Dr. Ma'ayan is the Director of the Mount Sinai Center for Bioinformatics and Mount Sinai Professor of Bioinformatics in the Department of Pharmacological Sciences. Dr. Ma'ayan is also Principal Investigator of the NIH-funded BD2K-LINCS Data Coordination and Integration Center and Mount Sinai Knowledge Management Center for Illuminating the Druggable Genome as well as a frequent collaborator with the Icahn Institute for Data Science and Genomic Technology. The Ma'ayan Laboratory applies computational and mathematical methods to study the complexity of regulatory networks in mammalian cells. His research team applies 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. The Ma'ayan Laboratory develops 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.
Recently Released Software Tools Developed by the Ma'ayan Laboratory:
- Geneshot: Search engine for ranking genes from arbitrary text queries
- ChEA3: ChIP-X enrichment analysis
- DGB: Ranks drugs to modulate genes based on transcriptomic signatures
- BioJupies: Automatically generates RNA-seq data analysis notebooks
- X2K Web: Linking expression signatures to upstream cell signling networks
- ARCHS4: All RNA-seq and CHIP-seq signature search space
- Datasets2Tools: Repository and search engine for bioinformatics resources
- L1000FWD: Large-scale visualization of drug-induced transcriptomic signatures
- Clustergrammer: Visualization and analyis tool for high-dimensional biological data
- L1000CDS2: L1000 Characteristic DIrection signature search engine
- Harmonizome: A biological knowledge engine
- Enrichr: Gene-list enrichment analysis tool
For a complete list of our software tools, databases and datasets, please visit our Resources page.
In the News:
- Ten Renowned Mount Sinai Faculty Members Honored at Convocation
- Mount Sinai Researchers Develop Software to Measure the Findability, Accessibility, Interoperability, and Reusability of Biomedical Digital Research Objects
- Finding Needles in the Haystack: Integrating 'Omics Data to Predict Toxicity
- Mount Sinai Researchers Develop Tool that Analyzes Biomedical Data within Minutes
- Mount Sinai Health System Top 10 Researchers
- Mount Sinai Researchers Receive NIH Grant to Develop New Ways to Share and Reuse Research Data
- Students Harness Big Data to Help Solve Medical Challenges
- The FAIR Data-Sharing Movement: BD2K Centers Make Headway
- Big Data Changes
- Big Data Highlight: The Harmonizome
- Crowdsourcing for Scientific Discovery
- Genetics: Big Hopes for Big Data
- NIH Launches a United Ecosystem for Big Data
Addiction, Aging, Bioinformatics, Biomedical Sciences, Biostatistics, Cancer, Computational Biology, Drug Design and Discovery, Gene Expressions, Gene Regulation, Genetics, Genomics, Kidney, Mass Spectrometry, Mathematical Modeling of Biomedical Systems, Mathematical and Computational Biology, Personalized Medicine, Pharmacogenomics, Pharmacology, Protein Complexes, Protein Kinases, Proteomics, Reprogramming, Signal Transduction, Stem Cells, Systems Biology, Systems Pharmacology, Technology & Innovation, Theoretical Biology, Transcription Factors, Viruses and Virology
Multi-Disciplinary Training Areas
Genetics and Data Science [GDS], Pharmacology and Therapeutics Discovery [PTD]
BSc, Fairleigh Dickinson University
MS, Fairleigh Dickinson University
PhD, Mount Sinai School of Medicine
Irma T. Hirschl Career Scientist Award
Dr. Harold and Golden Lamport Research Award
Mount Sinai School of Medicine
Doctoral Dissertation Award in the Graduate School of Biological Sciences
Mount Sinai School of Medicine
Graduate School of Biological Sciences Award for Research Achievement
Mount Sinai School of Medicine
Systems Biology, Systems Pharmacology, Biomedical Big Data, Bioinformatics, Computational Biology, Data-Mining, Software Engineering, Network Analysis
Program Manager: Sherry Jenkins, MS
Research Faculty: Alexander Lachmann, PhD
Postdoctoral Fellows: Alon Bartal, PhD; Kathleen Jagodnik, PhD
PhD Student: Megan Wojciechowicz, MS
Medical Student: Samuel Chiacchia, AB
MD/PhD Student: Alexandra Keenan, MS
Bioinformaticians, Data Scientists and Software Engineers: Daniel Clarke, MS; John Erol Evangelista, MS; Maxim Kuleshov, MS; Shirley Xu, BA
Master's Students: Eryk Kropiwnicki, BS; Adil Sabir, BS
2019 Undergraduates and Graduate Assistants: Zaire Bryant, Elizabeth Jung, Alessandra LaRocco, Samuel Maltz, Damon Pham, Kaeli Rizzo, Allison Seiden, Ingrid Shu, David Szanto, Andrew Warburton, Justin Williams
Summary of Research Studies:
Advances in high-throughput experimental molecular biology are allowing us to elucidate the molecular mechanisms of mammalian cell regulation with ever-increasing detail. However, the potential gains from these advances are often not fully realized since high-throughput techniques often produce more data than our current ability to adequately organize, model and visualize. A particular challenge is encountered when attempting to integrate several high-dimensional datasets from multiple types of high- and low-throughput experimental techniques applied to study mammalian cells.
