Dataset Information

ComHub: Community predictions of hubs in gene regulatory networks.

ABSTRACT:

Background

Hub transcription factors, regulating many target genes in gene regulatory networks (GRNs), play important roles as disease regulators and potential drug targets. However, while numerous methods have been developed to predict individual regulator-gene interactions from gene expression data, few methods focus on inferring these hubs.

Results

We have developed ComHub, a tool to predict hubs in GRNs. ComHub makes a community prediction of hubs by averaging over predictions by a compendium of network inference methods. Benchmarking ComHub against the DREAM5 challenge data and two independent gene expression datasets showed a robust performance of ComHub over all datasets.

Conclusions

In contrast to other evaluated methods, ComHub consistently scored among the top performing methods on data from different sources. Lastly, we implemented ComHub to work with both predefined networks and to perform stand-alone network inference, which will make the method generally applicable.

SUBMITTER: Akesson J

PROVIDER: S-EPMC7871572 | biostudies-literature | 2021 Feb

REPOSITORIES: biostudies-literature

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Publications

ComHub: Community predictions of hubs in gene regulatory networks.

Åkesson Julia J Lubovac-Pilav Zelmina Z Magnusson Rasmus R Gustafsson Mika M

BMC bioinformatics 20210209 1

<h4>Background</h4>Hub transcription factors, regulating many target genes in gene regulatory networks (GRNs), play important roles as disease regulators and potential drug targets. However, while numerous methods have been developed to predict individual regulator-gene interactions from gene expression data, few methods focus on inferring these hubs.<h4>Results</h4>We have developed ComHub, a tool to predict hubs in GRNs. ComHub makes a community prediction of hubs by averaging over predictions ...[more]

PMID: 33563211

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Project description:BackgroundMicroRNAs (miRNAs) are small non-coding RNAs which play a key role in the post-transcriptional regulation of many genes. Elucidating miRNA-regulated gene networks is crucial for the understanding of mechanisms and functions of miRNAs in many biological processes, such as cell proliferation, development, differentiation and cell homeostasis, as well as in many types of human tumors. To this aim, we have recently presented the biclustering method HOCCLUS2, for the discovery of miRNA regulatory networks. Experiments on predicted interactions revealed that the statistical and biological consistency of the obtained networks is negatively affected by the poor reliability of the output of miRNA target prediction algorithms. Recently, some learning approaches have been proposed to learn to combine the outputs of distinct prediction algorithms and improve their accuracy. However, the application of classical supervised learning algorithms presents two challenges: i) the presence of only positive examples in datasets of experimentally verified interactions and ii) unbalanced number of labeled and unlabeled examples.ResultsWe present a learning algorithm that learns to combine the score returned by several prediction algorithms, by exploiting information conveyed by (only positively labeled/) validated and unlabeled examples of interactions. To face the two related challenges, we resort to a semi-supervised ensemble learning setting. Results obtained using miRTarBase as the set of labeled (positive) interactions and mirDIP as the set of unlabeled interactions show a significant improvement, over competitive approaches, in the quality of the predictions. This solution also improves the effectiveness of HOCCLUS2 in discovering biologically realistic miRNA:mRNA regulatory networks from large-scale prediction data. Using the miR-17-92 gene cluster family as a reference system and comparing results with previous experiments, we find a large increase in the number of significantly enriched biclusters in pathways, consistent with miR-17-92 functions.ConclusionThe proposed approach proves to be fundamental for the computational discovery of miRNA regulatory networks from large-scale predictions. This paves the way to the systematic application of HOCCLUS2 for a comprehensive reconstruction of all the possible multiple interactions established by miRNAs in regulating the expression of gene networks, which would be otherwise impossible to reconstruct by considering only experimentally validated interactions.

Project description:BackgroundDuring maturation seeds acquire several physiological traits to enable them to survive drying and disseminate the species. Few studies have addressed the regulatory networks controlling acquisition of these traits at the tissue level particularly in endospermic seeds such as tomato, which matures in a fully hydrated environment and does not undergo maturation drying. Using temporal RNA-seq analyses of the different seed tissues during maturation, gene network and trait-based correlations were used to explore the transcriptome signatures associated with desiccation tolerance, longevity, germination under water stress and dormancy.ResultsDuring maturation, 15,173 differentially expressed genes were detected, forming a gene network representing 21 expression modules, with 3 being specific to seed coat and embryo and 5 to the endosperm. A gene-trait significance measure identified a common gene module between endosperm and embryo associated with desiccation tolerance and conserved with non-endospermic seeds. In addition to genes involved in protection such LEA and HSP and ABA response, the module included antioxidant and repair genes. Dormancy was released concomitantly with the increase in longevity throughout fruit ripening until 14?days after the red fruit stage. This was paralleled by an increase in SlDOG1-2 and PROCERA transcripts. The progressive increase in seed vigour was captured by three gene modules, one in common between embryo and endosperm and two tissue-specific. The common module was enriched with genes associated with mRNA processing in chloroplast and mitochondria (including penta- and tetratricopeptide repeat-containing proteins) and post-transcriptional regulation, as well several flowering genes. The embryo-specific module contained homologues of ABI4 and CHOTTO1 as hub genes associated with seed vigour, whereas the endosperm-specific module revealed a diverse set of processes that were related to genome stability, defence against pathogens and ABA/GA response genes.ConclusionThe spatio-temporal co-expression atlas of tomato seed maturation will serve as a valuable resource for the in-depth understanding of the dynamics of gene expression associated with the acquisition of seed vigour at the tissue level.

Dataset Information

ComHub: Community predictions of hubs in gene regulatory networks.

Background

Results

Conclusions

Publications

ComHub: Community predictions of hubs in gene regulatory networks.

Similar Datasets

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