Specific Research Interests: Systems Biology, Bioinformatics, Computational Biology, Data-Mining, Software Engineering, Network Analysis
Current Graduate Student: Huilei Xu, B.S.
Current Postdoctoral Fellows: Ben D. MacArthur, Ph.D., Amin R. Mazloom, Ph.D.
Research Personnel: Alexander Lachmann, M.Sc. (Systems Programmer Analyst)
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). Specifically, we are currently developing:
1) GATE (Grid Analysis of Time-series Expression) is a computational software platform for integrated visualization and analysis of expression time-series. Given a high-dimensional time-series dataset, GATE employs a clustering algorithm which creates movies of expression dynamics by assigning individual genes/proteins to hexagons on a hexagonal array and dynamically coloring each hexagon according to the expression level of the molecular species to which it is associated. Additionally, in order to infer potential regulatory control mechanisms from patterns of time-series correlations, GATE allows interactive interrogation of the movies with a wide variety of background knowledge datasets.
2) Lists2Networks is a web-based system that allows users to upload and analyze lists of mammalian gene-sets in a client-server software application. Within their workspace users can examine the overlap among the lists they upload, manipulate lists with different set operations, expand lists using existing mammalian networks of protein-protein, co-expression correlations, or background knowledge annotation correlations, and apply simple gene-set enrichment analyses on many gene lists at once against a plethora of prior knowledge datasets.
In the recent past, we have developed:
3) 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 in the journal Bioinformatics. PMID: 19176546
4) Genes2Networks is a software system that integrates the content of ten mammalian interaction network datasets. Filtering techniques to prune low-confidence interactions were implemented. Genes2Networks is delivered as a web-based service using AJAX. The system can be used to extract relevant subnetworks created from "seed" lists of human Entrez gene symbols. The output includes a dynamic linkable three color web-based network map, with a statistical analysis report that identifies significant intermediate nodes used to connect the seed list. An article describing the system has been published in the journal BMC Bioinformatics. PMID: 17916244
5) SNAVI is Windows-based desktop application that implements standard network analysis methods to compute the clustering, connectivity distribution, and detection of network motifs, as well as provides means to visualize networks and network motifs. SNAVI is capable of generating linked web pages from network datasets loaded in text format. SNAVI can also create networks from lists of gene or protein names. SNAVI is a useful tool for analyzing, visualizing and sharing cell signaling data. SNAVI is open source free software. An article describing the application has been published in the journal BMC Systems Biology. PMID: 19154595
We apply these and other computational methods for the analysis of a variety of projects including: high-dimensional time-series data collected from differentiating mES cells and differentiating neuro2A cells, multi-layered experiment al data collected from kidneys of Tg26 mice, a mouse model of HIV associated nephopathy (HIVAN), as well as proteomics and phosphoproteomics experiments applied to profile components downstream of stimulated G-protein coupled receptors. These 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 user friendly powerful software tools and databases for the systems biology research community.
For more information, please visit the Ma'ayan Laboratory website.
