| Chapter title |
Structural Variant Breakpoint Detection with novoBreak
|
|---|---|
| Chapter number | 10 |
| Book title |
Copy Number Variants
|
| Published in |
Methods in molecular biology, January 2018
|
| DOI | 10.1007/978-1-4939-8666-8_10 |
| Pubmed ID | |
| Book ISBNs |
978-1-4939-8665-1, 978-1-4939-8666-8
|
| Authors |
Zechen Chong, Ken Chen, Chong, Zechen, Chen, Ken |
| Abstract |
Structural variations (SVs) are an important type of genomic variants and always play a critical role for cancer development and progression. In the cancer genomics era, detecting structural variations from short sequencing data is still challenging. We developed a novel algorithm, novoBreak (Chong et al. Nat Methods 14:65-67, 2017), which achieved the highest balanced accuracy (mean of sensitivity and precision) in the ICGC-TCGA DREAM 8.5 Somatic Mutation Calling Challenge. Here we describe detailed instructions of applying novoBreak ( https://github.com/czc/nb_distribution ), an open-source software, for somatic SVs detection. We also briefly introduce how to detect germline SVs using novoBreak pipeline and how to use the Workflow ( https://cgc.sbgenomics.com/public/apps#ZCHONG/novobreak-commit/novobreak-analysis/ ) of novoBreak on the Seven Bridges Cancer Genomics Cloud. |
Mendeley readers
Geographical breakdown
| Country | Count | As % |
|---|---|---|
| Unknown | 9 | 100% |
Demographic breakdown
| Readers by professional status | Count | As % |
|---|---|---|
| Researcher | 3 | 33% |
| Unspecified | 1 | 11% |
| Student > Doctoral Student | 1 | 11% |
| Student > Ph. D. Student | 1 | 11% |
| Student > Bachelor | 1 | 11% |
| Other | 2 | 22% |
| Readers by discipline | Count | As % |
|---|---|---|
| Biochemistry, Genetics and Molecular Biology | 3 | 33% |
| Agricultural and Biological Sciences | 2 | 22% |
| Unspecified | 1 | 11% |
| Engineering | 1 | 11% |
| Unknown | 2 | 22% |