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A cancer cell-line titration series for evaluating somatic classification

Overview of attention for article published in BMC Research Notes, December 2015
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  • Above-average Attention Score compared to outputs of the same age (54th percentile)
  • Above-average Attention Score compared to outputs of the same age and source (59th percentile)

Mentioned by

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4 tweeters

Citations

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9 Dimensions

Readers on

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22 Mendeley
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Title
A cancer cell-line titration series for evaluating somatic classification
Published in
BMC Research Notes, December 2015
DOI 10.1186/s13104-015-1803-7
Pubmed ID
Authors

Robert E. Denroche, Laura Mullen, Lee Timms, Timothy Beck, Christina K. Yung, Lincoln Stein, John D. McPherson, Andrew M. K. Brown

Abstract

Accurate detection of somatic single nucleotide variants and small insertions and deletions from DNA sequencing experiments of tumour-normal pairs is a challenging task. Tumour samples are often contaminated with normal cells confounding the available evidence for the somatic variants. Furthermore, tumours are heterogeneous so sub-clonal variants are observed at reduced allele frequencies. We present here a cell-line titration series dataset that can be used to evaluate somatic variant calling pipelines with the goal of reliably calling true somatic mutations at low allele frequencies. Cell-line DNA was mixed with matched normal DNA at 8 different ratios to generate samples with known tumour cellularities, and exome sequenced on Illumina HiSeq to depths of >300×. The data was processed with several different variant calling pipelines and verification experiments were performed to assay >1500 somatic variant candidates using Ion Torrent PGM as an orthogonal technology. By examining the variants called at varying cellularities and depths of coverage, we show that the best performing pipelines are able to maintain a high level of precision at any cellularity. In addition, we estimate the number of true somatic variants undetected as cellularity and coverage decrease. Our cell-line titration series dataset, along with the associated verification results, was effective for this evaluation and will serve as a valuable dataset for future somatic calling algorithm development. The data is available for further analysis at the European Genome-phenome Archive under accession number EGAS00001001016. Data access requires registration through the International Cancer Genome Consortium's Data Access Compliance Office (ICGC DACO).

Twitter Demographics

The data shown below were collected from the profiles of 4 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 22 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
Unknown 22 100%

Demographic breakdown

Readers by professional status Count As %
Researcher 8 36%
Student > Ph. D. Student 4 18%
Student > Bachelor 2 9%
Student > Master 2 9%
Other 1 5%
Other 3 14%
Unknown 2 9%
Readers by discipline Count As %
Agricultural and Biological Sciences 10 45%
Biochemistry, Genetics and Molecular Biology 5 23%
Medicine and Dentistry 3 14%
Computer Science 1 5%
Neuroscience 1 5%
Other 0 0%
Unknown 2 9%

Attention Score in Context

This research output has an Altmetric Attention Score of 2. 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 06 January 2016.
All research outputs
#9,541,704
of 16,534,657 outputs
Outputs from BMC Research Notes
#1,446
of 3,575 outputs
Outputs of similar age
#164,725
of 373,625 outputs
Outputs of similar age from BMC Research Notes
#157
of 412 outputs
Altmetric has tracked 16,534,657 research outputs across all sources so far. This one is in the 41st percentile – i.e., 41% of other outputs scored the same or lower than it.
So far Altmetric has tracked 3,575 research outputs from this source. They receive a mean Attention Score of 4.6. This one has gotten more attention than average, scoring higher than 57% 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 373,625 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 54% of its contemporaries.
We're also able to compare this research output to 412 others from the same source and published within six weeks on either side of this one. This one has gotten more attention than average, scoring higher than 59% of its contemporaries.