scholarly journals TReNCo: Topologically associating domain (TAD) aware regulatory network construction

2021 ◽  
Author(s):  
Christopher Bennett ◽  
Viren Amin ◽  
Daehwan Kim ◽  
Murat Cobanoglu ◽  
Venkat Malladi
Keyword(s):  
Gene Regulatory Networks ◽  
Regulatory Network ◽  
Regulatory Networks ◽  
Cardiac Development ◽  
Network Construction ◽  
Biologically Relevant ◽  
Topologically Associated Domains ◽  
Gene Regulatory ◽  
Context Specific ◽  
Tf Gene

AbstractThere has long been a desire to understand, describe, and model gene regulatory networks controlling numerous biologically meaningful processes like differentiation. Despite many notable improvements to models over the years, many models do not accurately capture subtle biological and chemical characteristics of the cell such as high-order chromatin domains of the chromosomes. Topologically Associated Domains (TAD) are one of these genomic regions that are enriched for contacts within themselves. Here we present TAD-aware Regulatory Network Construction or TReNCo, a memory-lean method utilizing epigenetic marks of enhancer and promoter activity, and gene expression to create context-specific transcription factor-gene regulatory networks. TReNCo utilizes common assay’s, ChIP-seq, RNA-seq, and TAD boundaries as a hard cutoff, instead of distance based, to efficiently create context-specific TF-gene regulatory networks. We used TReNCo to define the enhancer landscape and identify transcription factors (TFs) that drive the cardiac development of the mouse. Our results show that we are able to build specialized adjacency regulatory network graphs containing biologically relevant connections and time dependent dynamics.

scholarly journals Accurate differential analysis of transcription factor activity from gene expression

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)

scholarly journals NetREX: Network Rewiring using EXpression - Towards Context Specific Regulatory Networks

2017 ◽  
Author(s):  
Yijie Wang ◽  
Dong-Yeon Cho ◽  
Hangnoh Lee ◽  
Justin Fear ◽  
Brian Oliver ◽  
...  
Keyword(s):  
Transcription Factor ◽  
Regulatory Network ◽  
Gene Network ◽  
Regulatory Networks ◽  
Tissue Type ◽  
Expression Data ◽  
Gene Regulatory ◽  
Context Specific ◽  
Tf Gene ◽  
Biological Context

AbstractUnderstanding gene regulation is a fundamental step towards understanding of how cells function and respond to environmental cues and perturbations. An important step in this direction is the ability to infer the transcription factor (TF)-gene regulatory network (GRN). However gene regulatory networks are typically constructed disregarding the fact that regulatory programs are conditioned on tissue type, developmental stage, sex, and other factors. Due to lack of the biological context specificity, these context-agnostic networks may not provide insight for revealing the precise actions of genes for a specific biological system under concern. Collecting multitude of features required for a reliable construction of GRNs such as physical features (TF binding, chromatin accessibility) and functional features (correlation of expression or chromatin patterns) for every context of interest is costly. Therefore we need methods that is able to utilize the knowledge about a context-agnostic network (or a network constructed in a related context) for construction of a context specific regulatory network.To address this challenge we developed a computational approach that utilizes expression data obtained in a specific biological context such as a particular development stage, sex, tissue type and a GRN constructed in a different but related context (alternatively an incomplete or a noisy network for the same context) to construct a context specific GRN. Our method, NetREX, is inspired by network component analysis (NCA) that estimates TF activities and their influences on target genes given predetermined topology of a TF-gene network. To predict a network under a different condition, NetREX removes the restriction that the topology of the TF-gene network is fixed and allows for adding and removing edges to that network. To solve the corresponding optimization problem, which is non-convex and non-smooth, we provide a general mathematical framework allowing use of the recently proposed Proximal Alternative Linearized Maximization technique and prove that our formulation has the properties required for convergence.We tested our NetREX on simulated data and subsequently applied it to gene expression data in adult females from 99 hemizygotic lines of the Drosophila deletion (DrosDel) panel. The networks predicted by NetREX showed higher biological consistency than alternative approaches. In addition, we used the list of recently identified targets of the Doublesex (DSX) transcription factor to demonstrate the predictive power of our method.

scholarly journals GRaNIE and GRaNPA: Inference and evaluation of enhancer-mediated gene regulatory networks applied to study macrophages

2021 ◽  
Author(s):  
Aryan Kamal ◽  
Christian Arnold ◽  
Annique Claringbould ◽  
Rim Moussa ◽  
Neha Daga ◽  
...  
Keyword(s):  
Gene Regulatory Network ◽  
Gene Regulatory Networks ◽  
Genetic Variants ◽  
Regulatory Network ◽  
Regulatory Networks ◽  
Open Chromatin ◽  
Genome Wide Association Studies ◽  
Gene Regulatory Network Inference ◽  
Gene Regulatory ◽  
Tf Gene

