%0 Journal Article
%T Lists2Networks: Integrated analysis of gene/protein lists
%A Alexander Lachmann
%A Avi Ma'ayan
%J BMC Bioinformatics
%D 2010
%I BioMed Central
%R 10.1186/1471-2105-11-87
%X We present Lists2Networks, a web-based system that allows users to upload lists of mammalian genes/proteins onto a server-based program for integrated analysis. The system includes web-based tools to manipulate lists with different set operations, to expand lists using existing mammalian networks of protein-protein interactions, co-expression correlation, or background knowledge co-annotation correlation, as well as to apply gene-list enrichment analyses against many gene-list libraries of prior biological knowledge such as pathways, gene ontology terms, kinase-substrate, microRNA-mRAN, and protein-protein interactions, metabolites, and protein domains. Such analyses can be applied to several lists at once against many prior knowledge libraries of gene-lists associated with specific annotations. The system also contains features that allow users to export networks and share lists with other users of the system.Lists2Networks is a user friendly web-based software system expected to significantly ease the computational analysis process for experimental systems biologists employing high-throughput experiments at multiple layers of regulation. The system is freely available at http://www.lists2networks.org webcite.Experimental biologists who incorporate high-content profiling experiments within their research often face the difficultly of understanding results from many different experiments, under different conditions, and at different layers of regulation. Results from such experiments report the activity of groups of genes that function together to give rise to changes in cellular phenotype. It is often desired to compare the results from studies such as mRNA expression microarrays, ChIP-chip or ChIP-seq (ChIP-X), RNAi screens, proteomics and phosphoproteomics in one coherent global framework. Several advanced data mining techniques have been developed to address the challenge of analyzing the complexity of such datasets. Approaches fall into different categories whi
%U http://www.biomedcentral.com/1471-2105/11/87