Identification of context-specific gene regulatory networks with GEMULA—gene expression modeling using LAsso

The regulation of gene expression is a key factor in the development and maintenance of life in all organisms. Even so, little is known at whole genome scale for most genes and contexts. We propose a method, Tool for Weighted Epigenomic Networks in Drosophila melanogaster (Fly T-WEoN), to generate context-specific gene regulatory networks starting from a reference network that contains all known gene regulations in the fly. Unlikely regulations are removed by applying a series of knowledge-based filters. Each of these filters is implemented as an independent module that considers a type of experimental evidence, including DNA methylation, chromatin accessibility, histone modifications and gene expression. Fly T-WEoN is based on heuristic rules that reflect current knowledge on gene regulation in D. melanogaster obtained from the literature. Experimental data files can be generated with several standard procedures and used solely when and if available. Fly T-WEoN is available as a Cytoscape application that permits integration with other tools and facilitates downstream network analysis. In this work, we first demonstrate the reliability of our method to then provide a relevant application case of our tool: early development of D. melanogaster . Fly T-WEoN together with its step-by-step guide is available at https://weon.readthedocs.io .

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Inference of differential gene regulatory networks based on gene expression and genetic perturbation data

Bioinformatics ◽

10.1093/bioinformatics/btz529 ◽

2019 ◽

Vol 36 (1) ◽

pp. 197-204 ◽

Cited By ~ 2

Author(s):

Xin Zhou ◽

Xiaodong Cai

Keyword(s):

Gene Expression ◽

Gene Regulatory Networks ◽

Structural Equation ◽

Regulatory Networks ◽

Supplementary Information ◽

Specific Gene ◽

Joint Inference ◽

Perturbation Data ◽

Gene Regulatory ◽

The Difference

Abstract Motivation Gene regulatory networks (GRNs) of the same organism can be different under different conditions, although the overall network structure may be similar. Understanding the difference in GRNs under different conditions is important to understand condition-specific gene regulation. When gene expression and other relevant data under two different conditions are available, they can be used by an existing network inference algorithm to estimate two GRNs separately, and then to identify the difference between the two GRNs. However, such an approach does not exploit the similarity in two GRNs, and may sacrifice inference accuracy. Results In this paper, we model GRNs with the structural equation model (SEM) that can integrate gene expression and genetic perturbation data, and develop an algorithm named fused sparse SEM (FSSEM), to jointly infer GRNs under two conditions, and then to identify difference of the two GRNs. Computer simulations demonstrate that the FSSEM algorithm outperforms the approaches that estimate two GRNs separately. Analysis of a dataset of lung cancer and another dataset of gastric cancer with FSSEM inferred differential GRNs in cancer versus normal tissues, whose genes with largest network degrees have been reported to be implicated in tumorigenesis. The FSSEM algorithm provides a valuable tool for joint inference of two GRNs and identification of the differential GRN under two conditions. Availability and implementation The R package fssemR implementing the FSSEM algorithm is available at https://github.com/Ivis4ml/fssemR.git. It is also available on CRAN. Supplementary information Supplementary data are available at Bioinformatics online.

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Predicting genotype-specific gene regulatory networks

10.1101/2021.01.18.427134 ◽

2021 ◽

Author(s):

Deborah Weighill ◽

Marouen Ben Guebila ◽

Kimberly Glass ◽

John Quackenbush ◽

John Platig

Keyword(s):

Gene Expression ◽

Gene Regulatory Network ◽

Gene Regulatory Networks ◽

Genetic Variants ◽

Regulatory Network ◽

Regulatory Networks ◽

Specific Gene ◽

Disease Etiology ◽

Gene Regulatory ◽

The Impact

AbstractThe majority of disease-associated genetic variants are thought to have regulatory effects, including the disruption of transcription factor (TF) binding and the alteration of downstream gene expression. Identifying how a person’s genotype affects their individual gene regulatory network has the potential to provide important insights into disease etiology and to enable improved genotype-specific disease risk assessments and treatments. However, the impact of genetic variants is generally not considered when constructing gene regulatory networks. To address this unmet need, we developed EGRET (Estimating the Genetic Regulatory Effect on TFs), which infers a genotype-specific gene regulatory network (GRN) for each individual in a study population by using message passing to integrate genotype-informed TF motif predictions - derived from individual genotype data, the predicted effects of variants on TF binding and gene expression, and TF motif predictions - with TF protein-protein interactions and gene expression. Comparing EGRET networks for two blood-derived cell lines identified genotype-associated cell-line specific regulatory differences which were subsequently validated using allele-specific expression, chromatin accessibility QTLs, and differential TF binding from ChIP-seq. In addition, EGRET GRNs for three cell types across 119 individuals captured regulatory differences associated with disease in a cell-type-specific manner. Our analyses demonstrate that EGRET networks can capture the impact of genetic variants on complex phenotypes, supporting a novel fine-scale stratification of individuals based on their genetic background. EGRET is available through the Network Zoo R package (netZooR v0.9; netzoo.github.io).

