A Next Generation Connectivity Map: L1000 platform and the first 1,000,000 profiles

By Aravind Subramanian, Rajiv Narayan, S. M. Corsello, David Peck, Ted E. Natoli, Xiaodong Lu, Joshua Gould, John F. Davis, Andrew A. Tubelli, Jacob K. Asiedu and 46 more. bioRxiv, 2017

2,985 citations recorded by the source corpus.

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. HIGHLIGHTS A new gene expression profiling method, L1000, dramatically lowers cost The Connectivity Map database now includes 1.3 million publicly accessible L1000 perturbational profiles This expanded Connectivity Map facilitates discovery of small molecule mechanism of action and functional annotation of genetic variants The work establishes feasibility and utility of a truly comprehensive Connectivity Map

Subfields: Computational Theory and Mathematics, Molecular Biology

View Paper: https://www.semanticscholar.org/paper/eea58cab99ab134a173d2fda1b7b7e6eef306683

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Citations in the corpus

Network medicine framework for identifying drug-repurposing opportunities for COVID-19 (2020, neutral): “j∈N i r α r W (k) r h (k) j + α i rh (k) i ) , (10)”
HERB: a high-throughput experiment- and reference-guided database of traditional Chinese medicine (2020, methodology): “Using the connectivity map method (3), the similarities between the input DEG list and the gene expression profiles for all these 2837 perturbagens were evaluated and ranked by their connectivity map scores, τ , ranked from −100 to 100.”
Assessment of network module identification across complex diseases (2019, neutral): “All samples were then quantile normalized together as a cohort 17 .”
Deep learning in drug discovery: an integrative review and future challenges (2022, methodology): “The revised version of CMAP, the LINCS-L1000 database, has been integrated into the DL DTI models in recent works (Subramanian et al. 2017; Thafar et al. 2020; Karpov et al. 2020; Arus-Pous et al. 2020).”
ToxicBlend: Virtual Screening of Toxic Compounds with Ensemble Predictors (2018, methodology): “To foster further development of machine learning techniques, multiple public initiatives [5] have been launched to compile datasets with wide coverage of compounds and their properties.”