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SomInaClust: detection of cancer genes based on somatic mutation patterns of inactivation and clustering

Overview of attention for article published in BMC Bioinformatics, April 2015
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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 (81st percentile)

Mentioned by

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9 tweeters
patent
1 patent

Citations

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32 Dimensions

Readers on

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65 Mendeley
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Title
SomInaClust: detection of cancer genes based on somatic mutation patterns of inactivation and clustering
Published in
BMC Bioinformatics, April 2015
DOI 10.1186/s12859-015-0555-7
Pubmed ID
Authors

Jimmy Van den Eynden, Ana Carolina Fierro, Lieven PC Verbeke, Kathleen Marchal

Abstract

With the advances in high throughput technologies, increasing amounts of cancer somatic mutation data are being generated and made available. Only a small number of (driver) mutations occur in driver genes and are responsible for carcinogenesis, while the majority of (passenger) mutations do not influence tumour biology. In this study, SomInaClust is introduced, a method that accurately identifies driver genes based on their mutation pattern across tumour samples and then classifies them into oncogenes or tumour suppressor genes respectively. SomInaClust starts from the observation that oncogenes mainly contain mutations that, due to positive selection, cluster at similar positions in a gene across patient samples, whereas tumour suppressor genes contain a high number of protein-truncating mutations throughout the entire gene length. The method was shown to prioritize driver genes in 9 different solid cancers. Furthermore it was found to be complementary to existing similar-purpose methods with the additional advantages that it has a higher sensitivity, also for rare mutations (occurring in less than 1% of all samples), and it accurately classifies candidate driver genes in putative oncogenes and tumour suppressor genes. Pathway enrichment analysis showed that the identified genes belong to known cancer signalling pathways, and that the distinction between oncogenes and tumour suppressor genes is biologically relevant. SomInaClust was shown to detect candidate driver genes based on somatic mutation patterns of inactivation and clustering and to distinguish oncogenes from tumour suppressor genes. The method could be used for the identification of new cancer genes or to filter mutation data for further data-integration purposes.

Twitter Demographics

The data shown below were collected from the profiles of 9 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 65 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
United States 1 2%
Belgium 1 2%
Unknown 63 97%

Demographic breakdown

Readers by professional status Count As %
Student > Ph. D. Student 14 22%
Researcher 13 20%
Student > Master 12 18%
Student > Bachelor 9 14%
Other 3 5%
Other 7 11%
Unknown 7 11%
Readers by discipline Count As %
Biochemistry, Genetics and Molecular Biology 22 34%
Agricultural and Biological Sciences 19 29%
Computer Science 7 11%
Medicine and Dentistry 5 8%
Engineering 2 3%
Other 2 3%
Unknown 8 12%

Attention Score in Context

This research output has an Altmetric Attention Score of 8. 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 19 October 2017.
All research outputs
#3,252,998
of 19,211,930 outputs
Outputs from BMC Bioinformatics
#1,330
of 6,554 outputs
Outputs of similar age
#45,013
of 239,343 outputs
Outputs of similar age from BMC Bioinformatics
#1
of 1 outputs
Altmetric has tracked 19,211,930 research outputs across all sources so far. Compared to these this one has done well and is in the 83rd percentile: it's in the top 25% of all research outputs ever tracked by Altmetric.
So far Altmetric has tracked 6,554 research outputs from this source. They typically receive a little more attention than average, with a mean Attention Score of 5.3. This one has done well, scoring higher than 79% 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 239,343 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 81% of its contemporaries.
We're also able to compare this research output to 1 others from the same source and published within six weeks on either side of this one. This one has scored higher than all of them