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Accurate differential analysis of transcription factor activity from gene expression

10.1101/296210 ◽

2018 ◽

Author(s):

Viren Amin ◽

Murat Can Cobanoglu

Keyword(s):

Gene Expression ◽

Transcription Factor ◽

Gene Regulatory Networks ◽

Regulatory Networks ◽

Expression Data ◽

Differential Analysis ◽

Gene Regulatory ◽

Context Specific ◽

Tf Gene ◽

Perturbation Estimation

AbstractWe present EPEE (Effector and Perturbation Estimation Engine), a method for differential analysis of transcription factor (TF) activity from gene expression data. EPEE addresses two principal challenges in the field, namely incorporating context-specific TF-gene regulatory networks, and accounting for the fact that TF activity inference is intrinsically coupled for all TFs that share targets. Our validations in well-studied immune and cancer contexts show that addressing the overlap challenge and using state-of-the-art regulatory networks enable EPEE to consistently produce accurate results. (Accessible at: https://github.com/Cobanoglu-Lab/EPEE)

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Expression pattern determines regulatory logic

PLoS ONE ◽

10.1371/journal.pone.0244864 ◽

2021 ◽

Vol 16 (1) ◽

pp. e0244864

Author(s):

Carlos Mora-Martinez

Keyword(s):

Gene Expression ◽

Gene Regulatory Networks ◽

Dna Sequences ◽

In Silico ◽

Regulatory Networks ◽

Expression Patterns ◽

Cell Types ◽

Evolutionary Strategy ◽

Specific Gene ◽

Gene Regulatory

Large amounts of effort have been invested in trying to understand how a single genome is able to specify the identity of hundreds of cell types. Inspired by some aspects of Caenorhabditis elegans biology, we implemented an in silico evolutionary strategy to produce gene regulatory networks (GRNs) that drive cell-specific gene expression patterns, mimicking the process of terminal cell differentiation. Dynamics of the gene regulatory networks are governed by a thermodynamic model of gene expression, which uses DNA sequences and transcription factor degenerate position weight matrixes as input. In a version of the model, we included chromatin accessibility. Experimentally, it has been determined that cell-specific and broadly expressed genes are regulated differently. In our in silico evolved GRNs, broadly expressed genes are regulated very redundantly and the architecture of their cis-regulatory modules is different, in accordance to what has been found in C. elegans and also in other systems. Finally, we found differences in topological positions in GRNs between these two classes of genes, which help to explain why broadly expressed genes are so resilient to mutations. Overall, our results offer an explanatory hypothesis on why broadly expressed genes are regulated so redundantly compared to cell-specific genes, which can be extrapolated to phenomena such as ChIP-seq HOT regions.

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Data integration for inferring context-specific gene regulatory networks

Current Opinion in Systems Biology ◽

10.1016/j.coisb.2020.09.005 ◽

2020 ◽

Vol 23 ◽

pp. 38-46

Author(s):

Brittany Baur ◽

Junha Shin ◽

Shilu Zhang ◽

Sushmita Roy

Keyword(s):

Data Integration ◽

Gene Regulatory Networks ◽

Regulatory Networks ◽

Specific Gene ◽

Gene Regulatory ◽

Context Specific

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CLUSTERING CONTEXT-SPECIFIC GENE REGULATORY NETWORKS

Biocomputing 2010 ◽

10.1142/9789814295291_0046 ◽

2009 ◽

pp. 444-455 ◽

Cited By ~ 2

Author(s):

ARCHANA RAMESH ◽

ROBERT TREVINO ◽

DANIEL D. VON HOFF ◽

SEUNGCHAN KIM

Keyword(s):

Gene Regulatory Networks ◽

Regulatory Networks ◽

Specific Gene ◽

Gene Regulatory ◽

Context Specific

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Automated generation of context-specific Gene Regulatory Networks with a weighted approach in D. melanogaster

10.1101/2020.08.03.232959 ◽

2020 ◽

Author(s):

Leandro Murgas ◽

Sebastian Contreras-Riquelme ◽

J. Eduardo Martínez ◽

Camilo Villaman ◽

Rodrigo Santibáñez ◽

...

