BRANE Clust: Cluster-Assisted Gene Regulatory Network Inference Refinement

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

doi:10.1101/114769

Cited by 8 publications

(8 citation statements)

References 40 publications

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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%

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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“…To provide informative, non-redundant features, a pre-processing step is needed to cluster the data samples into a smaller number of clusters based on their similarity. This step differs from determination of clusters based on genes, applied in other GRN reconstruction approaches 27 . To this end, GRADIS employs the k-means clustering algorithm, so that the original data samples are grouped into k clusters.…”

Section: Formulation Of the Gradis Approachmentioning

confidence: 99%

Supervised learning of gene-regulatory networks based on graph distance profiles of transcriptomics data

Razaghi-Moghadam

Nikoloski

2020

npj Syst Biol Appl

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Characterisation of gene-regulatory network (GRN) interactions provides a stepping stone to understanding how genes affect cellular phenotypes. Yet, despite advances in profiling technologies, GRN reconstruction from gene expression data remains a pressing problem in systems biology. Here, we devise a supervised learning approach, GRADIS, which utilises support vector machine to reconstruct GRNs based on distance profiles obtained from a graph representation of transcriptomics data. By employing the data from Escherichia coli and Saccharomyces cerevisiae as well as synthetic networks from the DREAM4 and five network inference challenges, we demonstrate that our GRADIS approach outperforms the state-of-the-art supervised and unsupervided approaches. This holds when predictions about target genes for individual transcription factors as well as for the entire network are considered. We employ experimentally verified GRNs from E. coli and S. cerevisiae to validate the predictions and obtain further insights in the performance of the proposed approach. Our GRADIS approach offers the possibility for usage of other network-based representations of large-scale data, and can be readily extended to help the characterisation of other cellular networks, including protein-protein and protein-metabolite interactions.

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BRANE Clust: Cluster-Assisted Gene Regulatory Network Inference Refinement

Cited by 8 publications

References 40 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

Supervised learning of gene-regulatory networks based on graph distance profiles of transcriptomics data

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