title: A novel algorithm for detecting differentially regulated paths based on gene set enrichment analysis.
abstract: BACKGROUND
  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 well-known 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.
  
  
  BACKGROUND
  C++ source code is freely available, BiNA can be downloaded from http://www.bnplusplus.org/.
  
  
  BACKGROUND
  ack@bioinf.uni-sb.de
  
  
  BACKGROUND
  Supplementary data are available at Bioinformatics online.
authors.affiliation: Center for Bioinformatics, Saarland University, Building E.1.1, Saarbrücken, Germany. ack@bioinf.uni-sb.de
authors.forename: Andreas
authors.initials: A
authors.lastname: Keller
authors.forename: Christina
authors.initials: C
authors.lastname: Backes
authors.forename: Andreas
authors.initials: A
authors.lastname: Gerasch
authors.forename: Michael
authors.initials: M
authors.lastname: Kaufmann
authors.forename: Oliver
authors.initials: O
authors.lastname: Kohlbacher
authors.forename: Eckart
authors.initials: E
authors.lastname: Meese
authors.forename: Hans-Peter
authors.initials: HP
authors.lastname: Lenhof
mesh_terms: D000465:Algorithms; D019295:Computational Biology / Q000379:methods*; D030541:Databases, Genetic; D020869:Gene Expression Profiling / Q000379:methods; D053263:Gene Regulatory Networks; D012333:RNA, Messenger / Q000378:metabolism
chemical_list: D012333:RNA, Messenger