Resources | Ma'ayan Laboratory, Computational Systems Biology

These are the Ma’ayan Lab’s flagship tools, widely used by the biomedical research community for tasks such as gene set enrichment analysis. They serve as entry points for many users and highlight the laboratory’s impact across diverse areas of systems biology.

Browse through the following categories to explore the various original resources we have developed:

Enrichr

Gene-List Enrichment Analysis Tool
An integrative web-based and mobile gene-list enrichment analysis tool that includes 225 gene-set libraries, an alternative approach to rank enriched terms, and various interactive visualization approaches to display enrichment results using the JavaScript library Data-Driven Documents (D3). Enrichr is freely available online. The software can also be embedded easily into any tool that performs gene list analysis.
PMID: 23586463
PMID: 27141961
PMID: 33780170

Harmonizome

Biological Knowledge Engine
A biological knowledge engine built on top of information about genes and proteins from 114 datasets. To create the Harmonizome, we distilled information from original datasets into attribute tables that define significant associations between genes and attributes, where attributes could be genes, proteins, cell lines, tissues, experimental perturbations, diseases, phenotypes, or drugs, depending on the dataset. Gene and protein identifiers were mapped to NCBI Entrez Gene Symbols and attributes were mapped to appropriate ontologies. We also computed gene-gene and attribute-attribute similarity networks from the attribute tables. These attribute tables and similarity networks can be integrated to perform many types of computational analyses for knowledge discovery and hypothesis generation.
Harmonizome mobile app
PMID: 27374120

Enrichr-KG

Knowledge Graph Implementation of Enrichr
Enrichr-KG is a knowledge graph database and a web-server application that combines selected gene set libraries from Enrichr for integrative enrichment analysis and visualization. The enrichment results are presented as subgraphs made of nodes and links that connect genes to their enriched terms. In addition, users of Enrichr-KG can add gene-gene links, as well as predicted genes to the subgraphs. This graphical representation of cross-library results with enriched and predicted genes can illuminate hidden associations between genes and annotated enriched terms from across datasets and resources.
PMID: 37166973

ARCHS4

All RNA-seq and ChIP-seq Signature Search Space
ARCHS4 provides access to gene counts from HiSeq 2000 and HiSeq 2500 platforms for human and mouse experiments from GEO and SRA. The website enables downloading of the data in H5 format for programmatic access as well as a 3-dimensional view of the sample and gene spaces. Search features allow browsing of the data by meta data annotation, ability to submit your own up and down gene sets, and explore matching samples enriched for annotated gene sets. Selected sample sets can be downloaded into a tab separated text file through auto-generated R scripts for further analysis. Reads are aligned with Kallisto using a custom cloud computing platform. Human samples are aligned against the GRCh38 human reference genome, and mouse samples against the GRCm38 mouse reference genome.
PMID: 29636450

ChEA3

ChIP-X Enrichment Analysis Version 3
A transcription factor enrichment analysis tool that ranks TFs associated with user-submitted gene sets. The ChEA3 background database contains a collection of gene set libraries generated from multiple sources including TF–gene co-expression from RNA-seq studies, TF–target associations from ChIP-seq experiments, and TF–gene co-occurrence computed from crowd-submitted gene lists.
PMID: 31114921

KEA3

Kinase Enrichment Analysis Version 3
Infers upstream kinases whose putative substrates are overrepresented in a user-inputted list of genes or differentially phosphorylated proteins. The KEA3 database contains putative kinase-substrate interactions collected from publicly available datasets. Gene sets of putative kinase substrates are used as the primary units of analysis in KEA3. These gene sets are organized in gene set “libraries.” Libraries are supersets of kinase substrate sets that are aggregated based on the database from which they are derived.
PMID: 34019655