Title |
PhyloWGS: Reconstructing subclonal composition and evolution from whole-genome sequencing of tumors
|
---|---|
Published in |
Genome Biology, February 2015
|
DOI | 10.1186/s13059-015-0602-8 |
Pubmed ID | |
Authors |
Amit G Deshwar, Shankar Vembu, Christina K Yung, Gun Ho Jang, Lincoln Stein, Quaid Morris |
Abstract |
Tumors often contain multiple subpopulations of cancerous cells defined by distinct somatic mutations. We describe a new method, PhyloWGS, which can be applied to whole-genome sequencing data from one or more tumor samples to reconstruct complete genotypes of these subpopulations based on variant allele frequencies (VAFs) of point mutations and population frequencies of structural variations. We introduce a principled phylogenic correction for VAFs in loci affected by copy number alterations and we show that this correction greatly improves subclonal reconstruction compared to existing methods. PhyloWGS is free, open-source software, available at https://github.com/morrislab/phylowgs. |
X Demographics
Geographical breakdown
Country | Count | As % |
---|---|---|
United Kingdom | 6 | 19% |
United States | 6 | 19% |
India | 3 | 9% |
Canada | 3 | 9% |
Australia | 2 | 6% |
Austria | 1 | 3% |
Japan | 1 | 3% |
Unknown | 10 | 31% |
Demographic breakdown
Type | Count | As % |
---|---|---|
Scientists | 20 | 63% |
Members of the public | 10 | 31% |
Science communicators (journalists, bloggers, editors) | 1 | 3% |
Practitioners (doctors, other healthcare professionals) | 1 | 3% |
Mendeley readers
Geographical breakdown
Country | Count | As % |
---|---|---|
United States | 12 | 3% |
Germany | 2 | <1% |
Italy | 2 | <1% |
Sweden | 2 | <1% |
Canada | 2 | <1% |
United Kingdom | 2 | <1% |
Korea, Republic of | 1 | <1% |
Ghana | 1 | <1% |
Spain | 1 | <1% |
Other | 1 | <1% |
Unknown | 428 | 94% |
Demographic breakdown
Readers by professional status | Count | As % |
---|---|---|
Student > Ph. D. Student | 112 | 25% |
Researcher | 108 | 24% |
Student > Bachelor | 42 | 9% |
Student > Master | 38 | 8% |
Student > Doctoral Student | 24 | 5% |
Other | 60 | 13% |
Unknown | 70 | 15% |
Readers by discipline | Count | As % |
---|---|---|
Biochemistry, Genetics and Molecular Biology | 121 | 27% |
Agricultural and Biological Sciences | 118 | 26% |
Computer Science | 53 | 12% |
Medicine and Dentistry | 26 | 6% |
Mathematics | 21 | 5% |
Other | 39 | 9% |
Unknown | 76 | 17% |