21 2%
Computer Science 19 2%
Other 66 8%
Unknown 228 26%
Attention Score in Context

Attention Score in Context

This research output has an Altmetric Attention Score of 411. 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 04 November 2023.
All research outputs
#72,783
of 26,017,215 outputs
Outputs from Genetics in Medicine
#10
of 2,978 outputs
Outputs of similar age
#1,675
of 329,380 outputs
Outputs of similar age from Genetics in Medicine
#1
of 51 outputs
Altmetric has tracked 26,017,215 research outputs across all sources so far. Compared to these this one has done particularly well and is in the 99th percentile: it's in the top 5% of all research outputs ever tracked by Altmetric.
So far Altmetric has tracked 2,978 research outputs from this source. They typically receive a lot more attention than average, with a mean Attention Score of 19.3. This one has done particularly well, scoring higher than 99% 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 329,380 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 99% of its contemporaries.
We're also able to compare this research output to 51 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 98% of its contemporaries.