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In silico analysis of missense substitutions using sequence‐alignment based methods

Overview of attention for article published in Human Mutation, October 2008
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  • In the top 25% of all research outputs scored by Altmetric
  • High Attention Score compared to outputs of the same age (92nd percentile)
  • High Attention Score compared to outputs of the same age and source (84th percentile)

Mentioned by

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1 news outlet
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2 patents

Citations

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171 Dimensions

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202 Mendeley
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1 Connotea
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Title
In silico analysis of missense substitutions using sequence‐alignment based methods
Published in
Human Mutation, October 2008
DOI 10.1002/humu.20892
Pubmed ID
Authors

Sean V. Tavtigian, Marc S. Greenblatt, Fabienne Lesueur, Graham B. Byrnes, for the IARC Unclassified Genetic Variants Working Group

Abstract

Genetic testing for mutations in high-risk cancer susceptibility genes often reveals missense substitutions that are not easily classified as pathogenic or neutral. Among the methods that can help in their classification are computational analyses. Predictions of pathogenic vs. neutral, or the probability that a variant is pathogenic, can be made based on: 1) inferences from evolutionary conservation using protein multiple sequence alignments (PMSAs) of the gene of interest for almost any missense sequence variant; and 2) for many variants, structural features of wild-type and variant proteins. These in silico methods have improved considerably in recent years. In this work, we review and/or make suggestions with respect to: 1) the rationale for using in silico methods to help predict the consequences of missense variants; 2) important aspects of creating PMSAs that are informative for classification; 3) specific features of algorithms that have been used for classification of clinically-observed variants; 4) validation studies demonstrating that computational analyses can have predictive values (PVs) of approximately 75 to 95%; 5) current limitations of data sets and algorithms that need to be addressed to improve the computational classifiers; and 6) how in silico algorithms can be a part of the "integrated analysis" of multiple lines of evidence to help classify variants. We conclude that carefully validated computational algorithms, in the context of other evidence, can be an important tool for classification of missense variants.

Mendeley readers

Mendeley readers

The data shown below were compiled from readership statistics for 202 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
United States 3 1%
Germany 2 <1%
Colombia 2 <1%
Switzerland 1 <1%
Netherlands 1 <1%
India 1 <1%
France 1 <1%
Spain 1 <1%
United Kingdom 1 <1%
Other 0 0%
Unknown 189 94%

Demographic breakdown

Readers by professional status Count As %
Student > Ph. D. Student 48 24%
Researcher 34 17%
Student > Master 32 16%
Student > Bachelor 18 9%
Other 13 6%
Other 35 17%
Unknown 22 11%
Readers by discipline Count As %
Agricultural and Biological Sciences 69 34%
Biochemistry, Genetics and Molecular Biology 48 24%
Medicine and Dentistry 30 15%
Chemistry 5 2%
Computer Science 4 2%
Other 17 8%
Unknown 29 14%
Attention Score in Context

Attention Score in Context

This research output has an Altmetric Attention Score of 13. 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 27 July 2023.
All research outputs
#2,655,919
of 25,374,917 outputs
Outputs from Human Mutation
#142
of 2,982 outputs
Outputs of similar age
#7,917
of 103,631 outputs
Outputs of similar age from Human Mutation
#3
of 19 outputs
Altmetric has tracked 25,374,917 research outputs across all sources so far. Compared to these this one has done well and is in the 89th percentile: it's in the top 25% of all research outputs ever tracked by Altmetric.
So far Altmetric has tracked 2,982 research outputs from this source. They receive a mean Attention Score of 4.8. This one has done particularly well, scoring higher than 95% 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 103,631 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 92% of its contemporaries.
We're also able to compare this research output to 19 others from the same source and published within six weeks on either side of this one. This one has done well, scoring higher than 84% of its contemporaries.