SteinerNet: a web server for integrating 'omic' data to discover hidden components of response pathways.
High-throughput technologies including transcriptional profiling, proteomics and reverse genetics screens provide detailed molecular descriptions of cellular responses to perturbations. However, it is difficult to integrate these diverse data to reconstruct biologically meaningful signaling networks. Previously, we have established a framework for integrating transcriptional, proteomic and interactome data by searching for the solution to the prize-collecting Steiner tree problem. Here, we present a web server, SteinerNet, to make this method available in a user-friendly format for a broad range of users with data from any species. At a minimum, a user only needs to provide a set of experimentally detected proteins and/or genes and the server will search for connections among these data from the provided interactomes for yeast, human, mouse, Drosophila melanogaster and Caenorhabditis elegans. More advanced users can upload their own interactome data as well. The server provides interactive visualization of the resulting optimal network and downloadable files detailing the analysis and results. We believe that SteinerNet will be useful for researchers who would like to integrate their high-throughput data for a specific condition or cellular response and to find biologically meaningful pathways. SteinerNet is accessible at http://fraenkel.mit.edu/steinernet.
Department of Biological Engineering, Massachusetts Institute of Technology, Cambridge, MA 02139, USA.
Nurcan
N
Tuncbag
Scott
S
McCallum
Shao-Shan Carol
SS
Huang
Ernest
E
Fraenkel
D000818:Animals; D017173:Caenorhabditis elegans / Q000235:genetics / Q000378:metabolism; D004331:Drosophila melanogaster / Q000235:genetics / Q000378:metabolism; D020869:Gene Expression Profiling; D053263:Gene Regulatory Networks; D006801:Humans; D020407:Internet; D051379:Mice; D040901:Proteomics / Q000379:methods*; D015398:Signal Transduction; D012984:Software; D015003:Yeasts / Q000235:genetics / Q000378:metabolism