ComHub: Community predictions of hubs in gene regulatory networks

Åkesson, Julia; Lubovac-Pilav, Zelmina; Magnusson, Rasmus; Gustafsson, Mika

doi:10.1186/s12859-021-03987-y

Cited by 12 publications

(12 citation statements)

References 34 publications

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“…The output of ComHub is the average outdegree of each regulator selecting the TFs with highest degree as hubs. The default value in ComHub is set to 10%, which previously resulted in highest performance ( Åkesson et al , 2021 ), but can be changed in the software by the user.…”

Section: Methodsmentioning

confidence: 99%

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MODalyseR—a novel software for inference of disease module hub regulators identified a putative multiple sclerosis regulator supported by independent eQTL data

Weerd

Åkesson

Guala

et al. 2022

Bioinformatics Advances

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Motivation Network-based disease modules have proven to be a powerful concept for extracting knowledge about disease mechanisms, predicting for example disease risk factors and side effects of treatments. Plenty of tools exist for the purpose of module inference, but less effort has been put on simultaneously utilizing knowledge about regulatory mechanisms for predicting disease module hub regulators. Results We developed MODalyseR, a novel software for identifying disease module regulators and reducing modules to the most disease associated genes. This pipeline integrates and extends previously published software packages MODifieR and ComHub and hereby provides a user-friendly network medicine framework combining the concepts of disease modules and hub regulators for precise disease gene identification from transcriptomics data. To demonstrate the usability of the tool, we designed a case study for multiple sclerosis that revealed IKZF1 as a promising hub regulator, which was supported by independent ChIP-seq data Availability MODalyseR is available as a Docker image at https://hub.docker.com/r/ddeweerd/modalyser with user guide and installation instructions found at https://gustafsson-lab.gitlab.io/MODalyseR/

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Section: Methodsmentioning

confidence: 99%

“…lack of agreement in golden standards that has hampered model comparisons and development. This led us to recently develop ComHub for robust prediction of hubs in gene regulatory networks combining the prediction of six different methods ( Åkesson et al , 2021 ).…”

Section: Introductionmentioning

confidence: 99%

MODalyseR—a novel software for inference of disease module hub regulators identified a putative multiple sclerosis regulator supported by independent eQTL data

Weerd

Åkesson

Guala

et al. 2022

Bioinformatics Advances

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“…To date, there is no complete interaction map between human TFs and their target genes, and there are multiple available approaches to infer such interactions [10]. Whereas most such approaches infer bindings from specific datasets, we sought to include dataset-independent TF-target interaction maps.…”

Section: Correlation-based Inference Of Downstream Processes Minimises False Positive Identificationsmentioning

confidence: 99%

TFTenricher: a python toolbox for annotation enrichment analysis of transcription factor target genes

Magnusson

Lubovac-Pilav

2021

BMC Bioinformatics

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Background Transcription factors (TFs) are the upstream regulators that orchestrate gene expression, and therefore a centrepiece in bioinformatics studies. While a core strategy to understand the biological context of genes and proteins includes annotation enrichment analysis, such as Gene Ontology term enrichment, these methods are not well suited for analysing groups of TFs. This is particularly true since such methods do not aim to include downstream processes, and given a set of TFs, the expected top ontologies would revolve around transcription processes. Results We present the TFTenricher, a Python toolbox that focuses specifically at identifying gene ontology terms, cellular pathways, and diseases that are over-represented among genes downstream of user-defined sets of human TFs. We evaluated the inference of downstream gene targets with respect to false positive annotations, and found an inference based on co-expression to best predict downstream processes. Based on these downstream genes, the TFTenricher uses some of the most common databases for gene functionalities, including GO, KEGG and Reactome, to calculate functional enrichments. By applying the TFTenricher to differential expression of TFs in 21 diseases, we found significant terms associated with disease mechanism, while the gene set enrichment analysis on the same dataset predominantly identified processes related to transcription. Conclusions and availability The TFTenricher package enables users to search for biological context in any set of TFs and their downstream genes. The TFTenricher is available as a Python 3 toolbox at https://github.com/rasma774/Tftenricher, under a GNU GPL license and with minimal dependencies.

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“…In GRNs, Transcription Factor encoding genes (TFs) regulate the expression of many target genes. However, only a few genes encode TFs, which can be seen as gene hubs 23 , 24 that confer GRNs with scale-free network characteristics, as suggested by previous research on the degree distribution of GRNs 25 . Numerous modifications of the Glasso algorithm distribution have been proposed in recent years to better model this complex structure of interactions in microarray data (and other real-world datasets).…”

Section: Introductionmentioning

confidence: 95%

Learning complex dependency structure of gene regulatory networks from high dimensional microarray data with Gaussian Bayesian networks

Graafland

Gutiérrez

2022

Sci Rep

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Reconstruction of Gene Regulatory Networks (GRNs) of gene expression data with Probabilistic Network Models (PNMs) is an open problem. Gene expression datasets consist of thousand of genes with relatively small sample sizes (i.e. are large-p-small-n). Moreover, dependencies of various orders coexist in the datasets. On the one hand transcription factor encoding genes act like hubs and regulate target genes, on the other hand target genes show local dependencies. In the field of Undirected Network Models (UNMs)—a subclass of PNMs—the Glasso algorithm has been proposed to deal with high dimensional microarray datasets forcing sparsity. To overcome the problem of the complex structure of interactions, modifications of the default Glasso algorithm have been developed that integrate the expected dependency structure in the UNMs beforehand. In this work we advocate the use of a simple score-based Hill Climbing algorithm (HC) that learns Gaussian Bayesian networks leaning on directed acyclic graphs. We compare HC with Glasso and variants in the UNM framework based on their capability to reconstruct GRNs from microarray data from the benchmarking synthetic dataset from the DREAM5 challenge and from real-world data from the Escherichia coli genome. We conclude that dependencies in complex data are learned best by the HC algorithm, presenting them most accurately and efficiently, simultaneously modelling strong local and weaker but significant global connections coexisting in the gene expression dataset. The HC algorithm adapts intrinsically to the complex dependency structure of the dataset, without forcing a specific structure in advance.

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ComHub: Community predictions of hubs in gene regulatory networks

Cited by 12 publications

References 34 publications

MODalyseR—a novel software for inference of disease module hub regulators identified a putative multiple sclerosis regulator supported by independent eQTL data

MODalyseR—a novel software for inference of disease module hub regulators identified a putative multiple sclerosis regulator supported by independent eQTL data

TFTenricher: a python toolbox for annotation enrichment analysis of transcription factor target genes

Learning complex dependency structure of gene regulatory networks from high dimensional microarray data with Gaussian Bayesian networks

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