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Calibration of Multiple In Silico Tools for Predicting Pathogenicity of Mismatch Repair Gene Missense Substitutions

Overview of attention for article published in Human Mutation, October 2012
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Title
Calibration of Multiple In Silico Tools for Predicting Pathogenicity of Mismatch Repair Gene Missense Substitutions
Published in
Human Mutation, October 2012
DOI 10.1002/humu.22214
Pubmed ID
Authors

Bryony A. Thompson, Marc S. Greenblatt, Maxime P. Vallee, Johanna C. Herkert, Chloe Tessereau, Erin L. Young, Ivan A. Adzhubey, Biao Li, Russell Bell, Bingjian Feng, Sean D. Mooney, Predrag Radivojac, Shamil R. Sunyaev, Thierry Frebourg, Robert M.W. Hofstra, Rolf H. Sijmons, Ken Boucher, Alun Thomas, David E. Goldgar, Amanda B. Spurdle, Sean V. Tavtigian

Abstract

Classification of rare missense substitutions observed during genetic testing for patient management is a considerable problem in clinical genetics. The Bayesian integrated evaluation of unclassified variants is a solution originally developed for BRCA1/2. Here, we take a step toward an analogous system for the mismatch repair (MMR) genes (MLH1, MSH2, MSH6, and PMS2) that confer colon cancer susceptibility in Lynch syndrome by calibrating in silico tools to estimate prior probabilities of pathogenicity for MMR gene missense substitutions. A qualitative five-class classification system was developed and applied to 143 MMR missense variants. This identified 74 missense substitutions suitable for calibration. These substitutions were scored using six different in silico tools (Align-Grantham Variation Grantham Deviation, multivariate analysis of protein polymorphisms [MAPP], MutPred, PolyPhen-2.1, Sorting Intolerant From Tolerant, and Xvar), using curated MMR multiple sequence alignments where possible. The output from each tool was calibrated by regression against the classifications of the 74 missense substitutions; these calibrated outputs are interpretable as prior probabilities of pathogenicity. MAPP was the most accurate tool and MAPP + PolyPhen-2.1 provided the best-combined model (R(2)  = 0.62 and area under receiver operating characteristic = 0.93). The MAPP + PolyPhen-2.1 output is sufficiently predictive to feed as a continuous variable into the quantitative Bayesian integrated evaluation for clinical classification of MMR gene missense substitutions.

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Mendeley readers

Mendeley readers

The data shown below were compiled from readership statistics for 111 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
United States 6 5%
Netherlands 2 2%
Denmark 1 <1%
Spain 1 <1%
Poland 1 <1%
Unknown 100 90%

Demographic breakdown

Readers by professional status Count As %
Researcher 30 27%
Student > Ph. D. Student 23 21%
Student > Master 14 13%
Student > Bachelor 11 10%
Other 8 7%
Other 14 13%
Unknown 11 10%
Readers by discipline Count As %
Agricultural and Biological Sciences 40 36%
Biochemistry, Genetics and Molecular Biology 23 21%
Medicine and Dentistry 22 20%
Engineering 3 3%
Mathematics 2 2%
Other 8 7%
Unknown 13 12%
Attention Score in Context

Attention Score in Context

This research output has an Altmetric Attention Score of 3. 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 10 June 2020.
All research outputs
#8,534,528
of 25,373,627 outputs
Outputs from Human Mutation
#1,043
of 2,982 outputs
Outputs of similar age
#67,203
of 200,497 outputs
Outputs of similar age from Human Mutation
#10
of 28 outputs
Altmetric has tracked 25,373,627 research outputs across all sources so far. This one is in the 43rd percentile – i.e., 43% of other outputs scored the same or lower than it.
So far Altmetric has tracked 2,982 research outputs from this source. They receive a mean Attention Score of 4.8. This one is in the 32nd percentile – i.e., 32% of its peers scored the same or lower than it.
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 200,497 tracked outputs that were published within six weeks on either side of this one in any source. This one is in the 44th percentile – i.e., 44% of its contemporaries scored the same or lower than it.
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 is in the 32nd percentile – i.e., 32% of its contemporaries scored the same or lower than it.