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Huang Y, Huang S, Zhang XF, Ou-Yang L, Liu C. NJGCG: A node-based joint Gaussian copula graphical model for gene networks inference across multiple states. Comput Struct Biotechnol J 2024; 23:3199-3210. [PMID: 39263209 PMCID: PMC11388165 DOI: 10.1016/j.csbj.2024.08.010] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/14/2024] [Revised: 08/05/2024] [Accepted: 08/11/2024] [Indexed: 09/13/2024] Open
Abstract
Inferring the interactions between genes is essential for understanding the mechanisms underlying biological processes. Gene networks will change along with the change of environment and state. The accumulation of gene expression data from multiple states makes it possible to estimate the gene networks in various states based on computational methods. However, most existing gene network inference methods focus on estimating a gene network from a single state, ignoring the similarities between networks in different but related states. Moreover, in addition to individual edges, similarities and differences between different networks may also be driven by hub genes. But existing network inference methods rarely consider hub genes, which affects the accuracy of network estimation. In this paper, we propose a novel node-based joint Gaussian copula graphical (NJGCG) model to infer multiple gene networks from gene expression data containing heterogeneous samples jointly. Our model can handle various gene expression data with missing values. Furthermore, a tree-structured group lasso penalty is designed to identify the common and specific hub genes in different gene networks. Simulation studies show that our proposed method outperforms other compared methods in all cases. We also apply NJGCG to infer the gene networks for different stages of differentiation in mouse embryonic stem cells and different subtypes of breast cancer, and explore changes in gene networks across different stages of differentiation or different subtypes of breast cancer. The common and specific hub genes in the estimated gene networks are closely related to stem cell differentiation processes and heterogeneity within breast cancers.
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Affiliation(s)
- Yun Huang
- Department of Geriatrics, The First Affiliated Hospital of Fujian Medical University, Fuzhou 350005, China
- Clinical Research Center for Geriatric Hypertension Disease of Fujian province, The First Affiliated Hospital of Fujian Medical University, Fuzhou 350005, China
| | - Sen Huang
- Guangdong Key Laboratory of Intelligent Information Processing, College of Electronics and Information Engineering, Shenzhen University, Shenzhen, China
| | - Xiao-Fei Zhang
- School of Mathematics and Statistics & Hubei Key Laboratory of Mathematical Sciences, Central China Normal University, Wuhan, China
| | - Le Ou-Yang
- Guangdong Key Laboratory of Intelligent Information Processing, College of Electronics and Information Engineering, Shenzhen University, Shenzhen, China
| | - Chen Liu
- Department of Oncology, Molecular Oncology Research Institute, The First Affiliated Hospital of Fujian Medical University, Fuzhou 350005, China
- Department of Oncology, National Regional Medical Center, Binhai Campus of The First Affiliated Hospital, Fujian Medical University, Fuzhou 350212, China
- Fujian Key Laboratory of Precision Medicine for Cancer, The First Affiliated Hospital of Fujian Medical University, Fuzhou 350005, China
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Segura-Ortiz A, García-Nieto J, Aldana-Montes JF, Navas-Delgado I. Multi-objective context-guided consensus of a massive array of techniques for the inference of Gene Regulatory Networks. Comput Biol Med 2024; 179:108850. [PMID: 39013340 DOI: 10.1016/j.compbiomed.2024.108850] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/08/2024] [Revised: 07/03/2024] [Accepted: 07/03/2024] [Indexed: 07/18/2024]
Abstract
BACKGROUND AND OBJECTIVE Gene Regulatory Network (GRN) inference is a fundamental task in biology and medicine, as it enables a deeper understanding of the intricate mechanisms of gene expression present in organisms. This bioinformatics problem has been addressed in the literature through multiple computational approaches. Techniques developed for inferring from expression data have employed Bayesian networks, ordinary differential equations (ODEs), machine learning, information theory measures and neural networks, among others. The diversity of implementations and their respective customization have led to the emergence of many tools and multiple specialized domains derived from them, understood as subsets of networks with specific characteristics that are challenging to detect a priori. This specialization has introduced significant uncertainty when choosing the most appropriate technique for a particular dataset. This proposal, named MO-GENECI, builds upon the basic idea of the previous proposal GENECI and optimizes consensus among different inference techniques, through a carefully refined multi-objective evolutionary algorithm guided by various objective functions, linked to the biological context at hand. METHODS MO-GENECI has been tested on an extensive and diverse academic benchmark of 106 gene regulatory networks from multiple sources and sizes. The evaluation of MO-GENECI compared its performance to individual techniques using key metrics (AUROC and AUPR) for gene regulatory network inference. Friedman's statistical ranking provided an ordered classification, followed by non-parametric Holm tests to determine statistical significance. RESULTS MO-GENECI's Pareto front approximation facilitates easy selection of an appropriate solution based on generic input data characteristics. The best solution consistently emerged as the winner in all statistical tests, and in many cases, the median precision solution showed no statistically significant difference compared to the winner. CONCLUSIONS MO-GENECI has not only demonstrated achieving more accurate results than individual techniques, but has also overcome the uncertainty associated with the initial choice due to its flexibility and adaptability. It is shown intelligently to select the most suitable techniques for each case. The source code is hosted in a public repository at GitHub under MIT license: https://github.com/AdrianSeguraOrtiz/MO-GENECI. Moreover, to facilitate its installation and use, the software associated with this implementation has been encapsulated in a Python package available at PyPI: https://pypi.org/project/geneci/.
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Affiliation(s)
- Adrián Segura-Ortiz
- Department de Lenguajes y Ciencias de la Computación, ITIS Software, Universidad de Málaga, Málaga, 29071, Spain.