Among the biggest challenges in the post-GWAS (genome-wide association studies) era is the interpretation of disease-associated genetic variants in non-coding genomic regions. Enhancers have emerged as key players in mediating the effect of genetic variants on complex traits and diseases. Their activity is regulated by a combination of transcription factors (TFs), epigenetic changes and genetic variants. Several approaches exist to link enhancers to their target genes, and others that infer TF-gene connections. However, we currently lack a framework that systematically integrates enhancers into TF-gene regulatory networks. Furthermore, we lack an unbiased way of assessing whether inferred regulatory interactions are biologically meaningful. Here we present two methods, implemented as user-friendly R-packages, for building and evaluating enhancer-mediated gene regulatory networks (eGRNs) called GRaNIE (Gene Regulatory Network Inference including Enhancers - https://git.embl.de/grp-zaugg/GRaNIE) and GRaNPA (Gene Regulatory Network Performance Analysis - https://git.embl.de/grp-zaugg/GRaNPA), respectively. GRaNIE jointly infers TF-enhancer, enhancer-gene and TF-gene interactions by integrating open chromatin data such as ATAC-Seq or H3K27ac with RNA-seq across a set of samples (e.g. individuals), and optionally also Hi-C data. GRaNPA is a general framework for evaluating the biological relevance of TF-gene GRNs by assessing their performance for predicting cell-type specific differential expression. We demonstrate the power of our tool-suite by investigating gene regulatory mechanisms in macrophages that underlie their response to infection, and their involvement in common genetic diseases including autoimmune diseases.Among the biggest challenges in the post-GWAS (genome-wide association studies) era is the interpretation of disease-associated genetic variants in non-coding genomic regions. Enhancers have emerged as key players in mediating the effect of genetic variants on complex traits and diseases. Their activity is regulated by a combination of transcription factors (TFs), epigenetic changes and genetic variants. Several approaches exist to link enhancers to their target genes, and others that infer TF-gene connections. However, we currently lack a framework that systematically integrates enhancers into TF-gene regulatory networks. Furthermore, we lack an unbiased way of assessing whether inferred regulatory interactions are biologically meaningful. Here we present two methods, implemented as user-friendly R-packages, for building and evaluating enhancer-mediated gene regulatory networks (eGRNs) called GRaNIE (Gene Regulatory Network Inference including Enhancers - https://git.embl.de/grp-zaugg/GRaNIE) and GRaNPA (Gene Regulatory Network Performance Analysis - https://git.embl.de/grp-zaugg/GRaNPA), respectively. GRaNIE jointly infers TF-enhancer, enhancer-gene and TF-gene interactions by integrating open chromatin data such as ATAC-Seq or H3K27ac with RNA-seq across a set of samples (e.g. individuals), and optionally also Hi-C data. GRaNPA is a general framework for evaluating the biological relevance of TF-gene GRNs by assessing their performance for predicting cell-type specific differential expression. We demonstrate the power of our tool-suite by investigating gene regulatory mechanisms in macrophages that underlie their response to infection, and their involvement in common genetic diseases including autoimmune diseases.Among the biggest challenges in the post-GWAS (genome-wide association studies) era is the interpretation of disease-associated genetic variants in non-coding genomic regions. Enhancers have emerged as key players in mediating the effect of genetic variants on complex traits and diseases. Their activity is regulated by a combination of transcription factors (TFs), epigenetic changes and genetic variants. Several approaches exist to link enhancers to their target genes, and others that infer TF-gene connections. However, we currently lack a framework that systematically integrates enhancers into TF-gene regulatory networks. Furthermore, we lack an unbiased way of assessing whether inferred regulatory interactions are biologically meaningful. Here we present two methods, implemented as user-friendly R-packages, for building and evaluating enhancer-mediated gene regulatory networks (eGRNs) called GRaNIE (Gene Regulatory Network Inference including Enhancers - https://git.embl.de/grp-zaugg/GRaNIE) and GRaNPA (Gene Regulatory Network Performance Analysis - https://git.embl.de/grp-zaugg/GRaNPA), respectively. GRaNIE jointly infers TF-enhancer, enhancer-gene and TF-gene interactions by integrating open chromatin data such as ATAC-Seq or H3K27ac with RNA-seq across a set of samples (e.g. individuals), and optionally also Hi-C data. GRaNPA is a general framework for evaluating the biological relevance of TF-gene GRNs by assessing their performance for predicting cell-type specific differential expression. We demonstrate the power of our tool-suite by investigating gene regulatory mechanisms in macrophages that underlie their response to infection, and their involvement in common genetic diseases including autoimmune diseases.

scholarly journals Improving gene regulatory network structure using redundancy reduction in the MRNET algorithm

RSC Advances ◽  
2017 ◽  
Vol 7 (37) ◽  
pp. 23222-23233 ◽  
Author(s):  
Wei Liu ◽  
Wen Zhu ◽  
Bo Liao ◽  
Haowen Chen ◽  
Siqi Ren ◽  
...  
Keyword(s):  
Systems Biology ◽  
Gene Regulatory Network ◽  
Gene Regulatory Networks ◽  
Network Structure ◽  
Regulatory Network ◽  
Regulatory Networks ◽  
Central Problem ◽  
Expression Data ◽  
Redundancy Reduction ◽  
Gene Regulatory

