L1000FWD: fireworks visualization of drug‐induced transcriptomic signatures

Motivation: As part of the NIH Library of Integrated Network‐based Cellular Signatures program, hundreds of thousands of transcriptomic signatures were generated with the L1000 technology, profiling the response of human cell lines to over 20 000 small molecule compounds. This effort is a promising approach toward revealing the mechanisms‐of‐action (MOA) for marketed drugs and other less studied potential therapeutic compounds. Results: L1000 fireworks display (L1000FWD) is a web application that provides interactive visualization of over 16 000 drug and small‐molecule induced gene expression signatures. L1000FWD enables coloring of signatures by different attributes such as cell type, time point, concentration, as well as drug attributes such as MOA and clinical phase. Signature similarity search is implemented to enable the search for mimicking or opposing signatures given as input of up and down gene sets. Each point on the L1000FWD interactive map is linked to a signature landing page, which provides multifaceted knowledge from various sources about the signature and the drug. Notably such information includes most frequent diagnoses, co‐prescribed drugs and age distribution of prescriptions as extracted from the Mount Sinai Health System electronic medical records. Overall, L1000FWD serves as a platform for identifying functions for novel small molecules using unsupervised clustering, as well as for exploring drug MOA. Availability and implementation: L1000FWD is freely accessible at: http://amp.pharm.mssm.edu/L1000FWD. Supplementary information: Supplementary data are available at Bioinformatics online.

[1]  R. Tagliaferri,et al.  Discovery of drug mode of action and drug repositioning from transcriptional responses , 2010, Proceedings of the National Academy of Sciences.

[2]  Geoffrey E. Hinton,et al.  Visualizing Data using t-SNE , 2008 .

[3]  Avi Ma'ayan,et al.  The characteristic direction: a geometrical approach to identify differentially expressed genes , 2014, BMC Bioinformatics.

[4]  Julio Saez-Rodriguez,et al.  DvD: An R/Cytoscape pipeline for drug repurposing using public repositories of gene expression data , 2012, Bioinform..

[5]  Angela N. Brooks,et al.  A Next Generation Connectivity Map: L1000 Platform And The First 1,000,000 Profiles , 2017 .

[6]  Paul A Clemons,et al.  The Connectivity Map: Using Gene-Expression Signatures to Connect Small Molecules, Genes, and Disease , 2006, Science.

[7]  Avi Ma'ayan,et al.  Enrichr: interactive and collaborative HTML5 gene list enrichment analysis tool , 2013, BMC Bioinformatics.

[8]  Jeffrey Heer,et al.  SpanningAspectRatioBank Easing FunctionS ArrayIn ColorIn Date Interpolator MatrixInterpola NumObjecPointI Rectang ISchedu Parallel Pause Scheduler Sequen Transition Transitioner Transiti Tween Co DelimGraphMLCon IData JSONCon DataField DataSc Dat DataSource Data DataUtil DirtySprite LineS RectSprite , 2011 .

[9]  Pankaj Agarwal,et al.  Systematic evaluation of connectivity map for disease indications , 2014, Genome Medicine.

[10]  Peer Bork,et al.  Drug-Induced Regulation of Target Expression , 2010, PLoS Comput. Biol..

[11]  Avi Ma'ayan,et al.  Drug-induced adverse events prediction with the LINCS L1000 data , 2016, Bioinform..

[12]  Marc Hafner,et al.  Common and cell-type specific responses to anti-cancer drugs revealed by high throughput transcript profiling , 2017, Nature Communications.

[13]  Sean R. Davis,et al.  NCBI GEO: archive for functional genomics data sets—update , 2012, Nucleic Acids Res..

[14]  Marc Hafner,et al.  L1000CDS2: LINCS L1000 characteristic direction signatures search engine , 2016, npj Systems Biology and Applications.

[15]  Kathleen M Jagodnik,et al.  Extraction and analysis of signatures from the Gene Expression Omnibus by the crowd , 2016, Nature Communications.

[16]  Jacob K. Asiedu,et al.  The Drug Repurposing Hub: a next-generation drug library and information resource , 2017, Nature Medicine.

[17]  Gaël Varoquaux,et al.  Scikit-learn: Machine Learning in Python , 2011, J. Mach. Learn. Res..

[18]  Hans-Peter Kriegel,et al.  A Density-Based Algorithm for Discovering Clusters in Large Spatial Databases with Noise , 1996, KDD.

[19]  Diego di Bernardo,et al.  Mantra 2.0: an online collaborative resource for drug mode of action and repurposing by network analysis , 2014, Bioinform..

[20]  P. Shannon,et al.  Cytoscape: a software environment for integrated models of biomolecular interaction networks. , 2003, Genome research.

[21]  Pablo Tamayo,et al.  Gene set enrichment analysis: A knowledge-based approach for interpreting genome-wide expression profiles , 2005, Proceedings of the National Academy of Sciences of the United States of America.