| | - José García-Nieto
- Department de Lenguajes y Ciencias de la Computación, ITIS Software, Universidad de Málaga, Málaga, 29071, Spain; Biomedical Research Institute of Málaga (IBIMA), Universidad de Málaga, Málaga, Spain
| | - José F Aldana-Montes
- Department de Lenguajes y Ciencias de la Computación, ITIS Software, Universidad de Málaga, Málaga, 29071, Spain; Biomedical Research Institute of Málaga (IBIMA), Universidad de Málaga, Málaga, Spain
| | - Ismael Navas-Delgado
- Department de Lenguajes y Ciencias de la Computación, ITIS Software, Universidad de Málaga, Málaga, 29071, Spain; Biomedical Research Institute of Málaga (IBIMA), Universidad de Málaga, Málaga, Spain
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Wei PJ, Bao JJ, Gao Z, Tan JY, Cao RF, Su Y, Zheng CH, Deng L. MEFFGRN: Matrix enhancement and feature fusion-based method for reconstructing the gene regulatory network of epithelioma papulosum cyprini cells by spring viremia of carp virus infection. Comput Biol Med 2024; 179:108835. [PMID: 38996550 DOI: 10.1016/j.compbiomed.2024.108835] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/01/2024] [Revised: 06/05/2024] [Accepted: 06/29/2024] [Indexed: 07/14/2024]
Abstract
Gene regulatory networks (GRNs) are crucial for understanding organismal molecular mechanisms and processes. Construction of GRN in the epithelioma papulosum cyprini (EPC) cells of cyprinid fish by spring viremia of carp virus (SVCV) infection helps understand the immune regulatory mechanisms that enhance the survival capabilities of cyprinid fish. Although many computational methods have been used to infer GRNs, specialized approaches for predicting the GRN of EPC cells following SVCV infection are lacking. In addition, most existing methods focus primarily on gene expression features, neglecting the valuable network structural information in known GRNs. In this study, we propose a novel supervised deep neural network, named MEFFGRN (Matrix Enhancement- and Feature Fusion-based method for Gene Regulatory Network inference), to accurately predict the GRN of EPC cells following SVCV infection. MEFFGRN considers both gene expression data and network structure information of known GRN and introduces a matrix enhancement method to address the sparsity issue of known GRN, extracting richer network structure information. To optimize the benefits of CNN (Convolutional Neural Network) in image processing, gene expression and enhanced GRN data were transformed into histogram images for each gene pair respectively. Subsequently, these histograms were separately fed into CNNs for training to obtain the corresponding gene expression and network structural features. Furthermore, a feature fusion mechanism was introduced to comprehensively integrate the gene expression and network structural features. This integration considers the specificity of each feature and their interactive information, resulting in a more comprehensive and precise feature representation during the fusion process. Experimental results from both real-world and benchmark datasets demonstrate that MEFFGRN achieves competitive performance compared with state-of-the-art computational methods. Furthermore, study findings from SVCV-infected EPC cells suggest that MEFFGRN can predict novel gene regulatory relationships.
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Affiliation(s)
- Pi-Jing Wei
- Key Laboratory of Intelligent Computing & Signal Processing of Ministry of Education, Institutes of Physical Science and Information Technology, Anhui University, 111 Jiulong Road, Hefei, 230601, Anhui, China
| | - Jin-Jin Bao
- Key Laboratory of Intelligent Computing & Signal Processing of Ministry of Education, School of Computer Science and Technology, Anhui University, 111 Jiulong Road, Hefei, 230601, Anhui, China
| | - Zhen Gao
- Key Laboratory of Intelligent Computing & Signal Processing of Ministry of Education, School of Computer Science and Technology, Anhui University, 111 Jiulong Road, Hefei, 230601, Anhui, China
| | - Jing-Yun Tan
- Shenzhen Key Laboratory of Microbial Genetic Engineering, College of Life Sciences and Oceanology, Shenzhen University, Shenzhen, 518055, Guangdong, China
| | - Rui-Fen Cao
- Key Laboratory of Intelligent Computing & Signal Processing of Ministry of Education, School of Computer Science and Technology, Anhui University, 111 Jiulong Road, Hefei, 230601, Anhui, China
| | - Yansen Su
- School of Artificial Intelligence, Anhui University, 111 Jiulong Road, Hefei, 230601, Anhui, China
| | - Chun-Hou Zheng
- School of Artificial Intelligence, Anhui University, 111 Jiulong Road, Hefei, 230601, Anhui, China.
| | - Li Deng
- Shenzhen Key Laboratory of Microbial Genetic Engineering, College of Life Sciences and Oceanology, Shenzhen University, Shenzhen, 518055, Guangdong, China.
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Peng H, Xu J, Liu K, Liu F, Zhang A, Zhang X. EIEPCF: accurate inference of functional gene regulatory networks by eliminating indirect effects from confounding factors. Brief Funct Genomics 2024; 23:373-383. [PMID: 37642217 DOI: 10.1093/bfgp/elad040] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/05/2023] [Revised: 07/07/2023] [Accepted: 08/14/2023] [Indexed: 08/31/2023] Open
Abstract
Reconstructing functional gene regulatory networks (GRNs) is a primary prerequisite for understanding pathogenic mechanisms and curing diseases in animals, and it also provides an important foundation for cultivating vegetable and fruit varieties that are resistant to diseases and corrosion in plants. Many computational methods have been developed to infer GRNs, but most of the regulatory relationships between genes obtained by these methods are biased. Eliminating indirect effects in GRNs remains a significant challenge for researchers. In this work, we propose a novel approach for inferring functional GRNs, named EIEPCF (eliminating indirect effects produced by confounding factors), which eliminates indirect effects caused by confounding factors. This method eliminates the influence of confounding factors on regulatory factors and target genes by measuring the similarity between their residuals. The validation results of the EIEPCF method on simulation studies, the gold-standard networks provided by the DREAM3 Challenge and the real gene networks of Escherichia coli demonstrate that it achieves significantly higher accuracy compared to other popular computational methods for inferring GRNs. As a case study, we utilized the EIEPCF method to reconstruct the cold-resistant specific GRN from gene expression data of cold-resistant in Arabidopsis thaliana. The source code and data are available at https://github.com/zhanglab-wbgcas/EIEPCF.
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Affiliation(s)
- Huixiang Peng
- Key Laboratory of Plant Germplasm Enhancement and Specialty Agriculture, Wuhan Botanical Garden, Chinese Academy of Sciences, Wuhan 430074 China
- University of Chinese Academy of Sciences, Beijing 100049 China
| | - Jing Xu
- Key Laboratory of Plant Germplasm Enhancement and Specialty Agriculture, Wuhan Botanical Garden, Chinese Academy of Sciences, Wuhan 430074 China
- University of Chinese Academy of Sciences, Beijing 100049 China
| | - Kangchen Liu
- Key Laboratory of Plant Germplasm Enhancement and Specialty Agriculture, Wuhan Botanical Garden, Chinese Academy of Sciences, Wuhan 430074 China
- University of Chinese Academy of Sciences, Beijing 100049 China
| | - Fang Liu
- Key Laboratory of Plant Germplasm Enhancement and Specialty Agriculture, Wuhan Botanical Garden, Chinese Academy of Sciences, Wuhan 430074 China
| | - Aidi Zhang
- Key Laboratory of Plant Germplasm Enhancement and Specialty Agriculture, Wuhan Botanical Garden, Chinese Academy of Sciences, Wuhan 430074 China
| | - Xiujun Zhang
- Key Laboratory of Plant Germplasm Enhancement and Specialty Agriculture, Wuhan Botanical Garden, Chinese Academy of Sciences, Wuhan 430074 China
- Center of Economic Botany, Core Botanical Gardens, Chinese Academy of Sciences, Wuhan 430074, China
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Yu J, Leng J, Yuan F, Sun D, Wu LY. Reverse network diffusion to remove indirect noise for better inference of gene regulatory networks. Bioinformatics 2024; 40:btae435. [PMID: 38963312 PMCID: PMC11236096 DOI: 10.1093/bioinformatics/btae435] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/16/2024] [Revised: 06/24/2024] [Accepted: 07/03/2024] [Indexed: 07/05/2024] Open
Abstract
MOTIVATION Gene regulatory networks (GRNs) are vital tools for delineating regulatory relationships between transcription factors and their target genes. The boom in computational biology and various biotechnologies has made inferring GRNs from multi-omics data a hot topic. However, when networks are constructed from gene expression data, they often suffer from false-positive problem due to the transitive effects of correlation. The presence of spurious noise edges obscures the real gene interactions, which makes downstream analyses, such as detecting gene function modules and predicting disease-related genes, difficult and inefficient. Therefore, there is an urgent and compelling need to develop network denoising methods to improve the accuracy of GRN inference. RESULTS In this study, we proposed a novel network denoising method named REverse Network Diffusion On Random walks (RENDOR). RENDOR is designed to enhance the accuracy of GRNs afflicted by indirect effects. RENDOR takes noisy networks as input, models higher-order indirect interactions between genes by transitive closure, eliminates false-positive effects using the inverse network diffusion method, and produces refined networks as output. We conducted a comparative assessment of GRN inference accuracy before and after denoising on simulated networks and real GRNs. Our results emphasized that the network derived from RENDOR more accurately and effectively captures gene interactions. This study demonstrates the significance of removing network indirect noise and highlights the effectiveness of the proposed method in enhancing the signal-to-noise ratio of noisy networks. AVAILABILITY AND IMPLEMENTATION The R package RENDOR is provided at https://github.com/Wu-Lab/RENDOR and other source code and data are available at https://github.com/Wu-Lab/RENDOR-reproduce.
