生物
推论
基因
仿形(计算机编程)
基因表达谱
遗传学
基因表达
计算生物学
RNA序列
生物信息学
转录组
计算机科学
人工智能
操作系统
作者
Aravind Subramanian,Rajiv Narayan,Steven M. Corsello,D. D. Peck,Ted Natoli,Xiaodong Lü,Joshua Gould,John F. Davis,Andrew A. Tubelli,Jacob K. Asiedu,David L. Lahr,Jodi Hirschman,Zihan Liu,Melanie Donahue,Bina Julian,Mariya Khan,David Wadden,Ian C. P. Smith,Daniel D. Lam,Arthur Liberzon
出处
期刊:Cell
[Cell Press]
日期:2017-11-01
卷期号:171 (6): 1437-1452.e17
被引量:3683
标识
DOI:10.1016/j.cell.2017.10.049
摘要
Summary
We previously piloted the concept of a Connectivity Map (CMap), whereby genes, drugs, and disease states are connected by virtue of common gene-expression signatures. Here, we report more than a 1,000-fold scale-up of the CMap as part of the NIH LINCS Consortium, made possible by a new, low-cost, high-throughput reduced representation expression profiling method that we term L1000. We show that L1000 is highly reproducible, comparable to RNA sequencing, and suitable for computational inference of the expression levels of 81% of non-measured transcripts. We further show that the expanded CMap can be used to discover mechanism of action of small molecules, functionally annotate genetic variants of disease genes, and inform clinical trials. The 1.3 million L1000 profiles described here, as well as tools for their analysis, are available at https://clue.io.
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