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

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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PAGeneRN

Data Analytics in Medicine ◽

10.4018/978-1-7998-1204-3.ch055 ◽

2020 ◽

pp. 1052-1075 ◽

Cited By ~ 1

Author(s):

Dina Elsayad ◽

A. Ali ◽

Howida A. Shedeed ◽

Mohamed F. Tolba

Keyword(s):

Gene Expression ◽

Data Analysis ◽

Network Analysis ◽

Gene Regulatory Network ◽

Gene Expression Data ◽

Regulatory Network ◽

Regulatory Networks ◽

Expression Data ◽

Gene Expression Data Analysis ◽

Gene Regulatory

The gene expression analysis is an important research area of Bioinformatics. The gene expression data analysis aims to understand the genes interacting phenomena, gene functionality and the genes mutations effect. The Gene regulatory network analysis is one of the gene expression data analysis tasks. Gene regulatory network aims to study the genes interactions topological organization. The regulatory network is critical for understanding the pathological phenotypes and the normal cell physiology. There are many researches that focus on gene regulatory network analysis but unfortunately some algorithms are affected by data size. Where, the algorithm runtime is proportional to the data size, therefore, some parallel algorithms are presented to enhance the algorithms runtime and efficiency. This work presents a background, mathematical models and comparisons about gene regulatory networks analysis different techniques. In addition, this work proposes Parallel Architecture for Gene Regulatory Network (PAGeneRN).

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Gene regulatory networks and developmental plasticity in the early sea urchin embryo: alternative deployment of the skeletogenic gene regulatory network

Development ◽

10.1242/dev.009092 ◽

2007 ◽

Vol 134 (17) ◽

pp. 3077-3087 ◽

Cited By ~ 35

Author(s):

C. A. Ettensohn ◽

C. Kitazawa ◽

M. S. Cheers ◽

J. D. Leonard ◽

T. Sharma

Keyword(s):

Gene Regulatory Network ◽

Sea Urchin ◽

Gene Regulatory Networks ◽

Regulatory Network ◽

Regulatory Networks ◽

Developmental Plasticity ◽

Sea Urchin Embryo ◽

Gene Regulatory

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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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Chip-seq and gene expression data for the identification of functional sub-pathways: a proof of concept in lung cancer

10.1101/2020.06.15.151712 ◽

2020 ◽

Author(s):

Xanthoula Atsalaki ◽

Lefteris Koumakis ◽

George Potamias ◽

Manolis Tsiknakis

Keyword(s):

Gene Expression ◽

Lung Cancer ◽

Systems Biology ◽

Gene Expression Data ◽

Gene Regulatory Networks ◽

Regulatory Networks ◽

Molecular Mechanisms ◽

Integrative Approach ◽

Expression Data ◽

Gene Regulatory

AbstractHigh-throughput technologies, such as chromatin immunoprecipitation (ChIP) with massively parallel sequencing (ChIP-seq) have enabled cost and time efficient generation of immense amount of genome data. The advent of advanced sequencing techniques allowed biologists and bioinformaticians to investigate biological aspects of cell function and understand or reveal unexplored disease etiologies. Systems biology attempts to formulate the molecular mechanisms in mathematical models and one of the most important areas is the gene regulatory networks (GRNs), a collection of DNA segments that somehow interact with each other. GRNs incorporate valuable information about molecular targets that can be corellated to specific phenotype.In our study we highlight the need to develop new explorative tools and approaches for the integration of different types of -omics data such as ChIP-seq and GRNs using pathway analysis methodologies. We present an integrative approach for ChIP-seq and gene expression data on GRNs. Using public microarray expression samples for lung cancer and healthy subjects along with the KEGG human gene regulatory networks, we identified ways to disrupt functional sub-pathways on lung cancer with the aid of CTCF ChIP-seq data, as a proof of concept.We expect that such a systems biology pipeline could assist researchers to identify corellations and causality of transcription factors over functional or disrupted biological sub-pathways.

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A novel method of gene regulatory network structure inference from gene knock-out expression data

Tsinghua Science & Technology ◽

10.26599/tst.2018.9010097 ◽

2019 ◽

Vol 24 (4) ◽

pp. 446-455 ◽

Cited By ~ 6

Author(s):

Xiang Chen ◽

Min Li ◽

Ruiqing Zheng ◽

Siyu Zhao ◽

Fang-Xiang Wu ◽

...

Keyword(s):

Gene Regulatory Network ◽

Network Structure ◽

Regulatory Network ◽

Expression Data ◽

Knock Out ◽

Novel Method ◽

Gene Regulatory

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EVIDENCE OF SCALE-FREE TOPOLOGY IN GENE REGULATORY NETWORK OF HUMAN TISSUES

International Journal of Modern Physics C ◽

10.1142/s0129183108012091 ◽

2008 ◽

Vol 19 (02) ◽

pp. 283-290 ◽

Cited By ~ 2

Author(s):

M. ANDRECUT ◽

S. A. KAUFFMAN ◽

A. M. MADNI

Keyword(s):

Gene Regulatory Network ◽

Gene Regulatory Networks ◽

Regulatory Network ◽

Gene Network ◽

Regulatory Networks ◽

Regulatory Gene ◽

Human Tissues ◽

Scale Free ◽

The Common ◽

Gene Regulatory

We report the reconstruction of the topology of gene regulatory network in human tissues. The results show that the connectivity of the regulatory gene network is characterized by a scale-free distribution. This result supports the hypothesis that scale-free networks may represent the common blueprint for gene regulatory networks.

