Inferring and analyzing gene regulatory networks from multi-factorial expression data: a complete and interactive suite

Abstract Background High-throughput transcriptomic datasets are often examined to discover new actors and regulators of a biological response. To this end, graphical interfaces have been developed and allow a broad range of users to conduct standard analyses from RNA-seq data, even with little programming experience. Although existing solutions usually provide adequate procedures for normalization, exploration or differential expression, more advanced features, such as gene clustering or regulatory network inference, often miss or do not reflect current state of the art methodologies. Results We developed here a user interface called DIANE (Dashboard for the Inference and Analysis of Networks from Expression data) designed to harness the potential of multi-factorial expression datasets from any organisms through a precise set of methods. DIANE interactive workflow provides normalization, dimensionality reduction, differential expression and ontology enrichment. Gene clustering can be performed and explored via configurable Mixture Models, and Random Forests are used to infer gene regulatory networks. DIANE also includes a novel procedure to assess the statistical significance of regulator-target influence measures based on permutations for Random Forest importance metrics. All along the pipeline, session reports and results can be downloaded to ensure clear and reproducible analyses. Conclusions We demonstrate the value and the benefits of DIANE using a recently published data set describing the transcriptional response of Arabidopsis thaliana under the combination of temperature, drought and salinity perturbations. We show that DIANE can intuitively carry out informative exploration and statistical procedures with RNA-Seq data, perform model based gene expression profiles clustering and go further into gene network reconstruction, providing relevant candidate genes or signalling pathways to explore. DIANE is available as a web service (https://diane.bpmp.inrae.fr), or can be installed and locally launched as a complete R package.

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EnGRNT: Inference of gene regulatory networks using ensemble methods and topological feature extraction

10.1101/2021.08.05.455202 ◽

2021 ◽

Author(s):

Hakimeh Khojasteh ◽

Mohammad Hossein Olyaee ◽

Alireza Khanteymoori

Keyword(s):

Gene Expression ◽

Gene Expression Data ◽

Gene Regulatory Networks ◽

Regulatory Networks ◽

Expression Profiles ◽

Expression Data ◽

Class Imbalance Problem ◽

Experimental Conditions ◽

Topological Features ◽

Gene Regulatory

The development of computational methods to predict gene regulatory networks (GRNs) from gene expression data is a challenging task. Many machine learning methods have been developed, including supervised, unsupervised, and semi-supervised to infer gene regulatory networks. Most of these methods ignore the class imbalance problem which can lead to decreasing the accuracy of predicting regulatory interactions in the network. Therefore, developing an effective method considering imbalanced data is a challenging task. In this paper, we propose EnGRNT approach to infer GRNs with high accuracy that uses ensemble-based methods. The proposed approach, as well as the gene expression data, considers the topological features of GRN. We applied our approach to the simulated Escherichia coli dataset. Experimental results demonstrate that the appropriateness of the inference method relies on the size and type of expression profiles in microarray data. Except for multifactorial experimental conditions, the proposed approach outperforms unsupervised methods. The obtained results recommend the application of EnGRNT on the imbalanced datasets.

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TuxNet: A simple interface to process RNA sequencing data and infer gene regulatory networks

10.1101/337147 ◽

2018 ◽

Cited By ~ 1

Author(s):

Maria Angels de Luis Balaguer ◽

Ryan J. Spurney ◽

Natalie M. Clark ◽

Adam P. Fisher ◽

Rosangela Sozzani

Keyword(s):

