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Benchmarking mutation effect prediction algorithms using functionally validated cancer-related missense mutations

Overview of attention for article published in Genome Biology (Online Edition), October 2014
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  • In the top 25% of all research outputs scored by Altmetric
  • Good Attention Score compared to outputs of the same age (77th percentile)

Mentioned by

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8 tweeters

Citations

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103 Dimensions

Readers on

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124 Mendeley
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4 CiteULike
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Title
Benchmarking mutation effect prediction algorithms using functionally validated cancer-related missense mutations
Published in
Genome Biology (Online Edition), October 2014
DOI 10.1186/s13059-014-0484-1
Pubmed ID
Authors

Luciano G Martelotto, Charlotte KY Ng, Maria R De Filippo, Yan Zhang, Salvatore Piscuoglio, Raymond S Lim, Ronglai Shen, Larry Norton, Jorge S Reis-Filho, Britta Weigelt

Abstract

BackgroundMassively parallel sequencing studies have led to the identification of a large number of mutations present in a minority of cancers of a given site. Hence, methods to identify the likely pathogenic mutations that are worth exploring experimentally and clinically are required. We sought to compare the performance of 15 mutation effect prediction algorithms and their agreement. As a hypothesis-generating aim, we sought to define whether combinations of prediction algorithms would improve the functional effect predictions of specific mutations.ResultsLiterature and database mining of single nucleotide variants (SNVs) affecting 15 cancer genes was performed to identify mutations supported by functional evidence or hereditary disease association to be classified either as non-neutral (n¿=¿849) or neutral (n¿=¿140) with respect to their impact on protein function. These SNVs were employed to test the performance of 15 mutation effect prediction algorithms. The accuracy of the prediction algorithms varies considerably. Although all algorithms perform consistently well in terms of positive predictive value, their negative predictive value varies substantially. Cancer-specific mutation effect predictors display no-to-almost perfect agreement in their predictions of these SNVs, whereas the non-cancer-specific predictors showed no-to-moderate agreement. Combinations of predictors modestly improve accuracy and significantly improve negative predictive values.ConclusionsThe information provided by mutation effect predictors is not equivalent. No algorithm is able to predict sufficiently accurately SNVs that should be taken forward for experimental or clinical testing. Combining algorithms aggregates orthogonal information and may result in improvements in the negative predictive value of mutation effect predictions.

Twitter Demographics

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Mendeley readers

The data shown below were compiled from readership statistics for 124 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
United States 5 4%
Spain 3 2%
Australia 1 <1%
Brazil 1 <1%
Unknown 114 92%

Demographic breakdown

Readers by professional status Count As %
Student > Ph. D. Student 36 29%
Researcher 29 23%
Student > Master 15 12%
Student > Bachelor 12 10%
Other 7 6%
Other 15 12%
Unknown 10 8%
Readers by discipline Count As %
Biochemistry, Genetics and Molecular Biology 38 31%
Agricultural and Biological Sciences 35 28%
Computer Science 15 12%
Medicine and Dentistry 11 9%
Engineering 4 3%
Other 8 6%
Unknown 13 10%

Attention Score in Context

This research output has an Altmetric Attention Score of 5. 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 04 January 2015.
All research outputs
#3,040,168
of 12,486,858 outputs
Outputs from Genome Biology (Online Edition)
#1,866
of 2,835 outputs
Outputs of similar age
#63,710
of 281,469 outputs
Outputs of similar age from Genome Biology (Online Edition)
#71
of 90 outputs
Altmetric has tracked 12,486,858 research outputs across all sources so far. Compared to these this one has done well and is in the 75th percentile: it's in the top 25% of all research outputs ever tracked by Altmetric.
So far Altmetric has tracked 2,835 research outputs from this source. They typically receive a lot more attention than average, with a mean Attention Score of 22.9. This one is in the 33rd percentile – i.e., 33% 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 281,469 tracked outputs that were published within six weeks on either side of this one in any source. This one has done well, scoring higher than 77% of its contemporaries.
We're also able to compare this research output to 90 others from the same source and published within six weeks on either side of this one. This one is in the 21st percentile – i.e., 21% of its contemporaries scored the same or lower than it.