@inbook{206f9f86610047d194aafbd7d479610f,
title = "Structural variant breakpoint detection with novoBreak",
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.",
keywords = "Algorithm, DNA sequence analysis, De novo assembly, Genetic variation, Genomic rearrangement, Next generation sequencing data analysis, Structural variations, k-mer",
author = "Zechen Chong and Ken Chen",
note = "Publisher Copyright: {\textcopyright} Springer International Publishing AG, part of Springer Nature 2018.",
year = "2018",
doi = "10.1007/978-1-4939-8666-8_10",
language = "English (US)",
series = "Methods in Molecular Biology",
publisher = "Humana Press Inc.",
pages = "129--141",
booktitle = "Methods in Molecular Biology",
}