Inferring gene regulatory networks from expression data is a central problem in systems biology.

scholarly journals D3GRN: a data driven dynamic network construction method to infer gene regulatory networks

BMC Genomics ◽  
2019 ◽  
Vol 20 (S13) ◽  
Author(s):  
Xiang Chen ◽  
Min Li ◽  
Ruiqing Zheng ◽  
Fang-Xiang Wu ◽  
Jianxin Wang
Keyword(s):  
Gene Regulatory Networks ◽  
Regulatory Networks ◽  
Dynamic Network ◽  
Construction Method ◽  
Data Driven ◽  
Regression Problem ◽  
Network Construction ◽  
Benchmark Datasets ◽  
Gene Regulatory ◽  
New Perspective

Abstract Background To infer gene regulatory networks (GRNs) from gene-expression data is still a fundamental and challenging problem in systems biology. Several existing algorithms formulate GRNs inference as a regression problem and obtain the network with an ensemble strategy. Recent studies on data driven dynamic network construction provide us a new perspective to solve the regression problem. Results In this study, we propose a data driven dynamic network construction method to infer gene regulatory network (D3GRN), which transforms the regulatory relationship of each target gene into functional decomposition problem and solves each sub problem by using the Algorithm for Revealing Network Interactions (ARNI). To remedy the limitation of ARNI in constructing networks solely from the unit level, a bootstrapping and area based scoring method is taken to infer the final network. On DREAM4 and DREAM5 benchmark datasets, D3GRN performs competitively with the state-of-the-art algorithms in terms of AUPR. Conclusions We have proposed a novel data driven dynamic network construction method by combining ARNI with bootstrapping and area based scoring strategy. The proposed method performs well on the benchmark datasets, contributing as a competitive method to infer gene regulatory networks in a new perspective.

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

2011 ◽  
Vol 28 (2) ◽  
pp. 214-221 ◽  
Author(s):  
Geert Geeven ◽  
Ronald E. van Kesteren ◽  
August B. Smit ◽  
Mathisca C. M. de Gunst
Keyword(s):  
Gene Expression ◽  
Gene Regulatory Networks ◽  
Regulatory Networks ◽  
Specific Gene ◽  
Gene Regulatory ◽  
Context Specific

scholarly journals Modelling Three Dimensional Gene Regulatory Networks

2021 ◽  
Vol 16 ◽  
pp. 755-763
Author(s):  
Inna Samuilik ◽  
Felix Sadyrbaev
Keyword(s):  
Differential Equations ◽  
Gene Regulatory Networks ◽  
Regulatory Network ◽  
Regulatory Networks ◽  
Linear Part ◽  
Three Dimensional ◽  
Special Kind ◽  
Sigmoidal Function ◽  
Solution Vector ◽  
Gene Regulatory

We consider the three-dimensional gene regulatory network (GRN in short). This model consists of ordinary differential equations of a special kind, where the nonlinearity is represented by a sigmoidal function and the linear part is present also. The evolution of GRN is described by the solution vector X(t), depending on time. We describe the changes that system undergoes if the entries of the regulatory matrix are perturbed in some way.

scholarly journals Predicting genotype-specific gene regulatory networks

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).

scholarly journals Neural Gene Network Constructor: A Neural Based Model for Reconstructing Gene Regulatory Network

2019 ◽  
Author(s):  
Zhang Zhang ◽  
Lifei Wang ◽  
Shuo Wang ◽  
Ruyi Tao ◽  
Jingshu Xiao ◽  
...  
Keyword(s):  
Gene Expression ◽  
Gene Regulatory Network ◽  
Gene Expression Data ◽  
Gene Regulatory Networks ◽  
Network Structure ◽  
Regulatory Network ◽  
Gene Network ◽  
Regulatory Networks ◽  
Expression Data ◽  
Gene Regulatory

SummaryReconstructing gene regulatory networks (GRNs) and inferring the gene dynamics are important to understand the behavior and the fate of the normal and abnormal cells. Gene regulatory networks could be reconstructed by experimental methods or from gene expression data. Recent advances in Single Cell RNA sequencing technology and the computational method to reconstruct trajectory have generated huge scRNA-seq data tagged with additional time labels. Here, we present a deep learning model “Neural Gene Network Constructor” (NGNC), for inferring gene regulatory network and reconstructing the gene dynamics simultaneously from time series gene expression data. NGNC is a model-free heterogenous model, which can reconstruct any network structure and non-linear dynamics. It consists of two parts: a network generator which incorporating gumbel softmax technique to generate candidate network structure, and a dynamics learner which adopting multiple feedforward neural networks to predict the dynamics. We compare our model with other well-known frameworks on the data set generated by GeneNetWeaver, and achieve the state of the arts results both on network reconstruction and dynamics learning.

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