New methods for joint analysis of biological networks and expression data.
Biological networks, such as protein interaction, regulatory or metabolic networks, derived from public databases, biological experiments or text mining can be useful for the analysis of high-throughput experimental data. We present two algorithms embedded in the ToPNet application that show promising performance in analyzing expression data in the context of such networks. First, the Significant Area Search algorithm detects subnetworks consisting of significantly regulated genes. These subnetworks often provide hints on which biological processes are affected in the measured conditions. Second, Pathway Queries allow detection of networks including molecules that are not necessarily significantly regulated, such as transcription factors or signaling proteins. Moreover, using these queries, the user can formulate biological hypotheses and check their validity with respect to experimental data. All resulting networks and pathways can be explored further using the interactive analysis tools provided by ToPNet program.
Sohler
Florian
F
Institut für Informatik, Ludwig-Maximilians-Universität München, Amalienstrasse 17, 80333 München, Germany. florian.sohler@ifi.lmu.edu
Hanisch
Daniel
D
Zimmer
Ralf
R
D000465:Algorithms; D003196:Computer Graphics; D003198:Computer Simulation; D003628:Database Management Systems; D016208:Databases, Factual; D020869:Gene Expression Profiling / Q000379:methods*; D005786:Gene Expression Regulation / Q000502:physiology*; D016247:Information Storage and Retrieval / Q000379:methods*; D008954:Models, Biological; D015398:Signal Transduction / Q000502:physiology*; D014584:User-Computer Interface