Since a few years, large scale biological studies produced huge amounts of data about networks of molecular interactions (protein interactions, gene regulation, metabolic reactions, signal transduction). The integration of these data sets can be combined to acquire a global view of the pieces that, altogether, contribute to the complexity of biological processes. High-throughput data is however notoriously noisy and incomplete, and it is important to evaluate the quality of the different pieces of information that are taken in consideration for building higher views of biological networks.
An important effort will be required to extract reliable information from the ever-increasing ocean of high-throughput data. This will require the utilization of powerful tools that enable us to apply statistical analysis on large graphs. For this purpose, we developed the Network Analysis Tools (NeAT), as set of tools performing basic operations on networks and clusters. The web interface gives a convenient and intuitive access to the tools, and allows you to bring your data sets through some typical analysis work flows in order to extract the best of it.
In addition, people having computer skills can also use be same tools via a Web services interface, in order to integrate them in automatic work-flows.
Each tool is presented as a form to fill. For each form, a manual page provides detailed information about the parameters. To get familiar with the tools, we recommend you to follow the tutorials.
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