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Affiliation(s)
- Jiating Yu
- School of Mathematics and Statistics, Nanjing University of Information Science & Technology, Nanjing 210044, China
- IAM, MADIS, NCMIS, Academy of Mathematics and Systems Science, Chinese Academy of Sciences, Beijing 100190, China
- School of Mathematical Sciences, University of Chinese Academy of Sciences, Beijing 100049, China
| | - Jiacheng Leng
- IAM, MADIS, NCMIS, Academy of Mathematics and Systems Science, Chinese Academy of Sciences, Beijing 100190, China
- School of Mathematical Sciences, University of Chinese Academy of Sciences, Beijing 100049, China
- Zhejiang Lab, Hangzhou 311121, China
| | - Fan Yuan
- IAM, MADIS, NCMIS, Academy of Mathematics and Systems Science, Chinese Academy of Sciences, Beijing 100190, China
- School of Mathematical Sciences, University of Chinese Academy of Sciences, Beijing 100049, China
| | - Duanchen Sun
- School of Mathematics, Shandong University, Jinan 250100, China
| | - Ling-Yun Wu
- IAM, MADIS, NCMIS, Academy of Mathematics and Systems Science, Chinese Academy of Sciences, Beijing 100190, China
- School of Mathematical Sciences, University of Chinese Academy of Sciences, Beijing 100049, China
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Wei PJ, Guo Z, Gao Z, Ding Z, Cao RF, Su Y, Zheng CH. Inference of gene regulatory networks based on directed graph convolutional networks. Brief Bioinform 2024; 25:bbae309. [PMID: 38935070 PMCID: PMC11209731 DOI: 10.1093/bib/bbae309] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/26/2023] [Revised: 05/17/2024] [Indexed: 06/28/2024] Open
Abstract
Inferring gene regulatory network (GRN) is one of the important challenges in systems biology, and many outstanding computational methods have been proposed; however there remains some challenges especially in real datasets. In this study, we propose Directed Graph Convolutional neural network-based method for GRN inference (DGCGRN). To better understand and process the directed graph structure data of GRN, a directed graph convolutional neural network is conducted which retains the structural information of the directed graph while also making full use of neighbor node features. The local augmentation strategy is adopted in graph neural network to solve the problem of poor prediction accuracy caused by a large number of low-degree nodes in GRN. In addition, for real data such as E.coli, sequence features are obtained by extracting hidden features using Bi-GRU and calculating the statistical physicochemical characteristics of gene sequence. At the training stage, a dynamic update strategy is used to convert the obtained edge prediction scores into edge weights to guide the subsequent training process of the model. The results on synthetic benchmark datasets and real datasets show that the prediction performance of DGCGRN is significantly better than existing models. Furthermore, the case studies on bladder uroepithelial carcinoma and lung cancer cells also illustrate the performance of the proposed model.
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Affiliation(s)
- Pi-Jing Wei
- Key Laboratory of Intelligent Computing & Signal Processing of Ministry of Education, Institutes of Physical Science and Information Technology, Anhui University, 111 Jiulong Road, 230601, Anhui, China
| | - Ziqiang Guo
- Key Laboratory of Intelligent Computing & Signal Processing of Ministry of Education, School of Computer Science and Technology, Anhui University, 111 Jiulong Road, 230601, Anhui, China
| | - Zhen Gao
- Key Laboratory of Intelligent Computing & Signal Processing of Ministry of Education, School of Computer Science and Technology, Anhui University, 111 Jiulong Road, 230601, Anhui, China
| | - Zheng Ding
- Key Laboratory of Intelligent Computing & Signal Processing of Ministry of Education, Institutes of Physical Science and Information Technology, Anhui University, 111 Jiulong Road, 230601, Anhui, China
| | - Rui-Fen Cao
- Key Laboratory of Intelligent Computing & Signal Processing of Ministry of Education, School of Computer Science and Technology, Anhui University, 111 Jiulong Road, 230601, Anhui, China
| | - Yansen Su
- Key Laboratory of Intelligent Computing & Signal Processing of Ministry of Education, School of Artificial Intelligence, Anhui University, 111 Jiulong Road, 230601, Anhui, China
| | - Chun-Hou Zheng
- Key Laboratory of Intelligent Computing & Signal Processing of Ministry of Education, School of Artificial Intelligence, Anhui University, 111 Jiulong Road, 230601, Anhui, China
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Stock M, Popp N, Fiorentino J, Scialdone A. Topological benchmarking of algorithms to infer gene regulatory networks from single-cell RNA-seq data. Bioinformatics 2024; 40:btae267. [PMID: 38627250 PMCID: PMC11096270 DOI: 10.1093/bioinformatics/btae267] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/16/2023] [Revised: 02/28/2024] [Accepted: 04/16/2024] [Indexed: 05/18/2024] Open
Abstract
MOTIVATION In recent years, many algorithms for inferring gene regulatory networks from single-cell transcriptomic data have been published. Several studies have evaluated their accuracy in estimating the presence of an interaction between pairs of genes. However, these benchmarking analyses do not quantify the algorithms' ability to capture structural properties of networks, which are fundamental, e.g., for studying the robustness of a gene network to external perturbations. Here, we devise a three-step benchmarking pipeline called STREAMLINE that quantifies the ability of algorithms to capture topological properties of networks and identify hubs. RESULTS To this aim, we use data simulated from different types of networks as well as experimental data from three different organisms. We apply our benchmarking pipeline to four inference algorithms and provide guidance on which algorithm should be used depending on the global network property of interest. AVAILABILITY AND IMPLEMENTATION STREAMLINE is available at https://github.com/ScialdoneLab/STREAMLINE. The data generated in this study are available at https://doi.org/10.5281/zenodo.10710444.
