Enrichr: a comprehensive gene set enrichment analysis web server 2016 update

By Maxim V. Kuleshov, Matthew R. Jones, Andrew D. Rouillard, Nicolas F. Fernandez, Qiaonan Duan, Zichen Wang, Simon Koplev, Sherry L. Jenkins, Kathleen M. Jagodnik, Alexander Lachmann and 4 more. Nucleic Acids Res., 2016

9,421 citations recorded by the source corpus.

Enrichment analysis is a popular method for analyzing gene sets generated by genome-wide experiments. Here we present a significant update to one of the tools in this domain called Enrichr. Enrichr currently contains a large collection of diverse gene set libraries available for analysis and download. In total, Enrichr currently contains 180 184 annotated gene sets from 102 gene set libraries. New features have been added to Enrichr including the ability to submit fuzzy sets, upload BED files, improved application programming interface and visualization of the results as clustergrams. Overall, Enrichr is a comprehensive resource for curated gene sets and a search engine that accumulates biological knowledge for further biological discoveries. Enrichr is freely available at: http://amp.pharm.mssm.edu/Enrichr.

Subfields: Molecular Biology

View Paper: https://www.semanticscholar.org/paper/c051f2ec35716226d3176b8689b329f671407049

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In Molecular Biology

1. Rahul Satija
2. Jianguo Xia
3. Aviv Regev
4. Zhiao Shi
5. Martina Kutmon
6. Hongjun Song
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Citations in the corpus

• SCENIC: Single-cell regulatory network inference and clustering (2017, positive): “The analyses were performed mainly through Mouse Mine 98 (although we also checked DAVID 99,100 , Enrichr 101,102 , which provided similar results).”
• Morphometric Similarity Networks Detect Microscale Cortical Organisation And Predict Inter-Individual Cognitive Variation (2017, positive): “In a complementary analysis of phenotype enrichment in mammalian gene knockout models (Smith and Eppig, 2012) using Enrichr (Chen et al., 2013; Kuleshov et al., 2016), we found that disruption in animal models of high-ranking genes (i.e., those with positive leave-one-out scores) was significantly a”
• Integrating single-cell transcriptomic data across different conditions, technologies, and species (2018, methodology): “We used the top 100 differentially expressed markers, ranked by integrated p-value, as input for gene ontology enrichment as performed using EnrichR 69 .”
• The Library of Integrated Network-based Cellular Signatures (LINCS) NIH Program: System-level Cataloging of Human Cells Response to Perturbations (2017, background): “The CMAP team catalogs the connections between these gene expression signatures to find drugs, diseases, pathways, and targets (Lamb et al., 2006).”
• A Patient-Derived Glioblastoma Organoid Model and Biobank Recapitulates Inter- and Intra-tumoral Heterogeneity. (2019, methodology): “A uthor M anuscript A uthor M anuscript A uthor M anuscript A uthor M anuscript REAGENT or RESOURCE SOURCE IDENTIFIER DESeq2 v.1.22.2 Love et al., 2014 https://bioconductor.org/packages/ release/bioc/html/DESeq2.html; RRID:SCR_015687 Drop-seq tools v.2.1.0 Saunders et al., 2018 https://github.com/br”