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.
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Chong, Z., Chen, K. (2018). Structural Variant Breakpoint Detection with novoBreak. In: Bickhart, D. (eds) Copy Number Variants. Methods in Molecular Biology, vol 1833. Humana Press, New York, NY. https://doi.org/10.1007/978-1-4939-8666-8_10
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DOI: https://doi.org/10.1007/978-1-4939-8666-8_10
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