Name
Abstract | ||
|---|---|---|
Motivation: Versatile and efficient variant calling tools are needed to analyze large scale sequencing datasets. In particular, identification of copy number changes remains a challenging task due to their complexity, susceptibility to sequencing biases, variation in coverage data and dependence on genome-wide sample properties, such as tumor polyploidy or polyclonality in cancer samples. Results: We have developed a new tool, Canvas, for identification of copy number changes from diverse sequencing experiments including whole-genome matched tumor-normal and single-sample normal re-sequencing, as well as whole-exome matched and unmatched tumor-normal studies. In addition to variant calling, Canvas infers genome-wide parameters such as cancer ploidy, purity and heterogeneity. It provides fast and easy-to-run workflows that can scale to thousands of samples and can be easily incorporated into variant calling pipelines. Availability and Implementation: Canvas is distributed under an open source license and can be downloaded from https://github.om/ Illumina/canvas. |
Year | DOI | Venue |
|---|---|---|
2016 | 10.1093/bioinformatics/btw163 | BIOINFORMATICS |
Field | DocType | Volume |
Data mining,Copy-number variation,Computer science,Coverage data,Throughput,Bioinformatics,Workflow,Scalability | Journal | 32 |
Issue | ISSN | Citations |
15 | 1367-4803 | 2 |
PageRank | References | Authors |
0.51 | 3 | 5 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Eric Roller | 1 | 2 | 0.84 |
| Sergii Ivakhno | 2 | 47 | 4.11 |
| Steve Lee | 3 | 2 | 0.51 |
| Thomas E. Royce | 4 | 51 | 5.23 |
| Stephen Tanner | 5 | 4 | 0.96 |