The Ma’ayan Laboratory develops computational and mathematical methods to study the complexity of regulatory networks in mammalian cells. We apply machine learning and other statistical mining techniques to study how intracellular regulatory systems function as networks to control cellular processes such as differentiation, dedifferentiation, apoptosis and proliferation. We develop software systems to help experimental biologists form novel hypotheses from high-throughput data, while aiming to better understand the structure and function of regulatory networks in mammalian cellular and multi-cellular systems.
Below are some of the resources we designed and developed recently:
Submit Biomedical Terms to Receive Ranked Lists of Relevant Genes
Enables researchers to enter arbitrary search terms, to receive ranked lists of genes relevant to the search terms. Returned ranked gene lists contain genes that were previously published in association with the search terms, as well as genes predicted to be associated with the terms based on data integration from multiple sources. The search results are presented with interactive visualizations.
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.
A Suite of Gene Set Enrichment Analysis Tools for Model Organisms
An expansion of Enrichr for four model organisms: fish, fly, worm and yeast. The modEnrichr suite of tools provides the ability to convert gene lists across species using an ortholog conversion tool that automatically detects the species.
Automated Generation of Interactive Notebooks for RNA-seq Data Analysis in the Cloud
Web server that enables automated creation, storage, and deployment of Jupyter Notebooks containing RNA-seq data analyses.
Drug Gene Budger
Identify Drugs and Small Molecules to Regulate Expression of Target Genes
Web-based and mobile application to prioritize small molecules that are predicted to maximally influence the expression of the target gene of interest.
Linking Expression Signatures to Upstream Cell Signaling Networks
Computationally predicts involvement of upstream cell signaling pathways, given a signature of differentially expressed genes.
Toolkit to Evaluate the FAIRness of Research Digital Resources
The FAIRshake toolkit was developed to enable the establishment of community-driven FAIR metrics and rubrics paired with manual and automated FAIR assessments. FAIR assessments are visualized as an insignia that can be embedded within digital-resources-hosting websites.
Repository and Search Engine for Bioinformatics Datasets, Tools and Canned Analyses
Repository indexing 31,473 canned bioinformatics analyses applied to 6,431 datasets.
Large-scale Visualization of Drug-induced Transcriptomic Signatures
L1000 fireworks display (L1000FWD) is a web application that provides interactive visualization of over 16,000 drug and small-molecule induced gene expression signatures.
Web-based Heatmap Visualization and Analysis Tool for High-Dimensional Biological Data
Clustergrammer is a visualization library built using D3.js that enables intuitive interaction with high-dimensional data.
L1000 Characteristic Direction Signature Search Engine
Queries gene expression signatures against the LINCS L1000 to identify and prioritize small molecules that can reverse or mimic the observed input expression pattern.
Gene-List Enrichment Analysis Tool
An integrative web-based and mobile gene list enrichment analysis tool providing various types of visualization summaries of collective functions of gene lists.
For a complete list of our software tools, databases and datasets please visit our Resources page. We apply these and other computational methods for the analysis of a variety of collaborative projects. The results from our analyses produce concrete suggestions and predictions for further functional experiments. The predictions are tested by our collaborators and our analyses methods are delivered as software tools and databases for the systems biology research community.