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

scholarly journals Rapid Reconstruction of Time-varying Gene Regulatory Networks

2018 ◽  
Author(s):  
Saptarshi Pyne ◽  
Alok Ranjan Kumar ◽  
Ashish Anand
Keyword(s):  
Gene Regulatory Networks ◽  
False Positive ◽  
Regulatory Networks ◽  
Source Code ◽  
Main Memory ◽  
Structural Constraints ◽  
Time Varying ◽  
True Positive ◽  
Supplementary Document ◽  
Gene Regulatory

Abstract—Rapid advancements in high-throughput technologies has resulted in genome-scale time series datasets. Uncovering the temporal sequence of gene regulatory events, in the form of time-varying gene regulatory networks (GRNs), demands computationally fast, accurate and scalable algorithms. The existing algorithms can be divided into two categories: ones that are time-intensive and hence unscalable; others that impose structural constraints to become scalable. In this paper, a novel algorithm, namely ‘an algorithm for reconstructing Time-varying Gene regulatory networks with Shortlisted candidate regulators’ (TGS), is proposed. TGS is time-efficient and does not impose any structural constraints. Moreover, it provides such flexibility and time-efficiency, without losing its accuracy. TGS consistently outperforms the state-of-the-art algorithms in true positive detection, on three benchmark synthetic datasets. However, TGS does not perform as well in false positive rejection. To mitigate this issue, TGS+ is proposed. TGS+ demonstrates competitive false positive rejection power, while maintaining the superior speed and true positive detection power of TGS. Nevertheless, main memory requirements of both TGS variants grow exponentially with the number of genes, which they tackle by restricting the maximum number of regulators for each gene. Relaxing this restriction remains a challenge as the actual number of regulators is not known a priori.ReproducibilityThe datasets and results can be found at: https://github.com/aaiitg-grp/TGS. This manuscript is currently under review. As soon as it is accepted, the source code will be made available at the same link. There are mentions of a ‘supplementary document’ throughout the text. The supplementary document will also be made available after acceptance of the manuscript. If you wish to be notified when the supplementary document and source code are available, kindly send an email to [email protected] with subject line ‘TGS Source Code: Request for Notification’. The email body can be kept blank.

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.

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