In this new release of Enrichr we updated our ChIP-x Enrichment Analysis (ChEA) database with gene sets extracted from forty new studies. The previous version is now in the 'Legacy' category for provenance.
We also added a new gene set library we created from the database of Genotypes and Phenotypes (dbGaP), as well as two new libraries with the up- and down-regulated genes from the L1000 Connectivity Map chemical perturbation profiles from the Broad Institute LINCS Center for Transcriptomics. The previous version of the Connectivity Map Affymetrix data was renamed to Old CMAP.
In this release we improved the 'Find a Gene' feature, making it more clear and descriptive.
If you haven’t noticed, Enrichr now has a calendar view of submission statistics - you can access it by clicking on the link of lists analyzed.
The documentation of the Enrichr API was also updated. We recently used the Enrichr API to develop a new Mobile App called the Harmonizome. This mobile app is available at Google Play and the App Store. With this app you can explore aggregated knowledge about mammalian genes. The knowledge provided within this app is a subset of the Harmonizome project which can be accessed at: http://amp.pharm.mssm.edu/Harmonizome.
In this release we added a new category to Enrichr called "Crowd". In this category we will have gene set libraries that are created through our crowdsourcing efforts. The Crowd category currently contains six gene-set libraries for up/down genes in disease vs. normal tissue, before and after drug perturbation of mammalian cells, and before and after single gene manipulation in mammalian cells.
This release also has a major upgrade to our own kinase enrichment analysis (KEA) library with many more kinase-substrate interactions. We also created a gene set library from NIH Reporter by associating grants with genes through grant related publications and GeneRIF.
This release of Enrichr also contains several bug fixes, improved table sorting, and new canvases and networks for all libraries.
A new related addition to Enrichr is GEO2Enrichr. GEO2Enrichr is a browser extension plug-in and an independent web based application that enables users of Enrichr to process expression data tables from GEO, or from their own unpublished studies. The application is implemented as a Chrome extension or a FireFox Add-on. With GEO2enrichr you can quickly extract differentially expressed genes from published datasets on GEO, or from you own data, and analyze these lists with Enrichr. A YouTube video from a recent works-in-progress presentation about GEO2Enrichr is available.
All the gene set libraries of Enrichr are now available for download. These datasets can be used for global and local analyses, and for building new tools. Please acknowledge our Enrichr publication if you use one of the original gene-set library files we created.
Several new gene set libraries were added to Enrichr in the past few months: Pathway gene-set libraries created from HumanCyc, NCI-Nature PID, and Panther; Gene set libraries created from the human phenotype ontology and Uberon cross species phenotype ontology; A gene set library extracted from our ESCAPE database; and a gene set library that group genes based on their evolutionary age created from Homologene.
This release of Enrichr includes a complete redesign of the gene set library database. This updates makes Enrichr load and display results faster.
This release also contains several new and updated gene set libraries bringing the total number of libraries to 69 and gene sets to 56498. The new and updated libraries are listed below:
The ENCODE transcription factors and histone modifications libraries were updated using the datasets listed at: https://www.encodeproject.org
The Pathways category now has a phosphosite enrichment analysis functionality using data processed from DEPOD: http://www.koehn.embl.de/depod
The Diseases/Drugs category has data from the Achilles project associating individual gene knockdowns with response of cancer cell lines to those responses: http://www.broadinstitute.org/achilles
The Cell Types category now has processed gene lists from the Allen Brain Atlas (http://www.brain-map.org) were each gene set describes highly and lowly expressed genes in hundreds of different brain regions.
We also added a Legacy category to list old gene set libraries so users can reproduce enrichment results they obtain before these libraries were updated.
The Human Phenotype Ontology is an ontology of phenotypic abnormalities encountered in human disease. Terms in the HPO describes a phenotypic abnormality, such as atrial septal defect. We have taken a cross section of the ontology at the level resulting in appropriate sized gene sets.
We have updated the three Gene Ontology Consortium gene set libraries. These libraries are created using the core ontology from the Gene Ontology Consortium, annotated with associated Homo Sapiens genes. We take a cross-section of the ontology tree at the level resulting in appropriate sized gene sets. The three gene set libraries in Enrichr are called: GO Biological Process, GO Cellular Component and GO Molecular Function
We have added three additional gene set libraries.
- Virus Perturbations from GEO: This gene set library contains the most significantly up or down regulated genes for 323 different virus perturbations. This data is derived from experiments in GEO that measured gene expression levels before and after virus perturbation.
- Tissue Protein Expression from ProteomicsDB: This gene set library was derived from ProteomicsDB - a high-throughput study profiling protein expression in ~200 tissues. It consists of tissues and their respective significantly up and down regulated genes. The genes are the result of mapping the protein profiles in the study to gene sets.
- Tissue Protein Expression from Human Proteome Map: This gene set library was derived from the Human Proteome Map - a high-throughput study profiling protein expression in ~30 tissues. As above, the data maps gene sets to tissues.
We have added seven new gene set libraries to Enrichr and updated two. Read on for further details of each library.
- ENCODE Histone Modifications: This gene set library maps each histone modification term to its related genes. Here related means the genes near histone modification peaks. The data is taken from the NIH project ENCODE.
- Transcription Factor PPIs: Using our literature based protein-protein interaction network, each transcription factor protein is a term in this library and the proteins that interact with it are the set elements.
- Kinase Perturbations from GEO: This library was compiled by manually searching GEO for experiments that measured gene expression level before and after a kinase perturbation. Each kinase perturbation is a term and its elements are the genes that are differentially expressed.
- Kinase Perturbations from L1000: This is the same as above except the data is created from the L1000 dataset created at the Broad Institute, by the Connectivity Map group.
- MGI Mammalian Phenotype Level 3 and MGI Mammalian Phenotype Level 4: These gene set libraries take the MGI Mammalian Phenotype ontology and cut it at a depth of 3 or 4. Each phenotype term is mapped to the associated knockout genes in the ontology. The level 4 version already existed in Enrichr, here we added level 3.
- Disease Signatures from GEO: Similarly to Kinase Perturbations from GEO this gene set library was compiled from experiments from GEO where gene expression levels were measured in normal tissue vs diseased tissue. Each disease is a term in this library and the differentially expressed genes are its elements. This library was contributed by the Dudley lab.
- Drug Perturbations from GEO: This library has been updated with recent experiments from GEO. This library was compiled using experiments from GEO where gene expression levels were measured before and after the administration of an FDA approved drug. Each drug is a term and the the associated differentially expressed genes are its elements.
- ChEA: This library has been updated with recent papers from Pubmed. This library consists of transcription factors featured in papers in pubmed. Each term is a transcription factor and the pubmed id of the paper the data was taken from and the set elements are the genes each paper links to the transcription factor.
Enrichr can now accept BED files as input for enrichment. Enrichr automatically converts the BED file into a gene list. The species supported are human and mouse. The maximum number of genes to produce from the bed file can be adjusted. If the gene list produced by the conversion has more genes than the maximum, Enrichr will take the best matching 500, 1000 or 2000 genes. The file must have the extension ".bed" and must be uploaded, not copy and pasted.
You can now view your input gene list from the results page and view past saved gene lists from your account page.