Gene Expression ◽

Rna Sequencing ◽

Gene Regulatory Networks ◽

Regulatory Networks ◽

Time Course ◽

Expression Profiles ◽

Expression Data ◽

Sequencing Data ◽

Simple Interface ◽

Gene Regulatory

ABSTRACTPredicting gene regulatory networks (GRNs) from gene expression profiles has become a common approach for identifying important biological regulators. Despite the increase in the use of inference methods, existing computational approaches do not integrate RNA-sequencing data analysis, are often not automated, and are restricted to users with bioinformatics and programming backgrounds. To address these limitations, we have developed TuxNet, an integrated user-friendly platform, which, with just a few selections, allows to process raw RNA-sequencing data (using the Tuxedo pipeline) and infer GRNs from these processed data. TuxNet is implemented as a graphical user interface and, using expression data from any organism with an existing reference genome, can mine the regulations among genes either by applying a dynamic Bayesian network inference algorithm, GENIST, or a regression tree-based pipeline that uses spatiotemporal data, RTP-STAR. To illustrate the use of TuxNet while getting insight into the regulatory cascade downstream of the Arabidopsis root stem cell regulator PERIANTHIA (PAN), we obtained time course gene expression data of a PAN inducible line and inferred a GRN using GENIST. Using RTP-STAR, we then inferred the network of a PAN secondary downstream gene, ATHB13, for which we obtained wildtype and mutant expression profiles. Our case studies feature the versatility of TuxNet to infer networks using different types of gene expression data (i.e time course and steady-state data) as well as how inference networks are used to identify important regulators.SUMMARYTuxNet offers a simple interface for non-computational biologists to infer GRNs from raw RNA-seq data.

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A statistical method for measuring activation of gene regulatory networks

Statistical Applications in Genetics and Molecular Biology ◽

10.1515/sagmb-2016-0059 ◽

2018 ◽

Vol 17 (3) ◽

Author(s):

Gustavo H. Esteves ◽

Luiz F. L. Reis

Keyword(s):

Gene Expression ◽

Gene Expression Data ◽

Gene Regulatory Networks ◽

Regulatory Networks ◽

Expression Profiles ◽

Expression Data ◽

Test Statistic ◽

Network Activation ◽

Bioconductor Project ◽

Gene Regulatory

Abstract Motivation: Gene expression data analysis is of great importance for modern molecular biology, given our ability to measure the expression profiles of thousands of genes and enabling studies rooted in systems biology. In this work, we propose a simple statistical model for the activation measuring of gene regulatory networks, instead of the traditional gene co-expression networks. Results: We present the mathematical construction of a statistical procedure for testing hypothesis regarding gene regulatory network activation. The real probability distribution for the test statistic is evaluated by a permutation based study. To illustrate the functionality of the proposed methodology, we also present a simple example based on a small hypothetical network and the activation measuring of two KEGG networks, both based on gene expression data collected from gastric and esophageal samples. The two KEGG networks were also analyzed for a public database, available through NCBI-GEO, presented as Supplementary Material. Availability: This method was implemented in an R package that is available at the BioConductor project website under the name maigesPack.

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SINCERITIES: Inferring gene regulatory networks from time-stamped single cell transcriptional expression profiles

10.1101/089110 ◽

2016 ◽

Cited By ~ 3

Author(s):

Nan Papili Gao ◽

S.M. Minhaz Ud-Dean ◽

Rudiyanto Gunawan

Keyword(s):

Cell Differentiation ◽

Single Cell ◽

Gene Regulatory Networks ◽

Regulatory Networks ◽

Expression Profiles ◽

Transcriptional Profiling ◽

Stem Cell Differentiation ◽

Expression Data ◽

Transcriptional Expression ◽

Gene Regulatory

AbstractRecent advances in single cell transcriptional profiling open up a new avenue in studying the functional role of cell-to-cell variability in physiological processes such as stem cell differentiation. In this work, we developed a novel algorithm called SINCERITIES (SINgle CEll Regularized Inference using TIme-stamped Expression profileS), for the inference of gene regulatory networks (GRNs) from single cell transcriptional expression data. In particular, we focused on time-stamped cross-sectional expression data, a common type of dataset generated from transcriptional profiling of single cells collected at multiple time points after cell stimulation. SINCERITIES recovers the regulatory (causal) relationships among genes by employing regularized linear regression, particularly ridge regression, using temporal changes in the distributions of gene expressions. Meanwhile, the modes of the gene regulations (activation and repression) come from partial correlation analyses between pairs of genes. We demonstrated the efficacy of SINCERITIES in inferring GRNs using simulated time-stamped in silico single cell expression data and single transcriptional profiling of THP-1 monocytic human leukemia cell differentiation. The case studies showed that SINCERITIES could provide accurate GRN predictions, significantly better than other GRN inference algorithms such as TSNI, GENIE3 and JUMP3. Meanwhile, SINCERITIES has a low computational complexity and is amenable to problems of extremely large dimensionality.

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