NeAT: a toolbox for the analysis of biological networks, clusters, classes and pathways.

Brohée, Sylvain;Faust, Karoline;Lima Mendez, Gipsi;Sand, Olivier;van Helden, Jacques;et.al.
(2008) Nucleic acids research — Vol. 36, n° Web Server issue, p. W444-51 (2008)

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Authors
  • Brohée, Sylvain
    Author
  • Faust, Karoline
    Author
  • Lima Mendez, GipsiUCLouvain
    Author
  • Sand, Olivier
    Author
  • Deville, Yvesorcid-logoUCLouvain
    Author
  • van Helden, Jacques
    Author
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Abstract
The network analysis tools (NeAT) (http://rsat.ulb.ac.be/neat/) provide a user-friendly web access to a collection of modular tools for the analysis of networks (graphs) and clusters (e.g. microarray clusters, functional classes, etc.). A first set of tools supports basic operations on graphs (comparison between two graphs, neighborhood of a set of input nodes, path finding and graph randomization). Another set of programs makes the connection between networks and clusters (graph-based clustering, cliques discovery and mapping of clusters onto a network). The toolbox also includes programs for detecting significant intersections between clusters/classes (e.g. clusters of co-expression versus functional classes of genes). NeAT are designed to cope with large datasets and provide a flexible toolbox for analyzing biological networks stored in various databases (protein interactions, regulation and metabolism) or obtained from high-throughput experiments (two-hybrid, mass-spectrometry and microarrays). The web interface interconnects the programs in predefined analysis flows, enabling to address a series of questions about networks of interest. Each tool can also be used separately by entering custom data for a specific analysis. NeAT can also be used as web services (SOAP/WSDL interface), in order to design programmatic workflows and integrate them with other available resources.
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Citations

Brohée, S., Faust, K., Lima Mendez, G., Sand, O., Janky, R., Vanderstocken, G., Deville, Y., & van Helden, J. (2008). NeAT: a toolbox for the analysis of biological networks, clusters, classes and pathways. Nucleic acids research, 36(Web Server issue), W444-51. https://doi.org/10.1093/nar/gkn336 (Original work published 2008)