BRANE Cut: Biologically-Related A priori Network Enhancement with Graph cuts for Gene Regulatory Network Inference

Pirayre, Aurélie; Bidard, Frédérique; Duval, Laurent; Pesquet, Jean-Christophe

doi:10.1101/032383

Cited by 8 publications

(13 citation statements)

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“…It should finally be noted that brain networks are not the only biological networks where GSP offers promising solutions. Graph signal processing elements and biological priors are combined to infer networks and discover meaningful interactions in gene regulatory networks, as in [182], [183]. The inference of the structure of protein interaction networks has also been addressed with help of spectral graph templates [148].…”

Section: B Biological Networkmentioning

confidence: 99%

Graph Signal Processing: Overview, Challenges, and Applications

et al. 2018

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Research in Graph Signal Processing (GSP) aims to develop tools for processing data defined on irregular graph domains. In this paper we first provide an overview of core ideas in GSP and their connection to conventional digital signal processing, along with a brief historical perspective to highlight how concepts recently developed in GSP build on top of prior research in other areas. We then summarize recent advances in developing basic GSP tools, including methods for sampling, filtering or graph learning. Next, we review progress in several application areas using GSP, including processing and analysis of sensor network data, biological data, and applications to image processing and machine learning.

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Section: B Biological Networkmentioning

confidence: 99%

Graph Signal Processing: Overview, Challenges, and Applications

et al. 2018

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“…From the set of DE genes, we built a gene regulatory network with the combination of CLR [ 61 ] and BRANE Cut [ 40 , 62 ] inference methods. When the use was judicious, we evaluated our discovered TF-targets interactions by performing a promoter analysis of the plausible targets given by the inferred network, with the Regulatory Sequence Analysis Tool (RSAT) [ 63 ].…”

Section: Resultsmentioning

confidence: 99%

“…Network enhancement thresholding performed by BRANE Cut post-processing [ 40 ] selected 161 genes (including 15 transcription factors) and inferred 205 links (Fig. 4 ).…”

Section: Resultsmentioning

confidence: 99%

“…GRN may be useful to visualize sketches of regulatory relationships and to unveil meaningful information from high-throughput data [ 39 ]. We employed BRANE Cut [ 40 ], a Biologically-Related Apriori Network Enhancement method based on graph cuts, previously developed by our team. It has been proven to provide robust meaningful inference on real and synthetic datasets from [ 41 , 42 ].…”

Section: Introductionmentioning

confidence: 99%

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Glucose-lactose mixture feeds in industry-like conditions: a gene regulatory network analysis on the hyperproducing Trichoderma reesei strain Rut-C30

et al. 2020

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Background The degradation of cellulose and hemicellulose molecules into simpler sugars such as glucose is part of the second generation biofuel production process. Hydrolysis of lignocellulosic substrates is usually performed by enzymes produced and secreted by the fungus Trichoderma reesei. Studies identifying transcription factors involved in the regulation of cellulase production have been conducted but no overview of the whole regulation network is available. A transcriptomic approach with mixtures of glucose and lactose, used as a substrate for cellulase induction, was used to help us decipher missing parts in the network of T. reesei Rut-C30. Results Experimental results on the Rut-C30 hyperproducing strain confirmed the impact of sugar mixtures on the enzymatic cocktail composition. The transcriptomic study shows a temporal regulation of the main transcription factors and a lactose concentration impact on the transcriptional profile. A gene regulatory network built using BRANE Cut software reveals three sub-networks related to i) a positive correlation between lactose concentration and cellulase production, ii) a particular dependence of the lactose onto the β-glucosidase regulation and iii) a negative regulation of the development process and growth. Conclusions This work is the first investigating a transcriptomic study regarding the effects of pure and mixed carbon sources in a fed-batch mode. Our study expose a co-orchestration of xyr1, clr2 and ace3 for cellulase and hemicellulase induction and production, a fine regulation of the β-glucosidase and a decrease of growth in favor of cellulase production. These conclusions provide us with potential targets for further genetic engineering leading to better cellulase-producing strains in industry-like conditions.

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“…Our approach is based on Markov Random Fields 16 and motivated by applications in Computer Vision such as image denoising or segmentation tasks 17 18 . Borrowing concepts from the field of Computer Vision to infer gene regulatory networks in prokaryotes has recently gained some attention 19 . GRACE enhances gene regulatory networks by integrating two complementary network data: (i) DNA binding based regulatory networks and (ii) co-functional networks.…”

mentioning

confidence: 99%

Enhancing gene regulatory network inference through data integration with markov random fields

Banf

Rhee

2017

Sci Rep

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A gene regulatory network links transcription factors to their target genes and represents a map of transcriptional regulation. Much progress has been made in deciphering gene regulatory networks computationally. However, gene regulatory network inference for most eukaryotic organisms remain challenging. To improve the accuracy of gene regulatory network inference and facilitate candidate selection for experimentation, we developed an algorithm called GRACE (Gene Regulatory network inference ACcuracy Enhancement). GRACE exploits biological a priori and heterogeneous data integration to generate high-confidence network predictions for eukaryotic organisms using Markov Random Fields in a semi-supervised fashion. GRACE uses a novel optimization scheme to integrate regulatory evidence and biological relevance. It is particularly suited for model learning with sparse regulatory gold standard data. We show GRACE's potential to produce high confidence regulatory networks compared to state of the art approaches using Drosophila melanogaster and Arabidopsis thaliana data. In an A. thaliana developmental gene regulatory network, GRACE recovers cell cycle related regulatory mechanisms and further hypothesizes several novel regulatory links, including a putative control mechanism of vascular structure formation due to modifications in cell proliferation.

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BRANE Cut: Biologically-Related A priori Network Enhancement with Graph cuts for Gene Regulatory Network Inference

Cited by 8 publications

References 34 publications

Graph Signal Processing: Overview, Challenges, and Applications

Graph Signal Processing: Overview, Challenges, and Applications

Glucose-lactose mixture feeds in industry-like conditions: a gene regulatory network analysis on the hyperproducing Trichoderma reesei strain Rut-C30

Enhancing gene regulatory network inference through data integration with markov random fields

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