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Affiliation(s)
- Marco Stock
- Institute of Epigenetics and Stem Cells, Helmholtz Zentrum München—German Research Center for Environmental Health, Munich 81377, Germany
- Institute of Functional Epigenetics, Helmholtz Zentrum München—German Research Center for Environmental Health, Munich 85764, Germany
- Institute of Computational Biology, Helmholtz Zentrum München—German Research Center for Environmental Health, Munich 85764, Germany
- TUM School of Life Sciences Weihenstephan, Technical University of Munich, Munich 85354, Germany
| | - Niclas Popp
- Institute of Epigenetics and Stem Cells, Helmholtz Zentrum München—German Research Center for Environmental Health, Munich 81377, Germany
- Institute of Functional Epigenetics, Helmholtz Zentrum München—German Research Center for Environmental Health, Munich 85764, Germany
- Institute of Computational Biology, Helmholtz Zentrum München—German Research Center for Environmental Health, Munich 85764, Germany
| | - Jonathan Fiorentino
- Institute of Epigenetics and Stem Cells, Helmholtz Zentrum München—German Research Center for Environmental Health, Munich 81377, Germany
- Institute of Functional Epigenetics, Helmholtz Zentrum München—German Research Center for Environmental Health, Munich 85764, Germany
- Institute of Computational Biology, Helmholtz Zentrum München—German Research Center for Environmental Health, Munich 85764, Germany
| | - Antonio Scialdone
- Institute of Epigenetics and Stem Cells, Helmholtz Zentrum München—German Research Center for Environmental Health, Munich 81377, Germany
- Institute of Functional Epigenetics, Helmholtz Zentrum München—German Research Center for Environmental Health, Munich 85764, Germany
- Institute of Computational Biology, Helmholtz Zentrum München—German Research Center for Environmental Health, Munich 85764, Germany
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Kim D, Heo Y, Kim M, Suminda GGD, Manzoor U, Min Y, Kim M, Yang J, Park Y, Zhao Y, Ghosh M, Son YO. Inhibitory effects of Acanthopanax sessiliflorus Harms extract on the etiology of rheumatoid arthritis in a collagen-induced arthritis mouse model. Arthritis Res Ther 2024; 26:11. [PMID: 38167214 PMCID: PMC10763440 DOI: 10.1186/s13075-023-03241-1] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/16/2023] [Accepted: 12/15/2023] [Indexed: 01/05/2024] Open
Abstract
BACKGROUND The biological function of Acanthopanax sessiliflorus Harm (ASH) has been investigated on various diseases; however, the effects of ASH on arthritis have not been investigated so far. This study investigates the effects of ASH on rheumatoid arthritis (RA). METHODS Supercritical carbon dioxide (CO2) was used for ASH extract preparation, and its primary components, pimaric and kaurenoic acids, were identified using gas chromatography-mass spectrometer (GC-MS). Collagenase-induced arthritis (CIA) was used as the RA model, and primary cultures of articular chondrocytes were used to examine the inhibitory effects of ASH extract on arthritis in three synovial joints: ankle, sole, and knee. RESULTS Pimaric and kaurenoic acids attenuated pro-inflammatory cytokine-mediated increase in the catabolic factors and retrieved pro-inflammatory cytokine-mediated decrease in related anabolic factors in vitro; however, they did not affect pro-inflammatory cytokine (IL-1β, TNF-α, and IL-6)-mediated cytotoxicity. ASH effectively inhibited cartilage degradation in the knee, ankle, and toe in the CIA model and decreased pannus development in the knee. Immunohistochemistry demonstrated that ASH mostly inhibited the IL-6-mediated matrix metalloproteinase. Gene Ontology and pathway studies bridge major gaps in the literature and provide insights into the pathophysiology and in-depth mechanisms of RA-like joint degeneration. CONCLUSIONS To the best of our knowledge, this is the first study to conduct extensive research on the efficacy of ASH extract in inhibiting the pathogenesis of RA. However, additional animal models and clinical studies are required to validate this hypothesis.
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Affiliation(s)
- Dahye Kim
- Division of Animal Genetics and Bioinformatics, National Institute of Animal Science, RDA, Wanju, Republic of Korea
| | - Yunji Heo
- Department of Animal Biotechnology, Faculty of Biotechnology, College of Applied Life Sciences, Jeju National University, Jeju City, Jeju Special Self-Governing Province, 63243, Republic of Korea
| | - Mangeun Kim
- Department of Animal Biotechnology, Faculty of Biotechnology, College of Applied Life Sciences, Jeju National University, Jeju City, Jeju Special Self-Governing Province, 63243, Republic of Korea
| | - Godagama Gamaarachchige Dinesh Suminda
- Interdisciplinary Graduate Program in Advanced Convergence Technology and Science, Jeju National University, Jeju City, Jeju Special Self-Governing Province, 63243, Republic of Korea
| | - Umar Manzoor
- Interdisciplinary Graduate Program in Advanced Convergence Technology and Science, Jeju National University, Jeju City, Jeju Special Self-Governing Province, 63243, Republic of Korea
- Laboratory of Immune and Inflammatory Disease, College of Pharmacy, Jeju Research Institute of Pharmaceutical Sciences, Jeju National University, Jeju, 63243, Republic of Korea
| | - Yunhui Min
- Interdisciplinary Graduate Program in Advanced Convergence Technology and Science, Jeju National University, Jeju City, Jeju Special Self-Governing Province, 63243, Republic of Korea
| | - Minhye Kim
- Department of Animal Biotechnology, Faculty of Biotechnology, College of Applied Life Sciences, Jeju National University, Jeju City, Jeju Special Self-Governing Province, 63243, Republic of Korea
| | - Jiwon Yang
- Interdisciplinary Graduate Program in Advanced Convergence Technology and Science, Jeju National University, Jeju City, Jeju Special Self-Governing Province, 63243, Republic of Korea
| | - Youngjun Park
- Interdisciplinary Graduate Program in Advanced Convergence Technology and Science, Jeju National University, Jeju City, Jeju Special Self-Governing Province, 63243, Republic of Korea
- Laboratory of Immune and Inflammatory Disease, College of Pharmacy, Jeju Research Institute of Pharmaceutical Sciences, Jeju National University, Jeju, 63243, Republic of Korea
| | - Yaping Zhao
- Frontiers Science Center for Transformative Molecules, School of Chemistry and Chemical Engineering, Shanghai Jiao Tong University, Shanghai, 200240, People's Republic of China
| | - Mrinmoy Ghosh
- Department of Animal Biotechnology, Faculty of Biotechnology, College of Applied Life Sciences, Jeju National University, Jeju City, Jeju Special Self-Governing Province, 63243, Republic of Korea.
