↓ Skip to main content

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

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

About this Attention Score

  • In the top 5% of all research outputs scored by Altmetric
  • One of the highest-scoring outputs from this source (#7 of 2,349)
  • High Attention Score compared to outputs of the same age (99th percentile)
  • High Attention Score compared to outputs of the same age and source (99th percentile)

Mentioned by

news
49 news outlets
blogs
1 blog
twitter
32 tweeters
facebook
2 Facebook pages

Citations

dimensions_citation
215 Dimensions

Readers on

mendeley
612 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.

Twitter Demographics

The data shown below were collected from the profiles of 32 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 612 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 611 100%

Demographic breakdown

Readers by professional status Count As %
Researcher 135 22%
Student > Ph. D. Student 85 14%
Student > Master 82 13%
Other 67 11%
Student > Bachelor 54 9%
Other 101 17%
Unknown 88 14%
Readers by discipline Count As %
Biochemistry, Genetics and Molecular Biology 206 34%
Medicine and Dentistry 123 20%
Agricultural and Biological Sciences 97 16%
Neuroscience 16 3%
Computer Science 16 3%
Other 42 7%
Unknown 112 18%

Attention Score in Context

This research output has an Altmetric Attention Score of 416. 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 03 November 2020.
All research outputs
#37,975
of 17,606,952 outputs
Outputs from Genetics in Medicine
#7
of 2,349 outputs
Outputs of similar age
#1,318
of 273,806 outputs
Outputs of similar age from Genetics in Medicine
#1
of 42 outputs
Altmetric has tracked 17,606,952 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 2,349 research outputs from this source. They typically receive a lot more attention than average, with a mean Attention Score of 17.8. 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 273,806 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 42 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 99% of its contemporaries.