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FLAGS, frequently mutated genes in public exomes

Overview of attention for article published in BMC Medical Genomics, December 2014
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About this Attention Score

  • In the top 5% of all research outputs scored by Altmetric
  • Among the highest-scoring outputs from this source (#22 of 1,029)
  • High Attention Score compared to outputs of the same age (95th percentile)
  • High Attention Score compared to outputs of the same age and source (98th percentile)

Mentioned by

twitter
42 tweeters
patent
1 patent

Citations

dimensions_citation
66 Dimensions

Readers on

mendeley
158 Mendeley
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3 CiteULike
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Title
FLAGS, frequently mutated genes in public exomes
Published in
BMC Medical Genomics, December 2014
DOI 10.1186/s12920-014-0064-y
Pubmed ID
Authors

Casper Shyr, Maja Tarailo-Graovac, Michael Gottlieb, Jessica JY Lee, Clara van Karnebeek, Wyeth W Wasserman

Abstract

BackgroundDramatic improvements in DNA-sequencing technologies and computational analyses have led to wide use of whole exome sequencing (WES) to identify the genetic basis of Mendelian disorders. More than 180 novel rare-disease-causing genes with Mendelian inheritance patterns have been discovered through sequencing the exomes of just a few unrelated individuals or family members. As rare/novel genetic variants continue to be uncovered, there is a major challenge in distinguishing true pathogenic variants from rare benign mutations.MethodsWe used publicly available exome cohorts, together with the dbSNP database, to derive a list of genes (n¿=¿100) that most frequently exhibit rare (<1%) non-synonymous/splice-site variants in general populations. We termed these genes FLAGS for FrequentLy mutAted GeneS and analyzed their properties.ResultsAnalysis of FLAGS revealed that these genes have significantly longer protein coding sequences, a greater number of paralogs and display less evolutionarily selective pressure than expected. FLAGS are more frequently reported in PubMed clinical literature and more frequently associated with diseased phenotypes compared to the set of human protein-coding genes. We demonstrated an overlap between FLAGS and the rare-disease causing genes recently discovered through WES studies (n¿=¿10) and the need for replication studies and rigorous statistical and biological analyses when associating FLAGS to rare disease. Finally, we showed how FLAGS are applied in disease-causing variant prioritization approach on exome data from a family affected by an unknown rare genetic disorder.ConclusionsWe showed that some genes are frequently affected by rare, likely functional variants in general population, and are frequently observed in WES studies analyzing diverse rare phenotypes. We found that the rate at which genes accumulate rare mutations is beneficial information for prioritizing candidates. We provided a ranking system based on the mutation accumulation rates for prioritizing exome-captured human genes, and propose that clinical reports associating any disease/phenotype to FLAGS be evaluated with extra caution.

Twitter Demographics

The data shown below were collected from the profiles of 42 tweeters who shared this research output. Click here to find out more about how the information was compiled.

Mendeley readers

The data shown below were compiled from readership statistics for 158 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
United Kingdom 3 2%
Canada 1 <1%
Italy 1 <1%
Spain 1 <1%
United States 1 <1%
Unknown 151 96%

Demographic breakdown

Readers by professional status Count As %
Student > Ph. D. Student 40 25%
Researcher 31 20%
Student > Bachelor 20 13%
Student > Master 17 11%
Other 8 5%
Other 22 14%
Unknown 20 13%
Readers by discipline Count As %
Agricultural and Biological Sciences 51 32%
Biochemistry, Genetics and Molecular Biology 43 27%
Medicine and Dentistry 17 11%
Computer Science 7 4%
Engineering 3 2%
Other 12 8%
Unknown 25 16%

Attention Score in Context

This research output has an Altmetric Attention Score of 29. 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 28 May 2020.
All research outputs
#948,266
of 19,491,641 outputs
Outputs from BMC Medical Genomics
#22
of 1,029 outputs
Outputs of similar age
#16,159
of 331,995 outputs
Outputs of similar age from BMC Medical Genomics
#1
of 51 outputs
Altmetric has tracked 19,491,641 research outputs across all sources so far. Compared to these this one has done particularly well and is in the 95th percentile: it's in the top 5% of all research outputs ever tracked by Altmetric.
So far Altmetric has tracked 1,029 research outputs from this source. They receive a mean Attention Score of 4.6. This one has done particularly well, scoring higher than 97% 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 331,995 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 95% of its contemporaries.
We're also able to compare this research output to 51 others from the same source and published within six weeks on either side of this one. This one has done particularly well, scoring higher than 98% of its contemporaries.