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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, October 2014
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  • Good Attention Score compared to outputs of the same age (70th percentile)

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8 X users

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Title
Benchmarking mutation effect prediction algorithms using functionally validated cancer-related missense mutations
Published in
Genome Biology, 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.

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X Demographics

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Mendeley readers

Mendeley readers

The data shown below were compiled from readership statistics for 138 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 128 93%

Demographic breakdown

Readers by professional status Count As %
Student > Ph. D. Student 37 27%
Researcher 29 21%
Student > Master 16 12%
Student > Bachelor 14 10%
Other 8 6%
Other 17 12%
Unknown 17 12%
Readers by discipline Count As %
Biochemistry, Genetics and Molecular Biology 41 30%
Agricultural and Biological Sciences 35 25%
Computer Science 15 11%
Medicine and Dentistry 12 9%
Engineering 5 4%
Other 9 7%
Unknown 21 15%
Attention Score in Context

Attention Score in Context

This research output has an Altmetric Attention Score of 4. 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
#7,356,343
of 25,374,917 outputs
Outputs from Genome Biology
#3,306
of 4,467 outputs
Outputs of similar age
#76,623
of 274,082 outputs
Outputs of similar age from Genome Biology
#87
of 119 outputs
Altmetric has tracked 25,374,917 research outputs across all sources so far. This one has received more attention than most of these and is in the 69th percentile.
So far Altmetric has tracked 4,467 research outputs from this source. They typically receive a lot more attention than average, with a mean Attention Score of 27.6. This one is in the 24th percentile – i.e., 24% 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 274,082 tracked outputs that were published within six weeks on either side of this one in any source. This one has gotten more attention than average, scoring higher than 70% of its contemporaries.
We're also able to compare this research output to 119 others from the same source and published within six weeks on either side of this one. This one is in the 26th percentile – i.e., 26% of its contemporaries scored the same or lower than it.