在大规模RNA干扰筛选中最小化报告假阳性的风险。
Minimizing the risk of reporting false positives in large-scale RNAi screens.
作者信息
Echeverri Christophe J, Beachy Philip A, Baum Buzz, Boutros Michael, Buchholz Frank, Chanda Sumit K, Downward Julian, Ellenberg Jan, Fraser Andrew G, Hacohen Nir, Hahn William C, Jackson Aimee L, Kiger Amy, Linsley Peter S, Lum Lawrence, Ma Yong, Mathey-Prévôt Bernard, Root David E, Sabatini David M, Taipale Jussi, Perrimon Norbert, Bernards René
机构信息
Cenix BioScience GmbH, Tatzberg 47, Dresden, 10307, Germany.
出版信息
Nat Methods. 2006 Oct;3(10):777-9. doi: 10.1038/nmeth1006-777.
Large-scale RNA interference (RNAi)-based analyses, very much as other 'omic' approaches, have inherent rates of false positives and negatives. The variability in the standards of care applied to validate results from these studies, if left unchecked, could eventually begin to undermine the credibility of RNAi as a powerful functional approach. This Commentary is an invitation to an open discussion started among various users of RNAi to set forth accepted standards that would insure the quality and accuracy of information in the large datasets coming out of genome-scale screens.
与其他“组学”方法一样，基于大规模RNA干扰（RNAi）的分析存在固有的假阳性和假阴性率。如果不对用于验证这些研究结果的护理标准的变异性加以控制，最终可能会开始损害RNAi作为一种强大功能方法的可信度。本评论旨在邀请RNAi的各类用户展开公开讨论，以制定公认的标准，确保基因组规模筛选产生的大型数据集中信息的质量和准确性。
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