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ClinGen Pathogenicity Calculator: a configurable system for assessing pathogenicity of genetic variants

Overview of attention for article published in Genome Medicine, January 2017
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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 (94th percentile)
  • Good Attention Score compared to outputs of the same age and source (78th percentile)

Mentioned by

news
1 news outlet
blogs
1 blog
twitter
35 X users

Citations

dimensions_citation
62 Dimensions

Readers on

mendeley
194 Mendeley
citeulike
1 CiteULike
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Title
ClinGen Pathogenicity Calculator: a configurable system for assessing pathogenicity of genetic variants
Published in
Genome Medicine, January 2017
DOI 10.1186/s13073-016-0391-z
Pubmed ID
Authors

Ronak Y. Patel, Neethu Shah, Andrew R. Jackson, Rajarshi Ghosh, Piotr Pawliczek, Sameer Paithankar, Aaron Baker, Kevin Riehle, Hailin Chen, Sofia Milosavljevic, Chris Bizon, Shawn Rynearson, Tristan Nelson, Gail P. Jarvik, Heidi L. Rehm, Steven M. Harrison, Danielle Azzariti, Bradford Powell, Larry Babb, Sharon E. Plon, Aleksandar Milosavljevic, on behalf of the ClinGen Resource

Abstract

The success of the clinical use of sequencing based tests (from single gene to genomes) depends on the accuracy and consistency of variant interpretation. Aiming to improve the interpretation process through practice guidelines, the American College of Medical Genetics and Genomics (ACMG) and the Association for Molecular Pathology (AMP) have published standards and guidelines for the interpretation of sequence variants. However, manual application of the guidelines is tedious and prone to human error. Web-based tools and software systems may not only address this problem but also document reasoning and supporting evidence, thus enabling transparency of evidence-based reasoning and resolution of discordant interpretations. In this report, we describe the design, implementation, and initial testing of the Clinical Genome Resource (ClinGen) Pathogenicity Calculator, a configurable system and web service for the assessment of pathogenicity of Mendelian germline sequence variants. The system allows users to enter the applicable ACMG/AMP-style evidence tags for a specific allele with links to supporting data for each tag and generate guideline-based pathogenicity assessment for the allele. Through automation and comprehensive documentation of evidence codes, the system facilitates more accurate application of the ACMG/AMP guidelines, improves standardization in variant classification, and facilitates collaborative resolution of discordances. The rules of reasoning are configurable with gene-specific or disease-specific guideline variations (e.g. cardiomyopathy-specific frequency thresholds and functional assays). The software is modular, equipped with robust application program interfaces (APIs), and available under a free open source license and as a cloud-hosted web service, thus facilitating both stand-alone use and integration with existing variant curation and interpretation systems. The Pathogenicity Calculator is accessible at http://calculator.clinicalgenome.org . By enabling evidence-based reasoning about the pathogenicity of genetic variants and by documenting supporting evidence, the Calculator contributes toward the creation of a knowledge commons and more accurate interpretation of sequence variants in research and clinical care.

X Demographics

X Demographics

The data shown below were collected from the profiles of 35 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 194 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
United Kingdom 2 1%
Korea, Republic of 1 <1%
United States 1 <1%
Unknown 190 98%

Demographic breakdown

Readers by professional status Count As %
Researcher 45 23%
Student > Ph. D. Student 32 16%
Other 30 15%
Student > Master 20 10%
Student > Bachelor 10 5%
Other 30 15%
Unknown 27 14%
Readers by discipline Count As %
Biochemistry, Genetics and Molecular Biology 69 36%
Agricultural and Biological Sciences 41 21%
Medicine and Dentistry 22 11%
Computer Science 7 4%
Engineering 4 2%
Other 19 10%
Unknown 32 16%
Attention Score in Context

Attention Score in Context

This research output has an Altmetric Attention Score of 35. 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 16 May 2019.
All research outputs
#1,142,383
of 25,390,970 outputs
Outputs from Genome Medicine
#220
of 1,584 outputs
Outputs of similar age
#22,883
of 419,764 outputs
Outputs of similar age from Genome Medicine
#7
of 28 outputs
Altmetric has tracked 25,390,970 research outputs across all sources so far. Compared to these this one has done particularly well and is in the 95th percentile: it's in the top 5% of all research outputs ever tracked by Altmetric.
So far Altmetric has tracked 1,584 research outputs from this source. They typically receive a lot more attention than average, with a mean Attention Score of 26.8. This one has done well, scoring higher than 86% 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 419,764 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 28 others from the same source and published within six weeks on either side of this one. This one has done well, scoring higher than 78% of its contemporaries.