For the purpose of organizing, visualizing, analyzing and modeling data from such sources we develop computational approaches which can assist experimental systems-biologists to form rational hypotheses for further experimentation. We analyze high-dimensional data collected for projects integrating results from multiple layers of regulation (genomics, transcriptomics and proteomics). In addition to our research efforts, we also develop software so that our methodologies can reach and impact the Big Data biomedical research community. Below are some of the software tools we have developed:
1) Enrichr is a gene set enrichment analysis tool that includes one of the largest collections of annotated gene sets: 298,481 gene sets organized into 153 gene set libraries. Enrichr provides visualization of enrichment results as bar graphs, tables, canvases and networks. Enrichment is computed by three different methods and users can save and share their lists and results with a single click. Articles describing the initial and updated versions of the software were published in BMC Bioinformatics and Nucleic Acids Research. PMID: 23586463 and PMID: 27141961
3) L1000CDS2 and Drug Pair Seeker (DPS) are two tools that use the Connectivity Map gene expression datasets, including the new version that utilizes the L1000 technology, to predict single and pairs of drugs that can either mimic or reverse gene expression given signatures of differentially expressed genes. Both tools use novel algorithms developed by the Ma’ayan Laboratory to prioritize drugs and small molecules. A detailed description of Drug Pair Seeker and its application to kidney disease can be found in publication in the journal JSAN. PMID: 23559582. An article describing L1000CDS2 was published in NPJ Systems Biology and Applications. PMID: 28413689
4) ChIP-X Enrichment Analysis (ChEA) database contains manually extracted datasets of transcription-factor/target-gene interactions from over 100 experiments such as ChIP-chip, ChIP-seq, ChIP-PET applied to mammalian cells. We use the database to analyze mRNA expression data where we perform gene-list enrichment analysis as the prior biological knowledge gene-list library. The system is delivered as web-based interactive software. With this software users can input lists of mammalian genes for which the program computes over-representation of transcription factor targets from the ChEA database. An article describing the system has been published in the journal Bioinformatics. PMID: 20709693
5) Kinase Enrichment Analysis (KEA) is a web-based tool with an underlying database providing users with the ability to link lists of mammalian proteins/genes with the kinases that phosphorylate them. The system draws from several available kinase–substrate databases to compute kinase enrichment probability based on the distribution of kinase–substrate proportions in the background kinase–substrate database compared with kinases found to be associated with an input list of genes/proteins. An article describing the system has been published inthe journal Bioinformatics. PMID: 19176546
6) Expression2Kinases (X2K) is a software tool that integrates and upgrades the functionality of ChEA, Genes2Networks, KEA and Lists2Networks into one platform and computational pipeline. Given a list of differentially expressed genes, the software identified upstream transcription factors using the software and database ChEA; X2K then connects the top identified transcription factors with Genes2Networks using databases of known protein-protein interactions; the resultant subnetwork is then entered into KEA for kinase enrichment analysis. X2K also includes all the functions for enrichment analysis available within Lists2Networks. An article describing the system has been published in the journal Bioinformatics. PMID: 22080467 and PMID: 29800326
We apply these and other computational methods for the analysis of data from a variety of projects with our collaborators. 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.
For more information, please visit the Ma'ayan Laboratory website.
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Dr.Ma'ayan did not report having any of the following types of financial relationships with industry during 2018 and/or 2019: consulting, scientific advisory board, industry-sponsored lectures, service on Board of Directors, participation on industry-sponsored committees, equity ownership valued at greater than 5% of a publicly traded company or any value in a privately held company. Please note that this information may differ from information posted on corporate sites due to timing or classification differences.
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