Gene Set Knowledge Discovery with Enrichr
By Zhuorui Xie, Allison Bailey, Maxim V. Kuleshov, Daniel J. B. Clarke, J. E. Evangelista, Sherry L. Jenkins, Alexander Lachmann, Megan L. Wojciechowicz, Eryk Kropiwnicki, Kathleen M. Jagodnik and 2 more. Current Protocols, 2021
2,914 citations recorded by the source corpus.
Profiling samples from patients, tissues, and cells with genomics, transcriptomics, epigenomics, proteomics, and metabolomics ultimately produces lists of genes and proteins that need to be further analyzed and integrated in the context of known biology. Enrichr (Chen et al., 2013; Kuleshov et al., 2016) is a gene set search engine that enables the querying of hundreds of thousands of annotated gene sets. Enrichr uniquely integrates knowledge from many high‐profile projects to provide synthesized information about mammalian genes and gene sets. The platform provides various methods to compute gene set enrichment, and the results are visualized in several interactive ways. This protocol provides a summary of the key features of Enrichr, which include using Enrichr programmatically and embedding an Enrichr button on any website. © 2021 Wiley Periodicals LLC.
Subfields: Molecular Biology
View Paper: https://www.semanticscholar.org/paper/6e4b4835a938f0559a75f4a3279b171cf8bd4522
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Citations in the corpus
- Mapping information-rich genotype-phenotype landscapes with genome-scale Perturb-seq (2021, positive): “Cluster identities were thenmanually annotated using a combination of these automated annotations, manual gene searches, and gene set enrichment analyses conducted using Enrichr (Xie et al., 2021).”
- FerrDb V2: update of the manually curated database of ferroptosis regulators and ferroptosis-disease associations (2022, methodology): “The ‘Enrichment analysis’ utility can be used to test if a given list of genes is statistically enriched in any of the ferroptosis regulator set based on either over-representation or GSEA analysis (Figure 4A) (44,45).”