- Department of Biotechnology, School of Bio, Chemical and Processing Engineering (SBCE), Kalasalingam Academy of Research and Education, Krishnankoil, Srivilliputhur, 626126, India.
| | - Young-Ok Son
- Department of Animal Biotechnology, Faculty of Biotechnology, College of Applied Life Sciences, Jeju National University, Jeju City, Jeju Special Self-Governing Province, 63243, Republic of Korea.
- Interdisciplinary Graduate Program in Advanced Convergence Technology and Science, Jeju National University, Jeju City, Jeju Special Self-Governing Province, 63243, Republic of Korea.
- Practical Translational Research Center, Jeju National University, Jeju, 63243, Republic of Korea.
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Nakulugamuwa Gamage H, Chetty M, Lim S, Hallinan J. MICFuzzy: A maximal information content based fuzzy approach for reconstructing genetic networks. PLoS One 2023; 18:e0288174. [PMID: 37418430 DOI: 10.1371/journal.pone.0288174] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/10/2022] [Accepted: 06/21/2023] [Indexed: 07/09/2023] Open
Abstract
In systems biology, the accurate reconstruction of Gene Regulatory Networks (GRNs) is crucial since these networks can facilitate the solving of complex biological problems. Amongst the plethora of methods available for GRN reconstruction, information theory and fuzzy concepts-based methods have abiding popularity. However, most of these methods are not only complex, incurring a high computational burden, but they may also produce a high number of false positives, leading to inaccurate inferred networks. In this paper, we propose a novel hybrid fuzzy GRN inference model called MICFuzzy which involves the aggregation of the effects of Maximal Information Coefficient (MIC). This model has an information theory-based pre-processing stage, the output of which is applied as an input to the novel fuzzy model. In this preprocessing stage, the MIC component filters relevant genes for each target gene to significantly reduce the computational burden of the fuzzy model when selecting the regulatory genes from these filtered gene lists. The novel fuzzy model uses the regulatory effect of the identified activator-repressor gene pairs to determine target gene expression levels. This approach facilitates accurate network inference by generating a high number of true regulatory interactions while significantly reducing false regulatory predictions. The performance of MICFuzzy was evaluated using DREAM3 and DREAM4 challenge data, and the SOS real gene expression dataset. MICFuzzy outperformed the other state-of-the-art methods in terms of F-score, Matthews Correlation Coefficient, Structural Accuracy, and SS_mean, and outperformed most of them in terms of efficiency. MICFuzzy also had improved efficiency compared with the classical fuzzy model since the design of MICFuzzy leads to a reduction in combinatorial computation.
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Affiliation(s)
| | - Madhu Chetty
- Health Innovation and Transformation Centre, Federation University, Churchill, Victoria, Australia
| | - Suryani Lim
- Health Innovation and Transformation Centre, Federation University, Churchill, Victoria, Australia
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10
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Wang Y, Xuan C, Wu H, Zhang B, Ding T, Gao J. P-CSN: single-cell RNA sequencing data analysis by partial cell-specific network. Brief Bioinform 2023; 24:bbad180. [PMID: 37170676 DOI: 10.1093/bib/bbad180] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/07/2022] [Revised: 03/14/2023] [Accepted: 04/19/2023] [Indexed: 05/13/2023] Open
Abstract
Although many single-cell computational methods proposed use gene expression as input, recent studies show that replacing 'unstable' gene expression with 'stable' gene-gene associations can greatly improve the performance of downstream analysis. To obtain accurate gene-gene associations, conditional cell-specific network method (c-CSN) filters out the indirect associations of cell-specific network method (CSN) based on the conditional independence of statistics. However, when there are strong connections in networks, the c-CSN suffers from false negative problem in network construction. To overcome this problem, a new partial cell-specific network method (p-CSN) based on the partial independence of statistics is proposed in this paper, which eliminates the singularity of the c-CSN by implicitly including direct associations among estimated variables. Based on the p-CSN, single-cell network entropy (scNEntropy) is further proposed to quantify cell state. The superiorities of our method are verified on several datasets. (i) Compared with traditional gene regulatory network construction methods, the p-CSN constructs partial cell-specific networks, namely, one cell to one network. (ii) When there are strong connections in networks, the p-CSN reduces the false negative probability of the c-CSN. (iii) The input of more accurate gene-gene associations further optimizes the performance of downstream analyses. (iv) The scNEntropy effectively quantifies cell state and reconstructs cell pseudo-time.
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Affiliation(s)
- Yan Wang
- School of Science, Jiangnan University, Wuxi 214122, China
| | - Chenxu Xuan
- School of Science, Jiangnan University, Wuxi 214122, China
| | - Hanwen Wu
- School of Science, Jiangnan University, Wuxi 214122, China
| | - Bai Zhang
- School of Science, Jiangnan University, Wuxi 214122, China
| | - Tao Ding
- School of Mathematics Statistics and Physics, Newcastle University, Newcastle upon Tyne NE1 7RU, UK
| | - Jie Gao
- School of Science, Jiangnan University, Wuxi 214122, China
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11
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Jiang X, Liu K, Peng H, Fang J, Zhang A, Han Y, Zhang X. Comparative network analysis reveals the dynamics of organic acid diversity during fruit ripening in peach (Prunus persica L. Batsch). BMC PLANT BIOLOGY 2023; 23:16. [PMID: 36617558 PMCID: PMC9827700 DOI: 10.1186/s12870-023-04037-w] [Citation(s) in RCA: 5] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 07/12/2022] [Accepted: 01/02/2023] [Indexed: 06/17/2023]
Abstract
BACKGROUND Organic acids are important components that determine the fruit flavor of peach (Prunus persica L. Batsch). However, the dynamics of organic acid diversity during fruit ripening and the key genes that modulate the organic acids metabolism remain largely unknown in this kind of fruit tree which yield ranks sixth in the world. RESULTS In this study, we used 3D transcriptome data containing three dimensions of information, namely time, phenotype and gene expression, from 5 different varieties of peach to construct gene co-expression networks throughout fruit ripening of peach. With the network inferred, the time-ordered network comparative analysis was performed to select high-acid specific gene co-expression network and then clarify the regulatory factors controlling organic acid accumulation. As a result, network modules related to organic acid synthesis and metabolism under high-acid and low-acid comparison conditions were identified for our following research. In addition, we obtained 20 candidate genes as regulatory factors related to organic acid metabolism in peach. CONCLUSIONS The study provides new insights into the dynamics of organic acid accumulation during fruit ripening, complements the results of classical co-expression network analysis and establishes a foundation for key genes discovery from time-series multiple species transcriptome data.