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Gene regulatory network reconstruction using single-cell RNA sequencing of barcoded genotypes in diverse environments

eLife ◽

10.7554/elife.51254 ◽

2020 ◽

Vol 9 ◽

Cited By ~ 16

Author(s):

Christopher A Jackson ◽

Dayanne M Castro ◽

Giuseppe-Antonio Saldi ◽

Richard Bonneau ◽

David Gresham

Keyword(s):

Gene Expression ◽

Single Cell ◽

Rna Sequencing ◽

Gene Regulatory Network ◽

Gene Regulatory Networks ◽

Regulatory Network ◽

Regulatory Networks ◽

Target Genes ◽

Single Cell Rna Sequencing ◽

Gene Regulatory

Understanding how gene expression programs are controlled requires identifying regulatory relationships between transcription factors and target genes. Gene regulatory networks are typically constructed from gene expression data acquired following genetic perturbation or environmental stimulus. Single-cell RNA sequencing (scRNAseq) captures the gene expression state of thousands of individual cells in a single experiment, offering advantages in combinatorial experimental design, large numbers of independent measurements, and accessing the interaction between the cell cycle and environmental responses that is hidden by population-level analysis of gene expression. To leverage these advantages, we developed a method for scRNAseq in budding yeast (Saccharomyces cerevisiae). We pooled diverse transcriptionally barcoded gene deletion mutants in 11 different environmental conditions and determined their expression state by sequencing 38,285 individual cells. We benchmarked a framework for learning gene regulatory networks from scRNAseq data that incorporates multitask learning and constructed a global gene regulatory network comprising 12,228 interactions.

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An Integrative Developmental Genomics and Systems Biology Approach to Identify an In Vivo Sox Trio-Mediated Gene Regulatory Network in Murine Embryos

BioMed Research International ◽

10.1155/2017/8932583 ◽

2017 ◽

Vol 2017 ◽

pp. 1-16 ◽

Cited By ~ 3

Author(s):

Wenqing Jean Lee ◽

Sumantra Chatterjee ◽

Sook Peng Yap ◽

Siew Lan Lim ◽

Xing Xing ◽

...

Keyword(s):

Systems Biology ◽

Gene Regulatory Network ◽

Regulatory Network ◽

Regulatory Networks ◽

Cell Types ◽

Specific Cell ◽

Morpholino Knockdown ◽

Cell Type Specific ◽

Gene Regulatory

Embryogenesis is an intricate process involving multiple genes and pathways. Some of the key transcription factors controlling specific cell types are the Sox trio, namely, Sox5, Sox6, and Sox9, which play crucial roles in organogenesis working in a concerted manner. Much however still needs to be learned about their combinatorial roles during this process. A developmental genomics and systems biology approach offers to complement the reductionist methodology of current developmental biology and provide a more comprehensive and integrated view of the interrelationships of complex regulatory networks that occur during organogenesis. By combining cell type-specific transcriptome analysis and in vivo ChIP-Seq of the Sox trio using mouse embryos, we provide evidence for the direct control of Sox5 and Sox6 by the transcriptional trio in the murine model and by Morpholino knockdown in zebrafish and demonstrate the novel role of Tgfb2, Fbxl18, and Tle3 in formation of Sox5, Sox6, and Sox9 dependent tissues. Concurrently, a complete embryonic gene regulatory network has been generated, identifying a wide repertoire of genes involved and controlled by the Sox trio in the intricate process of normal embryogenesis.

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Exact Reconstruction of Gene Regulatory Networks using Compressive Sensing

10.1101/004242 ◽

2014 ◽

Author(s):

Young Hwan Chang ◽

Joe W. Gray ◽

Claire J. Tomlin

Keyword(s):

Compressive Sensing ◽

Gene Regulatory Networks ◽

Regulatory Network ◽

Regulatory Networks ◽

A Priori ◽

Graph Structure ◽

Expression Data ◽

Exact Reconstruction ◽

Time Series Gene Expression ◽

Gene Regulatory

Background: We consider the problem of reconstructing a gene regulatory network structure from limited time series gene expression data, without any a priori knowledge of connectivity. We assume that the network is sparse, meaning the connectivity among genes is much less than full connectivity. We develop a method for network reconstruction based on compressive sensing, which takes advantage of the network's sparseness. Results: For the case in which all genes are accessible for measurement, and there is no measurement noise, we show that our method can be used to exactly reconstruct the network. For the more general problem, in which hidden genes exist and all measurements are contaminated by noise, we show that our method leads to reliable reconstruction. In both cases, coherence of the model is used to assess the ability to reconstruct the network and to design new experiments. For each problem, a set of numerical examples is presented. Conclusions: The method provides a guarantee on how well the inferred graph structure represents the underlying system, reveals deficiencies in the data and model, and suggests experimental directions to remedy the deficiencies.

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