Bioinformatics and Systems Medicine Laboratory

About GenRev

Outputs from many experiment platforms are commonly lists of genes. Exploring the relevance among these genes in the list(s) is very useful to understand the perturbed pathways and functions in the studied disease and phenotype.

To address this question in a network approach, we present a package called GenRev. GenRev is a Python software package that identifies subnetworks for a set of genes from a large network. Three algorithms are implemented in GenRev, the Klein-Ravi algorithm for node weighted Steiner tree problem, the limited k-walk algorithm and a heuristic local search algorithm. Each algorithm is implemented in an independent module. Another analysis module is also developed for graph clustering and gene ranking. This package can be used either as a standalone application, or as Python modules. The left figure shows the flowchart of GenRev.

Current Version
Related download
Additional randomization code
Test dataset and results
Reference networks
  • Klein, P. and Ravi, R. (1995) A nearly best-possible approximation algorithm for node-weighted Steiner trees. J. Algorithms, 19, 104-115.
  • Dupont, P. et al. (2006) Relevant subgraph extraction from random walks in a graph. Research report UCL/FSA/INGI 2006-07.
  • Chuang, H.Y. et al. (2007) Network-based classification of breast cancer metastasis. Mol. Syst. Biol., 3, 140. PubMed
  • Lee I. et al. (2004) A probabilistic functional network of yeast genes. Science, 306, 1555-1558 PubMed
  • Hu Z. et al. (2005) VisANT: data-integrating visual framework for biological networks and modules. Nucleic Acids Res., 33, W352-7. PubMed
  • Zheng S, Zhao Z (2012) GenRev: exploring functional relevance of genes in molecular networks. Genomics 99(3):183-188. PubMed link

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