Keyword(s):

Gene Expression ◽

Transcription Factors ◽

Gene Regulatory Networks ◽

Regulatory Networks ◽

Current Knowledge ◽

Regulation Of Gene Expression ◽

Chromatin Accessibility ◽

Reference Network ◽

Specific Gene ◽

Gene Regulatory

AbstractMotivationThe regulation of gene expression is a key factor in the development and maintenance of life in all organisms. This process is carried out mainly through the action of transcription factors (TFs), although other actors such as ncRNAs are involved. In this work, we propose a new method to construct Gene Regulatory Networks (GRNs) depicting regulatory events in a certain context for Drosophila melanogaster. Our approach is based on known relationships between epigenetics and the activity of transcription factors.ResultsWe developed method, Tool for Weighted Epigenomic Networks in D. melanogaster (Fly T-WEoN), which generates GRNs starting from a reference network that contains all known gene regulations in the fly. Regulations that are unlikely taking place are removed by applying a series of knowledge-based filters. Each of these filters is implemented as an independent module that considers a type of experimental evidence, including DNA methylation, chromatin accessibility, histone modifications, and gene expression. Fly T-WEoN is based on heuristic rules that reflect current knowledge on gene regulation in D. melanogaster obtained from literature. Experimental data files can be generated with several standard procedures and used solely when and if available.Fly T-WEoN is available as a Cytoscape application that permits integration with other tools, and facilitates downstream network analysis. In this work, we first demonstrate the reliability of our method to then provide a relevant application case of our tool: early development of D. melanogaster.AvailabilityFly T-WEoN, together with its step-by-step guide is available at https://[email protected]

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Context-specific gene regulatory networks subdivide intrinsic subtypes of breast cancer

BMC Bioinformatics ◽

10.1186/1471-2105-12-s2-s3 ◽

2011 ◽

Vol 12 (Suppl 2) ◽

pp. S3 ◽

Cited By ~ 4

Author(s):

Sara Nasser ◽

Heather E Cunliffe ◽

Michael A Black ◽

Seungchan Kim

Keyword(s):

Breast Cancer ◽

Gene Regulatory Networks ◽

Regulatory Networks ◽

Specific Gene ◽

Intrinsic Subtypes ◽

Gene Regulatory ◽

Context Specific

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Inference of Differential Gene Regulatory Networks Based on Gene Expression and Genetic Perturbation Data

10.1101/466623 ◽

2018 ◽

Cited By ~ 1

Author(s):

Xin Zhou ◽

Xiaodong Cai

Keyword(s):

Gene Expression ◽

Lung Cancer ◽

Gene Regulatory Networks ◽

Regulatory Networks ◽

Normal Lung ◽

Specific Gene ◽

Joint Inference ◽

Perturbation Data ◽

Gene Regulatory ◽

The Difference

AbstractMotivationGene regulatory networks (GRNs) of the same organism can be different under different conditions, although the overall network structure may be similar. Understanding the difference in GRNs under different conditions is important to understand condition-specific gene regulation. When gene expression and other relevant data under two different conditions are available, they can be used by an existing network inference algorithm to estimate two GRNs separately, and then to identify the difference between the two GRNs. However, such an approach does not exploit the similarity in two GRNs, and may sacrifice inference accuracy.ResultsIn this paper, we model GRNs with the structural equation model (SEM) that can integrate gene expression and genetic perturbation data, and develop an algorithm named fused sparse SEM (FSSEM), to jointly infer GRNs under two conditions, and then to identify difference of the two GRNs. Computer simulations demonstrate that the FSSEM algorithm outperforms the approach that estimates two GRNs separately. Analysis of a gene expression and SNP dataset of lung cancer and normal lung tissues with FSSEM inferred a GRN largely agree with the known lung GRN reported in the literature, and it identified a differential GRN, whose genes with largest degrees were reported to be implicated in lung cancer. The FSSEM algorithm provides a valuable tool for joint inference of two GRNs and identification of the differential GRN under two conditions.AvailabilityThe software package for the FSSEM algorithm is available at https://github.com/Ivis4ml/[email protected]

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