Software Tools and Resources

Gene Expression Data Analysis

Expression2Kinases is used to identify upstream regulators likely responsible for observed patterns in genome-wide gene expression. By integrating ChIP-seq/chip and position-weight-matrices (PWMs) data, protein-protein interactions, and kinase-substrate phosphorylation reactions, we can better identify regulatory mechanisms upstream of genome-wide differences in gene expression. The software and source code are freely available at: PMID: 22080467

GATE (Grid Analysis of Time-series Expression) is a computational software platform for integrated visualization and analysis of expression time-series data. 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. PMID: 19892805

Gene-list Enrichment Analysis

ChEA (ChIP-X Enrichment Analysis) 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. PMID: 20709693

Genes2Networks is a tool that can be used to place lists of mammalian genes in the context of background mammalian signalome and interactome networks. The input to the program is a list of human Entrez Gene gene symbols and background networks in SIG file format, while the output includes: (a) all identified interactions for the genes/proteins, (b) a subnetwork connecting the genes/proteins using intermediate components that are used to connect the genes, (c) ranking of the specificity of intermediate components to interact with the list of genes/proteins, and (d) a clustering analysis of the genes/proteins from the seed list based on their distance from one another in network space. PMID: 17916244

KEA (Kinase Enrichment Analysis) 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. PMID: 19176546

Lists2Networks is a web-based system that will allow users to upload and analyze lists of mammalian gene-sets in a client-server-based 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 numerous background datasets. PMID: 20152038

Network Analysis/Visualization

Genes2FANs is a web based tool and a database that utilizes 14 carefully constructed FANs and a large-scale protein-protein interaction (PPI) network to build subnetworks that connect input lists of human and mouse genes. The FANs are created from mammalian gene set libraries where mouse genes are converted to their human orthologs. The tool takes as input a list of human or mouse Entrez gene symbols to produce a subnetwork and a ranked list of intermediate genes that are used to connect the query input list. In addition, users can enter any PubMed search term and then the system automatically converts the returned results to gene lists using GeneRIF. This gene list is then used as input to generate a subnetwork from the user’s PubMed query. PMID: 22748121

Sets2Networks is general method for network inference from repeated observations of sets of related entities. Given experimental observations of sets of related entities, S2N infers the underlying network of binary interactions between these entities by generating an ensemble of networks consistent with the data; the frequency of occurrence of a given interaction throughout this ensemble is interpreted as the probability that the interaction is present in the underlying real network. PMID: 22824380

Genes2WordCloud is a web application that enables users to quickly identify biological themes from gene lists and research relevant text by constructing and displaying word-clouds. PMID: 21995939

FNV (Flash-based Network Viewer) is for the visualization of small to moderately-sized biological networks and pathways. FVN can also be used to embed pathways inside PDF files for the communication of pathways in soft publication materials. PMID: 21349871

SNAVI (Signaling Network Analysis and Visualization) is a Windows-based desktop application that implements standard network analysis methods to compute the clustering, connectivity distribution, and detection of network motifs, as well as provide means to visualize networks and network motifs. PMID: 19154595

PathwayGenerator  provides automatically generated pathways from receptors to effectors created using the neuronal signalome. PMID: 16099987

AVIS AJAX Viewer is a visualization tool for viewing and sharing intracellular signaling, gene regulation and protein interaction networks. AVIS is implemented as an AJAX enabled syndicated Google gadget. It allows any webpage to render an image from a text file representation of signaling, gene regulatory or protein interaction networks. PMID: 17855420


KEA (Kinase Enrichment Analysis) contains kinase-substrate interactions collected from several databases collecting literature-based kinase-substrate interactions from experimental studies of mammalian cells. [kea.sig] [kinome.sig] PMID: 19176546

iScMiD (Integrated Stem-Cell Molecular Interactions Database) is an initial database for disseminating and displaying gene regulatory networks in stem cells. It currently contains interactions from 12 recent publications of profiling stem-cell related transcription factors using various high throughput ChIP profiling methods. The resultant integrated network has 50,250 entries. The file contains four columns: transcription factor (TF), target gene, PubMed ID (pmid), and organism (mouse/human). Please be aware that this network is likely to contain many false-positives and should be used for hypothesis generation only. PMID: 19738627

Presynaptome ia a database created to visualize and share, with the neuroscience and molecular biology research communities, information about proteins and interactions identified to be present in presynaptic nerve terminals of mammalian neurons. The website features a network of protein-protein interactions manually extracted from neuroscience research literature. The interactions in this network are identified to be exclusively from presynaptic nerve terminals of mammalian neurons. (Contacts: Avi Ma'ayan and Lakshmi A. Devi) PMID: 19562802

Neuronal Signalome contains cell signaling interactions extracted from literature describing components and interactions in mammalian neurons. This dataset was used in the paper Ma'ayan et al. published in Science. [neuronal_signalome.owl] [neuronal_signalome.txt] PMID: 16099987

PathwayGenerator automatically generates pathways from receptors to effectors created using the neuronal signalome.PMID: 16099987 


Sig2BioPAX is a Java program that can be used to convert structured text files describing molecular interactions into the BioPAX Level 3 standard format. PMID: 21418653

Excel2BiositemapsAndHTML is a tool that is used to convert an Excel file containing details about biomedical resources (tools, data, software) into a Biositemaps .rdf file. A Biositemap file is a list of controlled metadata about resources. This file can be picked up and read by a number of search engines, the Biositemaps Search Tool or the Resource Discovery System, for example.

Contact Us

To learn more about the software resources:

Avi Ma'ayan, PhD
Associate Professor
SBCNY Investigator
Director of SBCNY Information Management Unit
Tel: 212-659-1739
Send e-mail

One Gustave L. Levy Place
Box 1215
New York, NY 10029

Systems Biology Center New York