The BRANE Page (Biologically-Related A priori for genomic data)

We do develop a series of bioinformatics tools based on graphs and optimization, dedicated to -omics gene expression data (from RNA-seq or microarrays). They are meant for Gene Regulatory Network (GRN) inference. Genomic data is complicated. Especially, gene-condition and transcription factor-target gene ratios are not favorable. Data counts are biased.

There exists therefore a large number of solutions. One needs to introduce a priori to improve the graph structure, for instance around modules, or using gene clustering. We use biological assumptions, and propose a couple of Biologically-Related A priori Network Enhancement (BRANE) techniques. They can be used in post-processing of other network inferences CLR (Context Likelihood of Relatedness) or GENIE3 (GEne Network Inference with Ensemble of trees). They have been successfully tested on DREAM4 and DREAM5 challenges, Escherichia coli, Trichoderma reesei. Inference  in the inferred Escherichia coli network is evaluated using the STRING database. Clustering is compared to SIMONE, WGCNA, X-means and RegulonDB.

We call that BRANE power:)

Publications on the methodology:
Work in progress:
  • BRANE HK (aka BRANE Cone)
BRANE: Biologically-related A priori Network Enhancement for gene regulation network
A note in passing: on Gilles Azzoni, wine producer (Le Raisin et l'Ange), who inspired our BRANE tools with his biodynamic Bran wines.