|Publication Type||Journal Article|
|Year of Publication||2017|
|Authors||Subramanian, A, Narayan, R, Corsello, SM, Peck, DD, Natoli, TE, Lu, X, Gould, J, Davis, JF, Tubelli, AA, Asiedu, JK, Lahr, DL, Hirschman, JE, Liu, Z, Donahue, M, Julian, B, Khan, M, Wadden, D, Smith, IC, Lam, D, Liberzon, A, Toder, C, Bagul, M, Orzechowski, M, Enache, OM, Piccioni, F, Johnson, SA, Lyons, NJ, Berger, AH, Shamji, AF, Brooks, AN, Vrcic, A, Flynn, C, Rosains, J, Takeda, DY, Hu, R, Davison, D, Lamb, J, Ardlie, K, Hogstrom, L, Greenside, P, Gray, NS, Clemons, PA, Silver, S, Wu, X, Zhao, W-N, Read-Button, W, Wu, X, Haggarty, SJ, Ronco, LV, Boehm, JS, Schreiber, SL, Doench, JG, Bittker, JA, Root, DE, Wong, B, Golub, TR|
|Date Published||2017 Nov 30|
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.