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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, 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 (94th percentile)
  • Above-average Attention Score compared to outputs of the same age and source (61st percentile)

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2 blogs
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183 Dimensions

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284 Mendeley
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Title
Evaluation of in silico algorithms for use with ACMG/AMP clinical variant interpretation guidelines
Published in
Genome Biology, 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.

X Demographics

X Demographics

The data shown below were collected from the profiles of 44 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 284 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
Unknown 284 100%

Demographic breakdown

Readers by professional status Count As %
Researcher 51 18%
Student > Ph. D. Student 41 14%
Student > Master 34 12%
Other 31 11%
Student > Bachelor 18 6%
Other 42 15%
Unknown 67 24%
Readers by discipline Count As %
Biochemistry, Genetics and Molecular Biology 101 36%
Agricultural and Biological Sciences 50 18%
Medicine and Dentistry 34 12%
Computer Science 9 3%
Nursing and Health Professions 4 1%
Other 15 5%
Unknown 71 25%
Attention Score in Context

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 05 April 2019.
All research outputs
#1,013,246
of 25,400,630 outputs
Outputs from Genome Biology
#723
of 4,470 outputs
Outputs of similar age
#22,854
of 446,753 outputs
Outputs of similar age from Genome Biology
#22
of 55 outputs
Altmetric has tracked 25,400,630 research outputs across all sources so far. Compared to these this one has done particularly well and is in the 96th percentile: it's in the top 5% of all research outputs ever tracked by Altmetric.
So far Altmetric has tracked 4,470 research outputs from this source. They typically receive a lot more attention than average, with a mean Attention Score of 27.6. This one has done well, scoring higher than 83% 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 446,753 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 55 others from the same source and published within six weeks on either side of this one. This one has gotten more attention than average, scoring higher than 61% of its contemporaries.