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Affiliation(s)
- Xiaohan Jiang
- Key Laboratory of Plant Germplasm Enhancement and Specialty Agriculture, Wuhan Botanical Garden, Chinese Academy of Sciences, Wuhan, 430074, Hubei, China
- Center of Economic Botany, Core Botanical Gardens, Wuhan Botanical Garden, Chinese Academy of Sciences, Wuhan, 430074, Hubei, China
- University of Chinese Academy of Sciences, Beijing, 100049, China
| | - Kangchen Liu
- Key Laboratory of Plant Germplasm Enhancement and Specialty Agriculture, Wuhan Botanical Garden, Chinese Academy of Sciences, Wuhan, 430074, Hubei, China
- Center of Economic Botany, Core Botanical Gardens, Wuhan Botanical Garden, Chinese Academy of Sciences, Wuhan, 430074, Hubei, China
- University of Chinese Academy of Sciences, Beijing, 100049, China
| | - Huixiang Peng
- Key Laboratory of Plant Germplasm Enhancement and Specialty Agriculture, Wuhan Botanical Garden, Chinese Academy of Sciences, Wuhan, 430074, Hubei, China
- Center of Economic Botany, Core Botanical Gardens, Wuhan Botanical Garden, Chinese Academy of Sciences, Wuhan, 430074, Hubei, China
- University of Chinese Academy of Sciences, Beijing, 100049, China
| | - Jing Fang
- Key Laboratory of Plant Germplasm Enhancement and Specialty Agriculture, Wuhan Botanical Garden, Chinese Academy of Sciences, Wuhan, 430074, Hubei, China
- Center of Economic Botany, Core Botanical Gardens, Wuhan Botanical Garden, Chinese Academy of Sciences, Wuhan, 430074, Hubei, China
- University of Chinese Academy of Sciences, Beijing, 100049, China
| | - Aidi Zhang
- Key Laboratory of Plant Germplasm Enhancement and Specialty Agriculture, Wuhan Botanical Garden, Chinese Academy of Sciences, Wuhan, 430074, Hubei, China
- Center of Economic Botany, Core Botanical Gardens, Wuhan Botanical Garden, Chinese Academy of Sciences, Wuhan, 430074, Hubei, China
| | - Yuepeng Han
- Key Laboratory of Plant Germplasm Enhancement and Specialty Agriculture, Wuhan Botanical Garden, Chinese Academy of Sciences, Wuhan, 430074, Hubei, China.
- Center of Economic Botany, Core Botanical Gardens, Wuhan Botanical Garden, Chinese Academy of Sciences, Wuhan, 430074, Hubei, China.
| | - Xiujun Zhang
- Key Laboratory of Plant Germplasm Enhancement and Specialty Agriculture, Wuhan Botanical Garden, Chinese Academy of Sciences, Wuhan, 430074, Hubei, China.
- Center of Economic Botany, Core Botanical Gardens, Wuhan Botanical Garden, Chinese Academy of Sciences, Wuhan, 430074, Hubei, China.
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12
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Ye Q, Guo NL. Inferencing Bulk Tumor and Single-Cell Multi-Omics Regulatory Networks for Discovery of Biomarkers and Therapeutic Targets. Cells 2022; 12:101. [PMID: 36611894 PMCID: PMC9818242 DOI: 10.3390/cells12010101] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/06/2022] [Revised: 12/22/2022] [Accepted: 12/24/2022] [Indexed: 12/28/2022] Open
Abstract
There are insufficient accurate biomarkers and effective therapeutic targets in current cancer treatment. Multi-omics regulatory networks in patient bulk tumors and single cells can shed light on molecular disease mechanisms. Integration of multi-omics data with large-scale patient electronic medical records (EMRs) can lead to the discovery of biomarkers and therapeutic targets. In this review, multi-omics data harmonization methods were introduced, and common approaches to molecular network inference were summarized. Our Prediction Logic Boolean Implication Networks (PLBINs) have advantages over other methods in constructing genome-scale multi-omics networks in bulk tumors and single cells in terms of computational efficiency, scalability, and accuracy. Based on the constructed multi-modal regulatory networks, graph theory network centrality metrics can be used in the prioritization of candidates for discovering biomarkers and therapeutic targets. Our approach to integrating multi-omics profiles in a patient cohort with large-scale patient EMRs such as the SEER-Medicare cancer registry combined with extensive external validation can identify potential biomarkers applicable in large patient populations. These methodologies form a conceptually innovative framework to analyze various available information from research laboratories and healthcare systems, accelerating the discovery of biomarkers and therapeutic targets to ultimately improve cancer patient survival outcomes.
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Affiliation(s)
- Qing Ye
- West Virginia University Cancer Institute, Morgantown, WV 26506, USA
- Lane Department of Computer Science and Electrical Engineering, West Virginia University, Morgantown, WV 26506, USA
| | - Nancy Lan Guo
- West Virginia University Cancer Institute, Morgantown, WV 26506, USA
- Department of Occupational and Environmental Health Sciences, School of Public Health, West Virginia University, Morgantown, WV 26506, USA
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13
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Jia Z, Zhang X. Accurate determination of causalities in gene regulatory networks by dissecting downstream target genes. Front Genet 2022; 13:923339. [PMID: 36568360 PMCID: PMC9768335 DOI: 10.3389/fgene.2022.923339] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/19/2022] [Accepted: 11/08/2022] [Indexed: 12/12/2022] Open
Abstract
Accurate determination of causalities between genes is a challenge in the inference of gene regulatory networks (GRNs) from the gene expression profile. Although many methods have been developed for the reconstruction of GRNs, most of them are insufficient in determining causalities or regulatory directions. In this work, we present a novel method, namely, DDTG, to improve the accuracy of causality determination in GRN inference by dissecting downstream target genes. In the proposed method, the topology and hierarchy of GRNs are determined by mutual information and conditional mutual information, and the regulatory directions of GRNs are determined by Taylor formula-based regression. In addition, indirect interactions are removed with the sparseness of the network topology to improve the accuracy of network inference. The method is validated on the benchmark GRNs from DREAM3 and DREAM4 challenges. The results demonstrate the superior performance of the DDTG method on causality determination of GRNs compared to some popular GRN inference methods. This work provides a useful tool to infer the causal gene regulatory network.
