作者
Aravind Subramanian,Rajiv Narayan,Steven M. Corsello,Donald J. Peck,Ted Natoli,Xiaodong Lü,Joshua Gould,John F. Davis,Andrew A. Tubelli,Jacob K. Asiedu,David L. Lahr,Jodi Hirschman,Zihan Liu,Maria A. Donahue,Bina Julian,Mariya Khan,David Wadden,Ian C. P. Smith,Daniel D. Lam,Arthur Liberzon,Courtney Toder,Mukta Bagul,Marek Orzechowski,Oana Enache,Federica Piccioni,Sarah A. Johnson,Nicholas Lyons,Alice H. Berger,Alykhan F. Shamji,Angela N. Brooks,Anita Vrcic,Corey Flynn,Jacqueline Rosains,David Y. Takeda,Roger Hu,Desiree Davison,Justin Lamb,Kristin Ardlie,Larson Hogstrom,Peyton Greenside,Nathanael S. Gray,Paul A. Clemons,Serena J. Silver,Xiaoyun Wu,Wen‐Ning Zhao,Willis Read-Button,Xiaohua Wu,Stephen J. Haggarty,Lucienne Ronco,Jesse S. Boehm,Stuart L. Schreiber,John G. Doench,Joshua A. Bittker,David E. Root,Bang Wong,Todd R. Golub
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.