Diaz Aaron A, Qin Han, Ramalho-Santos Miguel, Song Jun S
Institute for Human Genetics, University of California, San Francisco, CA, USA The Eli and Edythe Broad Center of Regeneration Medicine and Stem Cell Research, University of California, San Francisco, CA, USA Department of Epidemiology and Biostatistics, University of California, San Francisco, CA, USA.
The Eli and Edythe Broad Center of Regeneration Medicine and Stem Cell Research, University of California, San Francisco, CA, USA Departments of Obstetrics and Gynecology and Pathology and Center for Reproductive Sciences, University of California, San Francisco, CA, USA Diabetes Center, University of California, San Francisco, CA, USA.
Nucleic Acids Res. 2015 Feb 18;43(3):e16. doi: 10.1093/nar/gku1197. Epub 2014 Nov 26.
Genetic screens of an unprecedented scale have recently been made possible by the availability of high-complexity libraries of synthetic oligonucleotides designed to mediate either gene knockdown or gene knockout, coupled with next-generation sequencing. However, several sources of random noise and statistical biases complicate the interpretation of the resulting high-throughput data. We developed HiTSelect, a comprehensive analysis pipeline for rigorously selecting screen hits and identifying functionally relevant genes and pathways by addressing off-target effects, controlling for variance in both gene silencing efficiency and sequencing depth of coverage and integrating relevant metadata. We document the superior performance of HiTSelect using data from both genome-wide RNAi and CRISPR/Cas9 screens. HiTSelect is implemented as an open-source package, with a user-friendly interface for data visualization and pathway exploration. Binary executables are available at http://sourceforge.net/projects/hitselect/, and the source code is available at https://github.com/diazlab/HiTSelect.
合成寡核苷酸高复杂度文库的出现，加上新一代测序技术，使得前所未有的大规模基因筛选成为可能。这些文库旨在介导基因敲低或基因敲除。然而，随机噪声和统计偏差的几个来源使所得高通量数据的解释变得复杂。我们开发了HiTSelect，这是一个综合分析流程，通过解决脱靶效应、控制基因沉默效率和测序覆盖深度的方差以及整合相关元数据，来严格选择筛选命中结果并识别功能相关的基因和通路。我们使用来自全基因组RNAi和CRISPR/Cas9筛选的数据证明了HiTSelect的卓越性能。HiTSelect作为一个开源软件包实现，具有用于数据可视化和通路探索的用户友好界面。二进制可执行文件可在http://sourceforge.net/projects/hitselect/获取，源代码可在https://github.com/diazlab/HiTSelect获取。