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Affiliation(s)
- Zhigang Jia
- School of Mathematics and Statistics, Xinyang Normal University, Xinyang, China,Key Laboratory of Plant Germplasm Enhancement and Specialty Agriculture, Wuhan Botanical Garden, Chinese Academy of Sciences, Wuhan, China
| | - Xiujun Zhang
- Key Laboratory of Plant Germplasm Enhancement and Specialty Agriculture, Wuhan Botanical Garden, Chinese Academy of Sciences, Wuhan, China,Center of Economic Botany, Core Botanical Gardens, Chinese Academy of Sciences, Wuhan, China,*Correspondence: Xiujun Zhang,
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14
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Xu Y, Chen J, Lyu A, Cheung WK, Zhang L. dynDeepDRIM: a dynamic deep learning model to infer direct regulatory interactions using time-course single-cell gene expression data. Brief Bioinform 2022; 23:6720420. [PMID: 36168811 DOI: 10.1093/bib/bbac424] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/23/2022] [Revised: 08/02/2022] [Accepted: 09/01/2022] [Indexed: 12/14/2022] Open
Abstract
Time-course single-cell RNA sequencing (scRNA-seq) data have been widely used to explore dynamic changes in gene expression of transcription factors (TFs) and their target genes. This information is useful to reconstruct cell-type-specific gene regulatory networks (GRNs). However, the existing tools are commonly designed to analyze either time-course bulk gene expression data or static scRNA-seq data via pseudo-time cell ordering. A few methods successfully utilize the information from multiple time points while also considering the characteristics of scRNA-seq data. We proposed dynDeepDRIM, a novel deep learning model to reconstruct GRNs using time-course scRNA-seq data. It represents the joint expression of a gene pair as an image and utilizes the image of the target TF-gene pair and the ones of the potential neighbors to reconstruct GRNs from time-course scRNA-seq data. dynDeepDRIM can effectively remove the transitive TF-gene interactions by considering neighborhood context and model the gene expression dynamics using high-dimensional tensors. We compared dynDeepDRIM with six GRN reconstruction methods on both simulation and four real time-course scRNA-seq data. dynDeepDRIM achieved substantially better performance than the other methods in inferring TF-gene interactions and eliminated the false positives effectively. We also applied dynDeepDRIM to annotate gene functions and found it achieved evidently better performance than the other tools due to considering the neighbor genes.
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Affiliation(s)
- Yu Xu
- Department of Computer Science, Hong Kong Baptist University, Kowloon Tong, Hong Kong
| | - Jiaxing Chen
- Computer Science and Technology, Division of Science and Technology, BNU-HKBU United International College, Jintong Road, 519087, Zhuhai, China
| | - Aiping Lyu
- School of Chinese Medicine, Hong Kong Baptist University, Kowloon Tong, Hong Kong
| | - William K Cheung
- Department of Computer Science, Hong Kong Baptist University, Kowloon Tong, Hong Kong
| | - Lu Zhang
- Department of Computer Science, Hong Kong Baptist University, Kowloon Tong, Hong Kong
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15
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Lei J, Cai Z, He X, Zheng W, Liu J. An approach of gene regulatory network construction using mixed entropy optimizing context-related likelihood mutual information. Bioinformatics 2022; 39:6808612. [PMID: 36342190 PMCID: PMC9805593 DOI: 10.1093/bioinformatics/btac717] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/25/2022] [Revised: 09/18/2022] [Accepted: 11/04/2022] [Indexed: 11/09/2022] Open
Abstract
MOTIVATION The question of how to construct gene regulatory networks has long been a focus of biological research. Mutual information can be used to measure nonlinear relationships, and it has been widely used in the construction of gene regulatory networks. However, this method cannot measure indirect regulatory relationships under the influence of multiple genes, which reduces the accuracy of inferring gene regulatory networks. APPROACH This work proposes a method for constructing gene regulatory networks based on mixed entropy optimizing context-related likelihood mutual information (MEOMI). First, two entropy estimators were combined to calculate the mutual information between genes. Then, distribution optimization was performed using a context-related likelihood algorithm to eliminate some indirect regulatory relationships and obtain the initial gene regulatory network. To obtain the complex interaction between genes and eliminate redundant edges in the network, the initial gene regulatory network was further optimized by calculating the conditional mutual inclusive information (CMI2) between gene pairs under the influence of multiple genes. The network was iteratively updated to reduce the impact of mutual information on the overestimation of the direct regulatory intensity. RESULTS The experimental results show that the MEOMI method performed better than several other kinds of gene network construction methods on DREAM challenge simulated datasets (DREAM3 and DREAM5), three real Escherichia coli datasets (E.coli SOS pathway network, E.coli SOS DNA repair network and E.coli community network) and two human datasets. AVAILABILITY AND IMPLEMENTATION Source code and dataset are available at https://github.com/Dalei-Dalei/MEOMI/ and http://122.205.95.139/MEOMI/. SUPPLEMENTARY INFORMATION Supplementary data are available at Bioinformatics online.
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Affiliation(s)
- Jimeng Lei
- National Key Laboratory of Crop Genetic Improvement, Huazhong Agricultural University, Wuhan 430070, China,Key Laboratory of Smart Farming for Agricultural Animals, Huazhong Agricultural University, Wuhan 430070, China,College of Informatics, Huazhong Agricultural University, Wuhan 430070, China
| | - Zongheng Cai
- National Key Laboratory of Crop Genetic Improvement, Huazhong Agricultural University, Wuhan 430070, China,Key Laboratory of Smart Farming for Agricultural Animals, Huazhong Agricultural University, Wuhan 430070, China,College of Informatics, Huazhong Agricultural University, Wuhan 430070, China
| | - Xinyi He
- College of Informatics, Huazhong Agricultural University, Wuhan 430070, China
| | - Wanting Zheng
- College of Informatics, Huazhong Agricultural University, Wuhan 430070, China
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16
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Liu W, Sun X, Yang L, Li K, Yang Y, Fu X. NSCGRN: a network structure control method for gene regulatory network inference. Brief Bioinform 2022; 23:6585392. [PMID: 35554485 DOI: 10.1093/bib/bbac156] [Citation(s) in RCA: 9] [Impact Index Per Article: 4.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/31/2022] [Revised: 03/27/2022] [Accepted: 04/06/2022] [Indexed: 01/18/2023] Open
Abstract
Accurate inference of gene regulatory networks (GRNs) is an essential premise for understanding pathogenesis and curing diseases. Various computational methods have been developed for GRN inference, but the identification of redundant regulation remains a challenge faced by researchers. Although combining global and local topology can identify and reduce redundant regulations, the topologies' specific forms and cooperation modes are unclear and real regulations may be sacrificed. Here, we propose a network structure control method [network-structure-controlling-based GRN inference method (NSCGRN)] that stipulates the global and local topology's specific forms and cooperation mode. The method is carried out in a cooperative mode of 'global topology dominates and local topology refines'. Global topology requires layering and sparseness of the network, and local topology requires consistency of the subgraph association pattern with the network motifs (fan-in, fan-out, cascade and feedforward loop). Specifically, an ordered gene list is obtained by network topology centrality sorting. A Bernaola-Galvan mutation detection algorithm applied to the list gives the hierarchy of GRNs to control the upstream and downstream regulations within the global scope. Finally, four network motifs are integrated into the hierarchy to optimize local complex regulations and form a cooperative mode where global and local topologies play the dominant and refined roles, respectively. NSCGRN is compared with state-of-the-art methods on three different datasets (six networks in total), and it achieves the highest F1 and Matthews correlation coefficient. Experimental results show its unique advantages in GRN inference.
