Motivation: Deregulated signaling cascades are known to play a
crucial role in many pathogenic processes, among them are tumor
initiation and progression. In the recent past, modern experimental
techniques that allow for measuring the amount of mRNA transcripts
of almost all known human genes in a tissue or even in a single cell
have opened new avenues for studying the activity of the signaling
cascades and for understanding the information flow in the networks.
Results: We present a novel dynamic programming algorithm for
detecting deregulated signaling cascades. The so-called FiDePa
(Finding Deregulated Paths) algorithm interprets differences in the
expression profiles of tumor and normal tissues. It relies on the wellknown
gene set enrichment analysis (GSEA) and efficiently detects all
paths in a given regulatory or signaling network that are significantly
enriched with differentially expressed genes or proteins. Since our
algorithm allows for comparing a single tumor expression profile with
the control group, it facilitates the detection of specific regulatory
features of a tumor that may help to optimize tumor therapy. To
demonstrate the capabilities of our algorithm, we analyzed a glioma
expression dataset with respect to a directed graph that combined
the regulatory networks of the KEGG and TRANSPATH database. The
resulting glioma consensus network that encompasses all detected
deregulated paths contained many genes and pathways that are
known to be key players in glioma or cancer-related pathogenic
processes. Moreover, we were able to correlate clinically relevant
features like necrosis or metastasis with the detected paths.
Availability: C++ source code is freely available, BiNA can be
downloaded from http://www.bnplusplus.org/.
Contact: ack [at] bioinf [dot] uni-sb [dot] de
Supplementary information: Supplementary data are available at
Bioinformatics online.
| 