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Evaluation of in silico algorithms for use with ACMG/AMP clinical variant interpretation guidelines

Overview of attention for article published in Genome Biology (Online Edition), November 2017
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  • In the top 5% of all research outputs scored by Altmetric
  • High Attention Score compared to outputs of the same age (95th percentile)
  • Good Attention Score compared to outputs of the same age and source (78th percentile)

Mentioned by

blogs
2 blogs
twitter
63 tweeters

Citations

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125 Dimensions

Readers on

mendeley
225 Mendeley
citeulike
1 CiteULike
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Title
Evaluation of in silico algorithms for use with ACMG/AMP clinical variant interpretation guidelines
Published in
Genome Biology (Online Edition), November 2017
DOI 10.1186/s13059-017-1353-5
Pubmed ID
Authors

Rajarshi Ghosh, Ninad Oak, Sharon E. Plon

Abstract

The American College of Medical Genetics and American College of Pathologists (ACMG/AMP) variant classification guidelines for clinical reporting are widely used in diagnostic laboratories for variant interpretation. The ACMG/AMP guidelines recommend complete concordance of predictions among all in silico algorithms used without specifying the number or types of algorithms. The subjective nature of this recommendation contributes to discordance of variant classification among clinical laboratories and prevents definitive classification of variants. Using 14,819 benign or pathogenic missense variants from the ClinVar database, we compared performance of 25 algorithms across datasets differing in distinct biological and technical variables. There was wide variability in concordance among different combinations of algorithms with particularly low concordance for benign variants. We also identify a previously unreported source of error in variant interpretation (false concordance) where concordant in silico predictions are opposite to the evidence provided by other sources. We identified recently developed algorithms with high predictive power and robust to variables such as disease mechanism, gene constraint, and mode of inheritance, although poorer performing algorithms are more frequently used based on review of the clinical genetics literature (2011-2017). Our analyses identify algorithms with high performance characteristics independent of underlying disease mechanisms. We describe combinations of algorithms with increased concordance that should improve in silico algorithm usage during assessment of clinically relevant variants using the ACMG/AMP guidelines.

Twitter Demographics

The data shown below were collected from the profiles of 63 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 225 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
Unknown 225 100%

Demographic breakdown

Readers by professional status Count As %
Researcher 44 20%
Student > Ph. D. Student 37 16%
Student > Master 31 14%
Other 26 12%
Student > Bachelor 14 6%
Other 34 15%
Unknown 39 17%
Readers by discipline Count As %
Biochemistry, Genetics and Molecular Biology 87 39%
Agricultural and Biological Sciences 45 20%
Medicine and Dentistry 29 13%
Computer Science 7 3%
Nursing and Health Professions 4 2%
Other 12 5%
Unknown 41 18%

Attention Score in Context

This research output has an Altmetric Attention Score of 51. 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 05 April 2019.
All research outputs
#540,363
of 18,585,396 outputs
Outputs from Genome Biology (Online Edition)
#438
of 3,732 outputs
Outputs of similar age
#18,574
of 425,963 outputs
Outputs of similar age from Genome Biology (Online Edition)
#52
of 241 outputs
Altmetric has tracked 18,585,396 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 3,732 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 88% 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 425,963 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 95% of its contemporaries.
We're also able to compare this research output to 241 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.