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A comprehensive assessment of somatic mutation detection in cancer using whole-genome sequencing

Overview of attention for article published in Nature Communications, December 2015
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About this Attention Score

  • In the top 5% of all research outputs scored by Altmetric
  • High Attention Score compared to outputs of the same age (98th percentile)
  • High Attention Score compared to outputs of the same age and source (91st percentile)

Mentioned by

news
15 news outlets
twitter
68 X users
patent
2 patents
facebook
3 Facebook pages
googleplus
2 Google+ users

Citations

dimensions_citation
267 Dimensions

Readers on

mendeley
674 Mendeley
citeulike
8 CiteULike
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Title
A comprehensive assessment of somatic mutation detection in cancer using whole-genome sequencing
Published in
Nature Communications, December 2015
DOI 10.1038/ncomms10001
Pubmed ID
Authors

Tyler S. Alioto, Ivo Buchhalter, Sophia Derdak, Barbara Hutter, Matthew D. Eldridge, Eivind Hovig, Lawrence E. Heisler, Timothy A. Beck, Jared T. Simpson, Laurie Tonon, Anne-Sophie Sertier, Ann-Marie Patch, Natalie Jäger, Philip Ginsbach, Ruben Drews, Nagarajan Paramasivam, Rolf Kabbe, Sasithorn Chotewutmontri, Nicolle Diessl, Christopher Previti, Sabine Schmidt, Benedikt Brors, Lars Feuerbach, Michael Heinold, Susanne Gröbner, Andrey Korshunov, Patrick S. Tarpey, Adam P. Butler, Jonathan Hinton, David Jones, Andrew Menzies, Keiran Raine, Rebecca Shepherd, Lucy Stebbings, Jon W. Teague, Paolo Ribeca, Francesc Castro Giner, Sergi Beltran, Emanuele Raineri, Marc Dabad, Simon C. Heath, Marta Gut, Robert E. Denroche, Nicholas J. Harding, Takafumi N. Yamaguchi, Akihiro Fujimoto, Hidewaki Nakagawa, Víctor Quesada, Rafael Valdés-Mas, Sigve Nakken, Daniel Vodák, Lawrence Bower, Andrew G. Lynch, Charlotte L. Anderson, Nicola Waddell, John V. Pearson, Sean M. Grimmond, Myron Peto, Paul Spellman, Minghui He, Cyriac Kandoth, Semin Lee, John Zhang, Louis Létourneau, Singer Ma, Sahil Seth, David Torrents, Liu Xi, David A. Wheeler, Carlos López-Otín, Elías Campo, Peter J. Campbell, Paul C. Boutros, Xose S. Puente, Daniela S. Gerhard, Stefan M. Pfister, John D. McPherson, Thomas J. Hudson, Matthias Schlesner, Peter Lichter, Roland Eils, David T. W. Jones, Ivo G. Gut

Abstract

As whole-genome sequencing for cancer genome analysis becomes a clinical tool, a full understanding of the variables affecting sequencing analysis output is required. Here using tumour-normal sample pairs from two different types of cancer, chronic lymphocytic leukaemia and medulloblastoma, we conduct a benchmarking exercise within the context of the International Cancer Genome Consortium. We compare sequencing methods, analysis pipelines and validation methods. We show that using PCR-free methods and increasing sequencing depth to ∼100 × shows benefits, as long as the tumour:control coverage ratio remains balanced. We observe widely varying mutation call rates and low concordance among analysis pipelines, reflecting the artefact-prone nature of the raw data and lack of standards for dealing with the artefacts. However, we show that, using the benchmark mutation set we have created, many issues are in fact easy to remedy and have an immediate positive impact on mutation detection accuracy.

X Demographics

X Demographics

The data shown below were collected from the profiles of 68 X users who shared this research output. Click here to find out more about how the information was compiled.
Mendeley readers

Mendeley readers

The data shown below were compiled from readership statistics for 674 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
United States 7 1%
United Kingdom 5 <1%
Germany 4 <1%
Netherlands 3 <1%
Spain 2 <1%
Japan 2 <1%
Italy 2 <1%
France 1 <1%
India 1 <1%
Other 6 <1%
Unknown 641 95%

Demographic breakdown

Readers by professional status Count As %
Researcher 194 29%
Student > Ph. D. Student 128 19%
Student > Master 72 11%
Other 50 7%
Student > Bachelor 45 7%
Other 83 12%
Unknown 102 15%
Readers by discipline Count As %
Biochemistry, Genetics and Molecular Biology 201 30%
Agricultural and Biological Sciences 183 27%
Medicine and Dentistry 61 9%
Computer Science 47 7%
Engineering 11 2%
Other 51 8%
Unknown 120 18%
Attention Score in Context

Attention Score in Context

This research output has an Altmetric Attention Score of 152. 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 16 May 2023.
All research outputs
#274,476
of 25,837,817 outputs
Outputs from Nature Communications
#4,040
of 58,118 outputs
Outputs of similar age
#4,288
of 397,895 outputs
Outputs of similar age from Nature Communications
#59
of 678 outputs
Altmetric has tracked 25,837,817 research outputs across all sources so far. Compared to these this one has done particularly well and is in the 98th percentile: it's in the top 5% of all research outputs ever tracked by Altmetric.
So far Altmetric has tracked 58,118 research outputs from this source. They typically receive a lot more attention than average, with a mean Attention Score of 55.5. This one has done particularly well, scoring higher than 93% 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 397,895 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 98% of its contemporaries.
We're also able to compare this research output to 678 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 91% of its contemporaries.