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CloneCNA: detecting subclonal somatic copy number alterations in heterogeneous tumor samples from whole-exome sequencing data

Overview of attention for article published in BMC Bioinformatics, August 2016
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3 tweeters

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54 Mendeley
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Title
CloneCNA: detecting subclonal somatic copy number alterations in heterogeneous tumor samples from whole-exome sequencing data
Published in
BMC Bioinformatics, August 2016
DOI 10.1186/s12859-016-1174-7
Pubmed ID
Authors

Zhenhua Yu, Ao Li, Minghui Wang

Abstract

Copy number alteration is a main genetic structural variation that plays an important role in tumor initialization and progression. Accurate detection of copy number alterations is necessary for discovering cancer-causing genes. Whole-exome sequencing has become a widely used technology in the last decade for detecting various types of genomic aberrations in cancer genomes. However, there are several major issues encountered in these detection problems, including normal cell contamination, tumor aneuploidy, and intra-tumor heterogeneity. Especially, deciphering the intra-tumor heterogeneity is imperative for identifying clonal and subclonal copy number alterations. We introduce CloneCNA, a novel bioinformatics tool for efficiently addressing these issues and automatically detecting clonal and subclonal somatic copy number alterations from heterogeneous tumor samples. CloneCNA fully explores the log ratio of read counts between paired tumor-normal samples and tumor B allele frequency of germline heterozygous SNP positions, further employs efficient statistical models to quantitatively represent copy number status of tumor sample containing multiple clones. We examine CloneCNA on simulated heterogeneous and real tumor samples, and the results demonstrate that CloneCNA has higher power to detect copy number alterations than existing methods. CloneCNA, a novel algorithm is developed to efficiently and accurately identify somatic copy number alterations from heterogeneous tumor samples. We demonstrate the statistical framework of CloneCNA represents a remarkable advance for tumor whole-exome sequencing data. We expect that CloneCNA will promote cancer-focused studies for investigating the role of clonal evolution and elucidating critical events benefiting tumor tumourigenesis and progression.

Twitter Demographics

The data shown below were collected from the profiles of 3 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 54 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
Unknown 54 100%

Demographic breakdown

Readers by professional status Count As %
Researcher 15 28%
Student > Bachelor 11 20%
Student > Ph. D. Student 9 17%
Student > Master 5 9%
Student > Doctoral Student 4 7%
Other 5 9%
Unknown 5 9%
Readers by discipline Count As %
Biochemistry, Genetics and Molecular Biology 21 39%
Agricultural and Biological Sciences 15 28%
Computer Science 7 13%
Engineering 2 4%
Medicine and Dentistry 2 4%
Other 2 4%
Unknown 5 9%

Attention Score in Context

This research output has an Altmetric Attention Score of 1. 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 20 August 2016.
All research outputs
#12,619,755
of 16,534,657 outputs
Outputs from BMC Bioinformatics
#4,688
of 5,960 outputs
Outputs of similar age
#177,070
of 268,388 outputs
Outputs of similar age from BMC Bioinformatics
#17
of 29 outputs
Altmetric has tracked 16,534,657 research outputs across all sources so far. This one is in the 20th percentile – i.e., 20% of other outputs scored the same or lower than it.
So far Altmetric has tracked 5,960 research outputs from this source. They typically receive a little more attention than average, with a mean Attention Score of 5.1. This one is in the 15th percentile – i.e., 15% 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 268,388 tracked outputs that were published within six weeks on either side of this one in any source. This one is in the 28th percentile – i.e., 28% of its contemporaries scored the same or lower than it.
We're also able to compare this research output to 29 others from the same source and published within six weeks on either side of this one. This one is in the 37th percentile – i.e., 37% of its contemporaries scored the same or lower than it.