Dhingra Priyanka, Fu Yao, Gerstein Mark, Khurana Ekta
Institute for Computational Biomedicine, Weill Cornell Medical College, New York, New York.
Department of Physiology and Biophysics, Weill Cornell Medical College, New York, New York 10021.
Curr Protoc Bioinformatics. 2017 May 2;57:15.11.1-15.11.17. doi: 10.1002/cpbi.23.
The identification of non-coding drivers remains a challenge and bottleneck for the use of whole-genome sequencing in the clinic. FunSeq2 is a computational tool for annotation and prioritization of somatic mutations in coding and non-coding regions. It integrates a data context made from large-scale genomic datasets and uses a high-throughput variant prioritization pipeline. This unit provides guidelines for installing and running FunSeq2 to (a) annotate and prioritize variants, (b) incorporate user-defined annotations, and (c) detect differential gene expression. © 2017 by John Wiley & Sons, Inc.
对于在临床中使用全基因组测序而言,鉴定非编码驱动因素仍然是一项挑战和瓶颈。FunSeq2是一种用于对编码和非编码区域的体细胞突变进行注释和优先级排序的计算工具。它整合了由大规模基因组数据集构成的数据背景,并使用高通量变异优先级排序流程。本单元提供了安装和运行FunSeq2的指南,以(a)对变异进行注释和优先级排序,(b)纳入用户定义的注释,以及(c)检测差异基因表达。© 2017约翰威立国际出版公司。