Abstract
BackgroundInferring gene networks from high-throughput data constitutes an important step in the discovery of relevant regulatory relationships in organism cells. Despite the large number of available Gene Regulatory Network inference methods, the problem remains challenging: the underdetermination in the space of possible solutions requires additional constraints that incorporate a priori information on gene interactions.MethodsWeighting all possible pairwise gene relationships by a probability of edge presence, we formulate the regulatory network inference as a discrete variational problem on graphs. We enforce biologically plausible coupling between groups and types of genes by minimizing an edge labeling functional coding for a priori structures. The optimization is carried out with Graph cuts, an approach popular in image processing and computer vision. We compare the inferred regulatory networks to results achieved by the mutual-information-based Context Likelihood of Relatedness (CLR) method and by the state-of-the-art GENIE3, winner of the DREAM4 multifactorial challenge.ResultsOur BRANE Cut approach infers more accurately the five DREAM4 in silico networks (with improvements from 6 % to 11 %). On a real Escherichia coli compendium, an improvement of 11.8 % compared to CLR and 3 % compared to GENIE3 is obtained in terms of Area Under Precision-Recall curve. Up to 48 additional verified interactions are obtained over GENIE3 for a given precision. On this dataset involving 4345 genes, our method achieves a performance similar to that of GENIE3, while being more than seven times faster. The BRANE Cut code is available at: http://www-syscom.univ-mlv.fr/~pirayre/Codes-GRN-BRANE-cut.html.ConclusionsBRANE Cut is a weighted graph thresholding method. Using biologically sound penalties and data-driven parameters, it improves three state-of-the art GRN inference methods. It is applicable as a generic network inference post-processing, due to its computational efficiency.Electronic supplementary materialThe online version of this article (doi:10.1186/s12859-015-0754-2) contains supplementary material, which is available to authorized users.
Highlights
Inferring gene networks from high-throughput data constitutes an important step in the discovery of relevant regulatory relationships in organism cells
The evaluation of the inferred networks was performed using the gold standard provided in the DREAM4 multifactorial challenge
To validate our BRANE Cut approach, we used a variety of different initial weights, directly obtained from Context Likelihood of Relatedness (CLR), GENIE3, or after ND postprocessing [12] (NDCLR and ND-GENIE3)
Summary
Inferring gene networks from high-throughput data constitutes an important step in the discovery of relevant regulatory relationships in organism cells. Despite the large variety of proposed GRN inference methods, building a GRN remains a challenging task due to the nature of gene expression and the structure of Pirayre et al BMC Bioinformatics (2015) 16:369 the experimental data. It notably involves data dimensionality, especially in terms of gene/replicate/condition proportions. The cost of biological experiments diminishes, gene expression data is often acquired under a limited number of replicates and conditions compared to the number of genes This causes difficulties in properly inferring gene regulatory networks and in recovering reliable biological interpretations of such networks. Continuous efforts from the bioinformatics community, partly driven by the organization of the DREAM challenges [2], hitherto allowed for constant progresses in GRN inference efficiency
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