Interactional and functional centrality in transcriptional co-expression networks.
BACKGROUND
The noisy nature of transcriptomic data hinders the biological relevance of conventional network centrality measures, often used to select gene candidates in co-expression networks. Therefore, new tools and methods are required to improve the prediction of mechanistically important transcriptional targets.


RESULTS
We propose an original network centrality measure, called annotation transcriptional centrality (ATC) computed by integrating gene expression profiles from microarray experiments with biological knowledge extracted from public genomic databases. ATC computation algorithm delimits representative functional domains in the co-expression network and then relies on this information to find key nodes that modulate propagation of functional influences within the network. We demonstrate ATC ability to predict important genes in several experimental models and provide improved biological relevance over conventional topological network centrality measures.


BACKGROUND
ATC computational routine is implemented in a publicly available tool named FunNet (www.funnet.info).
INSERM, UMR-S 872, Les Cordeliers, Eq. 7 Nutriomique, Paris, France. edi.prifti@crc.jussieu.fr
Edi
E
Prifti
Jean-Daniel
JD
Zucker
Karine
K
Clément
Corneliu
C
Henegar
D000465:Algorithms; D030541:Databases, Genetic; D020869:Gene Expression Profiling; D053263:Gene Regulatory Networks; D008957:Models, Genetic; D014158:Transcription, Genetic