Title |
Detecting somatic point mutations in cancer genome sequencing data: a comparison of mutation callers
|
---|---|
Published in |
Genome Medicine, October 2013
|
DOI | 10.1186/gm495 |
Pubmed ID | |
Authors |
Qingguo Wang, Peilin Jia, Fei Li, Haiquan Chen, Hongbin Ji, Donald Hucks, Kimberly Brown Dahlman, William Pao, Zhongming Zhao |
Abstract |
Driven by high throughput next generation sequencing technologies and the pressing need to decipher cancer genomes, computational approaches for detecting somatic single nucleotide variants (sSNVs) have undergone dramatic improvements during the past 2 years. The recently developed tools typically compare a tumor sample directly with a matched normal sample at each variant locus in order to increase the accuracy of sSNV calling. These programs also address the detection of sSNVs at low allele frequencies, allowing for the study of tumor heterogeneity, cancer subclones, and mutation evolution in cancer development. |
X Demographics
Geographical breakdown
Country | Count | As % |
---|---|---|
United Kingdom | 3 | 33% |
Bahrain | 1 | 11% |
France | 1 | 11% |
United States | 1 | 11% |
India | 1 | 11% |
Unknown | 2 | 22% |
Demographic breakdown
Type | Count | As % |
---|---|---|
Scientists | 4 | 44% |
Members of the public | 4 | 44% |
Science communicators (journalists, bloggers, editors) | 1 | 11% |
Mendeley readers
Geographical breakdown
Country | Count | As % |
---|---|---|
United States | 13 | 4% |
Netherlands | 5 | 1% |
United Kingdom | 5 | 1% |
Australia | 3 | <1% |
China | 3 | <1% |
Italy | 2 | <1% |
Brazil | 2 | <1% |
Germany | 2 | <1% |
Sweden | 1 | <1% |
Other | 5 | 1% |
Unknown | 304 | 88% |
Demographic breakdown
Readers by professional status | Count | As % |
---|---|---|
Researcher | 100 | 29% |
Student > Ph. D. Student | 77 | 22% |
Student > Master | 36 | 10% |
Student > Bachelor | 28 | 8% |
Other | 23 | 7% |
Other | 48 | 14% |
Unknown | 33 | 10% |
Readers by discipline | Count | As % |
---|---|---|
Agricultural and Biological Sciences | 146 | 42% |
Biochemistry, Genetics and Molecular Biology | 59 | 17% |
Computer Science | 36 | 10% |
Medicine and Dentistry | 31 | 9% |
Engineering | 9 | 3% |
Other | 25 | 7% |
Unknown | 39 | 11% |