Yu Jiyang, Putcha Preeti, Califano Andrea, Silva Jose M
Department of Biomedical Informatics, Columbia University, New York, NY, USA.
Methods Mol Biol. 2013;980:371-84. doi: 10.1007/978-1-62703-287-2_22.
Genome-wide RNA interference screening has emerged as a powerful tool for functional genomic studies of disease-related phenotypes and the discovery of molecular therapeutic targets for human diseases. Commercial short hairpin RNA (shRNA) libraries are commonly used in this area, and state-of-the-art technologies including microarray and next-generation sequencing have emerged as powerful methods to analyze shRNA-triggered phenotypes. However, computational analysis of this complex data remains challenging due to noise and small sample size from such large-scaled experiments. In this chapter we discuss the pipelines and statistical methods of processing, quality assessment, and post-analysis for both microarray- and sequencing-based screening data.
全基因组RNA干扰筛选已成为一种强大的工具,用于与疾病相关表型的功能基因组学研究以及人类疾病分子治疗靶点的发现。商业短发夹RNA(shRNA)文库在该领域中常用,包括微阵列和下一代测序在内的先进技术已成为分析shRNA触发表型的强大方法。然而,由于此类大规模实验产生的噪声和小样本量,对这些复杂数据的计算分析仍然具有挑战性。在本章中,我们讨论了基于微阵列和测序的筛选数据的处理、质量评估和分析后处理的流程及统计方法。