Ontology highlight
ABSTRACT:
SUBMITTER: Subramanian A
PROVIDER: S-EPMC5990023 | biostudies-literature | 2017 Nov
REPOSITORIES: biostudies-literature
Subramanian Aravind A Narayan Rajiv R Corsello Steven M SM Peck David D DD Natoli Ted E TE Lu Xiaodong X Gould Joshua J Davis John F JF Tubelli Andrew A AA Asiedu Jacob K JK Lahr David L DL Hirschman Jodi E JE Liu Zihan Z Donahue Melanie M Julian Bina B Khan Mariya M Wadden David D Smith Ian C IC Lam Daniel D Liberzon Arthur A Toder Courtney C Bagul Mukta M Orzechowski Marek M Enache Oana M OM Piccioni Federica F Johnson Sarah A SA Lyons Nicholas J NJ Berger Alice H AH Shamji Alykhan F AF Brooks Angela N AN Vrcic Anita A Flynn Corey C Rosains Jacqueline J Takeda David Y DY Hu Roger R Davison Desiree D Lamb Justin J Ardlie Kristin K Hogstrom Larson L Greenside Peyton P Gray Nathanael S NS Clemons Paul A PA Silver Serena S Wu Xiaoyun X Zhao Wen-Ning WN Read-Button Willis W Wu Xiaohua X Haggarty Stephen J SJ Ronco Lucienne V LV Boehm Jesse S JS Schreiber Stuart L SL Doench John G JG Bittker Joshua A JA Root David E DE Wong Bang B Golub Todd R TR
Cell 20171101 6
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 o ...[more]