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ISOWN: accurate somatic mutation identification in the absence of normal tissue controls

Overview of attention for article published in Genome Medicine, June 2017
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About this Attention Score

  • In the top 25% of all research outputs scored by Altmetric
  • High Attention Score compared to outputs of the same age (84th percentile)
  • Above-average Attention Score compared to outputs of the same age and source (62nd percentile)

Mentioned by

twitter
20 tweeters

Citations

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28 Dimensions

Readers on

mendeley
95 Mendeley
citeulike
3 CiteULike
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Title
ISOWN: accurate somatic mutation identification in the absence of normal tissue controls
Published in
Genome Medicine, June 2017
DOI 10.1186/s13073-017-0446-9
Pubmed ID
Authors

Irina Kalatskaya, Quang M. Trinh, Melanie Spears, John D. McPherson, John M. S. Bartlett, Lincoln Stein

Abstract

A key step in cancer genome analysis is the identification of somatic mutations in the tumor. This is typically done by comparing the genome of the tumor to the reference genome sequence derived from a normal tissue taken from the same donor. However, there are a variety of common scenarios in which matched normal tissue is not available for comparison. In this work, we describe an algorithm to distinguish somatic single nucleotide variants (SNVs) in next-generation sequencing data from germline polymorphisms in the absence of normal samples using a machine learning approach. Our algorithm was evaluated using a family of supervised learning classifications across six different cancer types and ~1600 samples, including cell lines, fresh frozen tissues, and formalin-fixed paraffin-embedded tissues; we tested our algorithm with both deep targeted and whole-exome sequencing data. Our algorithm correctly classified between 95 and 98% of somatic mutations with F1-measure ranges from 75.9 to 98.6% depending on the tumor type. We have released the algorithm as a software package called ISOWN (Identification of SOmatic mutations Without matching Normal tissues). In this work, we describe the development, implementation, and validation of ISOWN, an accurate algorithm for predicting somatic mutations in cancer tissues in the absence of matching normal tissues. ISOWN is available as Open Source under Apache License 2.0 from https://github.com/ikalatskaya/ISOWN .

Twitter Demographics

The data shown below were collected from the profiles of 20 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 95 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
Unknown 95 100%

Demographic breakdown

Readers by professional status Count As %
Researcher 29 31%
Student > Ph. D. Student 13 14%
Student > Master 11 12%
Student > Doctoral Student 7 7%
Student > Bachelor 6 6%
Other 14 15%
Unknown 15 16%
Readers by discipline Count As %
Biochemistry, Genetics and Molecular Biology 36 38%
Agricultural and Biological Sciences 17 18%
Computer Science 8 8%
Engineering 6 6%
Medicine and Dentistry 4 4%
Other 7 7%
Unknown 17 18%

Attention Score in Context

This research output has an Altmetric Attention Score of 12. 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 03 August 2017.
All research outputs
#1,927,793
of 17,364,317 outputs
Outputs from Genome Medicine
#465
of 1,156 outputs
Outputs of similar age
#43,714
of 273,815 outputs
Outputs of similar age from Genome Medicine
#4
of 8 outputs
Altmetric has tracked 17,364,317 research outputs across all sources so far. Compared to these this one has done well and is in the 88th percentile: it's in the top 25% of all research outputs ever tracked by Altmetric.
So far Altmetric has tracked 1,156 research outputs from this source. They typically receive a lot more attention than average, with a mean Attention Score of 23.4. This one has gotten more attention than average, scoring higher than 59% 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 273,815 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 84% of its contemporaries.
We're also able to compare this research output to 8 others from the same source and published within six weeks on either side of this one. This one has scored higher than 4 of them.