Tang Zuojian, Steranka Jared P, Ma Sisi, Grivainis Mark, Rodić Nemanja, Huang Cheng Ran Lisa, Shih Ie-Ming, Wang Tian-Li, Boeke Jef D, Fenyö David, Burns Kathleen H
Center for Health Informatics and Bioinformatics, NYU Langone Medical Center, New York, NY 10016.
Institute for Systems Genetics, NYU Langone Medical Center, New York, NY 10016.
Proc Natl Acad Sci U S A. 2017 Jan 31;114(5):E733-E740. doi: 10.1073/pnas.1619797114. Epub 2017 Jan 17.
Mammalian genomes are replete with interspersed repeats reflecting the activity of transposable elements. These mobile DNAs are self-propagating, and their continued transposition is a source of both heritable structural variation as well as somatic mutation in human genomes. Tailored approaches to map these sequences are useful to identify insertion alleles. Here, we describe in detail a strategy to amplify and sequence long interspersed element-1 (LINE-1, L1) retrotransposon insertions selectively in the human genome, transposon insertion profiling by next-generation sequencing (TIPseq). We also report the development of a machine-learning-based computational pipeline, TIPseqHunter, to identify insertion sites with high precision and reliability. We demonstrate the utility of this approach to detect somatic retrotransposition events in high-grade ovarian serous carcinoma.
哺乳动物基因组中充满了散在重复序列，这些序列反映了转座元件的活性。这些可移动的DNA能够自我增殖，其持续转座是人类基因组中可遗传结构变异和体细胞突变的一个来源。绘制这些序列图谱的定制方法对于识别插入等位基因很有用。在这里，我们详细描述了一种在人类基因组中选择性扩增和测序长散在核元件1（LINE-1，L1）逆转录转座子插入的策略，即通过下一代测序进行转座子插入谱分析（TIPseq）。我们还报告了一种基于机器学习的计算流程TIPseqHunter的开发，该流程能够高精度、可靠地识别插入位点。我们展示了这种方法在检测高级别浆液性卵巢癌体细胞逆转座事件中的实用性。
