Title |
Comparing somatic mutation-callers: beyond Venn diagrams
|
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Published in |
BMC Bioinformatics, June 2013
|
DOI | 10.1186/1471-2105-14-189 |
Pubmed ID | |
Authors |
Su Yeon Kim, Terence P Speed |
Abstract |
Somatic mutation-calling based on DNA from matched tumor-normal patient samples is one of the key tasks carried by many cancer genome projects. One such large-scale project is The Cancer Genome Atlas (TCGA), which is now routinely compiling catalogs of somatic mutations from hundreds of paired tumor-normal DNA exome-sequence data. Nonetheless, mutation calling is still very challenging. TCGA benchmark studies revealed that even relatively recent mutation callers from major centers showed substantial discrepancies. Evaluation of the mutation callers or understanding the sources of discrepancies is not straightforward, since for most tumor studies, validation data based on independent whole-exome DNA sequencing is not available, only partial validation data for a selected (ascertained) subset of sites. |
X Demographics
Geographical breakdown
Country | Count | As % |
---|---|---|
United States | 9 | 24% |
Canada | 4 | 11% |
France | 3 | 8% |
United Kingdom | 3 | 8% |
Switzerland | 2 | 5% |
Germany | 2 | 5% |
Malawi | 1 | 3% |
Korea, Republic of | 1 | 3% |
Norway | 1 | 3% |
Other | 1 | 3% |
Unknown | 10 | 27% |
Demographic breakdown
Type | Count | As % |
---|---|---|
Scientists | 24 | 65% |
Members of the public | 12 | 32% |
Science communicators (journalists, bloggers, editors) | 1 | 3% |
Mendeley readers
Geographical breakdown
Country | Count | As % |
---|---|---|
United States | 16 | 7% |
United Kingdom | 5 | 2% |
Netherlands | 4 | 2% |
France | 3 | 1% |
Germany | 2 | <1% |
Italy | 2 | <1% |
Australia | 2 | <1% |
Spain | 2 | <1% |
Switzerland | 1 | <1% |
Other | 4 | 2% |
Unknown | 196 | 83% |
Demographic breakdown
Readers by professional status | Count | As % |
---|---|---|
Researcher | 89 | 38% |
Student > Ph. D. Student | 57 | 24% |
Student > Master | 17 | 7% |
Other | 15 | 6% |
Student > Doctoral Student | 10 | 4% |
Other | 31 | 13% |
Unknown | 18 | 8% |
Readers by discipline | Count | As % |
---|---|---|
Agricultural and Biological Sciences | 107 | 45% |
Biochemistry, Genetics and Molecular Biology | 43 | 18% |
Computer Science | 25 | 11% |
Medicine and Dentistry | 16 | 7% |
Mathematics | 7 | 3% |
Other | 17 | 7% |
Unknown | 22 | 9% |