CELLO: a longitudinal data analysis toolbox untangling cancer evolution

• 作者: Biaobin Jiang Dong Song Jiguang Wang Quanhua Mu
• 来源期刊: 定量生物学（英文版）
• 年，卷（期）: 2020,（3）
• 所属期刊栏目: PROTOCOL AND TUTORIALS
• 页码范围: 256-266
• 页数: 11页
• 语种: 英文

摘要

The complex pattern of cancer evolution poses a huge challenge to precision oncology.Longitudinal sequencing of tumor samples allows us to monitor the dynamics of mutations that occurred during this clonal evolution process.Here,we present a versatile toolbox,namely CELLO (Cancer EvoLution for LOngitudinal data),accompanied with a step-by-step tutorial,to exemplify how to profile,analyze and visualize the dynamic change of somatic mutational landscape using longitudinal genomic sequencing data.Moreover,we customize the hypermutation detection module in CELLO to adapt targeted-DNA and whole-transcriptome sequencing data,and verify the extensive applicability of CELLO in published longitudinal datasets from brain,bladder and breast cancers.The entire tutorial and reusable programs in MATLAB,R and docker versions are open access at https://github.com/WangLabHKUST/CELLO.