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 Rapid Reconstruction of Time-varying Gene Regulatory Networks with Limited Main Memory

2019 ◽  
Author(s):  
Saptarshi Pyne ◽  
Ashish Anand
Keyword(s):  
Gene Regulatory Networks ◽  
Regulatory Networks ◽  
Main Memory ◽  
Time Varying ◽  
Computational Systems Biology ◽  
Benchmark Datasets ◽  
Gene Regulatory ◽  
Novel Algorithms ◽  
Efficient Memory ◽  
Computational Systems

AbstractReconstruction of time-varying gene regulatory networks underlying a time-series gene expression data is a fundamental challenge in the computational systems biology. The challenge increases multi-fold if the target networks need to be constructed for hundreds to thousands of genes. There have been constant efforts to design an algorithm that can perform the reconstruction task correctly as well as can scale efficiently (with respect to both time and memory) to such a large number of genes. However, the existing algorithms either do not offer time-efficiency, or they offer it at other costs – memory-inefficiency or imposition of a constraint, known as the ‘smoothly time-varying assumption’. In this paper, two novel algorithms – ‘an algorithm for reconstructing Time-varying Gene regulatory networks with Shortlisted candidate regulators - which is Light on memory’ (TGS-Lite) and ‘TGS-Lite Plus’ (TGS-Lite+) – are proposed that are time-efficient, memory-efficient and do not impose the smoothly time-varying assumption. Additionally, they offer state-of-the-art reconstruction correctness as demonstrated with three benchmark datasets.Source Codehttps://github.com/sap01/TGS-Lite-supplem/tree/master/sourcecode

Data-driven Gene Regulatory Networks Inference Based on Classification Algorithms

2021 ◽  
Vol 30 (04) ◽  
pp. 2150022
Author(s):  
Sergio Peignier ◽  
Pauline Schmitt ◽  
Federica Calevro
Keyword(s):  
Gene Expression ◽  
Gene Regulatory Networks ◽  
Regulatory Networks ◽  
Target Genes ◽  
Data Driven ◽  
Classification Algorithms ◽  
New Family ◽  
Gene Regulatory ◽  
High Throughput Gene Expression ◽  
Inference Methods

Inferring Gene Regulatory Networks from high-throughput gene expression data is a challenging problem, addressed by the systems biology community. Most approaches that aim at unraveling the gene regulation mechanisms in a data-driven way, analyze gene expression datasets to score potential regulatory links between transcription factors and target genes. So far, three major families of approaches have been proposed to score regulatory links. These methods rely respectively on correlation measures, mutual information metrics, and regression algorithms. In this paper we present a new family of data-driven inference methods. This new family, inspired by the regression-based paradigm, relies on the use of classification algorithms. This paper assesses and advocates for the use of this paradigm as a new promising approach to infer gene regulatory networks. Indeed, the development and assessment of five new inference methods based on well-known classification algorithms shows that the classification-based inference family exhibits good results when compared to well-established paradigms.

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 Construction Method of Probabilistic Boolean Networks Based on Imperfect Information

Algorithms ◽  
2019 ◽  
Vol 12 (12) ◽  
pp. 268
Author(s):  
Katsuaki Umiji ◽  
Koichi Kobayashi ◽  
Yuh Yamashita
Keyword(s):  
Gene Regulatory Networks ◽  
Boolean Functions ◽  
Regulatory Networks ◽  
Imperfect Information ◽  
Boolean Network ◽  
Construction Method ◽  
Boolean Networks ◽  
Probabilistic Boolean Network ◽  
Gene Regulatory ◽  
Selection Probabilities

A probabilistic Boolean network (PBN) is well known as one of the mathematical models of gene regulatory networks. In a Boolean network, expression of a gene is approximated by a binary value, and its time evolution is expressed by Boolean functions. In a PBN, a Boolean function is probabilistically chosen from candidates of Boolean functions. One of the authors has proposed a method to construct a PBN from imperfect information. However, there is a weakness that the number of candidates of Boolean functions may be redundant. In this paper, this construction method is improved to efficiently utilize given information. To derive Boolean functions and those selection probabilities, the linear programming problem is solved. Here, we introduce the objective function to reduce the number of candidates. The proposed method is demonstrated by a numerical example.

scholarly journals scMomentum: Inference of Cell-Type-Specific Regulatory Networks and Energy Landscapes

2020 ◽  
Author(s):  
Larisa M. Soto ◽  
Juan P. Bernal-Tamayo ◽  
Robert Lehmann ◽  
Subash Balsamy ◽  
Xabier Martinez-de-Morentin ◽  
...  
Keyword(s):  
Single Cell ◽  
Gene Regulatory Networks ◽  
Regulatory Networks ◽  
Data Driven ◽  
Energy Landscapes ◽  
Cell Type ◽  
Cell Clustering ◽  
Cell Type Specific ◽  
Gene Regulatory ◽  
Python Package

AbstractRecent progress in single-cell genomics has generated multiple tools for cell clustering, annotation, and trajectory inference; yet, inferring their associated regulatory mechanisms is unresolved. Here we present scMomentum, a model-based data-driven formulation to predict gene regulatory networks and energy landscapes from single-cell transcriptomic data without requiring temporal or perturbation experiments. scMomentum provides significant advantages over existing methods with respect to computational efficiency, scalability, network structure, and biological application.AvailabilityscMomentum is available as a Python package at https://github.com/larisa-msoto/scMomentum.git

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