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Affiliation(s)
- Wei Liu
- Key Laboratory of Intelligent Computing and Information Processing of Ministry of Education, Xiangtan University, Xiangtan, 411105, China.,School of Computer Science, Xiangtan University, Xiangtan, 411105, China
| | - Xingen Sun
- School of Computer Science, Xiangtan University, Xiangtan, 411105, China.,Key Laboratory of Intelligent Computing and Information Processing of Ministry of Education, Xiangtan University, Xiangtan, 411105, China
| | - Li Yang
- Key Laboratory of Intelligent Computing and Information Processing of Ministry of Education, Xiangtan University, Xiangtan, 411105, China
| | - Kaiwen Li
- Artificial Intelligence Research Institute, China University of Mining and Technology, Xuzhou, 221116, China
| | - Yu Yang
- School of Computer Science, Xiangtan University, Xiangtan, 411105, China.,Key Laboratory of Intelligent Computing and Information Processing of Ministry of Education, Xiangtan University, Xiangtan, 411105, China
| | - Xiangzheng Fu
- College of Computer Science and Electronic Engineering, Hunan University, Changsha, 410000, China
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17
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Inference of Molecular Regulatory Systems Using Statistical Path-Consistency Algorithm. ENTROPY 2022; 24:e24050693. [PMID: 35626576 PMCID: PMC9142129 DOI: 10.3390/e24050693] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 03/31/2022] [Revised: 05/12/2022] [Accepted: 05/12/2022] [Indexed: 11/16/2022]
Abstract
One of the key challenges in systems biology and molecular sciences is how to infer regulatory relationships between genes and proteins using high-throughout omics datasets. Although a wide range of methods have been designed to reverse engineer the regulatory networks, recent studies show that the inferred network may depend on the variable order in the dataset. In this work, we develop a new algorithm, called the statistical path-consistency algorithm (SPCA), to solve the problem of the dependence of variable order. This method generates a number of different variable orders using random samples, and then infers a network by using the path-consistent algorithm based on each variable order. We propose measures to determine the edge weights using the corresponding edge weights in the inferred networks, and choose the edges with the largest weights as the putative regulations between genes or proteins. The developed method is rigorously assessed by the six benchmark networks in DREAM challenges, the mitogen-activated protein (MAP) kinase pathway, and a cancer-specific gene regulatory network. The inferred networks are compared with those obtained by using two up-to-date inference methods. The accuracy of the inferred networks shows that the developed method is effective for discovering molecular regulatory systems.
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18
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Jiang X, Zhang X. RSNET: inferring gene regulatory networks by a redundancy silencing and network enhancement technique. BMC Bioinformatics 2022; 23:165. [PMID: 35524190 PMCID: PMC9074326 DOI: 10.1186/s12859-022-04696-w] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/14/2021] [Accepted: 04/25/2022] [Indexed: 11/29/2022] Open
Abstract
Background Current gene regulatory network (GRN) inference methods are notorious for a great number of indirect interactions hidden in the predictions. Filtering out the indirect interactions from direct ones remains an important challenge in the reconstruction of GRNs. To address this issue, we developed a redundancy silencing and network enhancement technique (RSNET) for inferring GRNs. Results To assess the performance of RSNET method, we implemented the experiments on several gold-standard networks by using simulation study, DREAM challenge dataset and Escherichia coli network. The results show that RSNET method performed better than the compared methods in sensitivity and accuracy. As a case of study, we used RSNET to construct functional GRN for apple fruit ripening from gene expression data. Conclusions In the proposed method, the redundant interactions including weak and indirect connections are silenced by recursive optimization adaptively, and the highly dependent nodes are constrained in the model to keep the real interactions. This study provides a useful tool for inferring clean networks. Supplementary Information The online version contains supplementary material available at 10.1186/s12859-022-04696-w.
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Affiliation(s)
- Xiaohan Jiang
- Key Laboratory of Plant Germplasm Enhancement and Specialty Agriculture, Wuhan Botanical Garden, Chinese Academy of Sciences, Wuhan, 430074, China.,Center of Economic Botany, Core Botanical Gardens, Chinese Academy of Sciences, Wuhan, 430074, China.,University of Chinese Academy of Sciences, Beijing, 100049, China
| | - Xiujun Zhang
- Key Laboratory of Plant Germplasm Enhancement and Specialty Agriculture, Wuhan Botanical Garden, Chinese Academy of Sciences, Wuhan, 430074, China. .,Center of Economic Botany, Core Botanical Gardens, Chinese Academy of Sciences, Wuhan, 430074, China.
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19
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Zhao M, He W, Tang J, Zou Q, Guo F. A hybrid deep learning framework for gene regulatory network inference from single-cell transcriptomic data. Brief Bioinform 2022; 23:6513730. [DOI: 10.1093/bib/bbab568] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/28/2021] [Revised: 12/09/2021] [Accepted: 12/11/2021] [Indexed: 12/21/2022] Open
Abstract
Abstract
Inferring gene regulatory networks (GRNs) based on gene expression profiles is able to provide an insight into a number of cellular phenotypes from the genomic level and reveal the essential laws underlying various life phenomena. Different from the bulk expression data, single-cell transcriptomic data embody cell-to-cell variance and diverse biological information, such as tissue characteristics, transformation of cell types, etc. Inferring GRNs based on such data offers unprecedented advantages for making a profound study of cell phenotypes, revealing gene functions and exploring potential interactions. However, the high sparsity, noise and dropout events of single-cell transcriptomic data pose new challenges for regulation identification. We develop a hybrid deep learning framework for GRN inference from single-cell transcriptomic data, DGRNS, which encodes the raw data and fuses recurrent neural network and convolutional neural network (CNN) to train a model capable of distinguishing related gene pairs from unrelated gene pairs. To overcome the limitations of such datasets, it applies sliding windows to extract valuable features while preserving the direction of regulation. DGRNS is constructed as a deep learning model containing gated recurrent unit network for exploring time-dependent information and CNN for learning spatially related information. Our comprehensive and detailed comparative analysis on the dataset of mouse hematopoietic stem cells illustrates that DGRNS outperforms state-of-the-art methods. The networks inferred by DGRNS are about 16% higher than the area under the receiver operating characteristic curve of other unsupervised methods and 10% higher than the area under the precision recall curve of other supervised methods. Experiments on human datasets show the strong robustness and excellent generalization of DGRNS. By comparing the predictions with standard network, we discover a series of novel interactions which are proved to be true in some specific cell types. Importantly, DGRNS identifies a series of regulatory relationships with high confidence and functional consistency, which have not yet been experimentally confirmed and merit further research.
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20
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Chen J, Cheong C, Lan L, Zhou X, Liu J, Lyu A, Cheung WK, Zhang L. DeepDRIM: a deep neural network to reconstruct cell-type-specific gene regulatory network using single-cell RNA-seq data. Brief Bioinform 2021; 22:bbab325. [PMID: 34424948 PMCID: PMC8499812 DOI: 10.1093/bib/bbab325] [Citation(s) in RCA: 25] [Impact Index Per Article: 8.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/28/2021] [Revised: 07/12/2021] [Accepted: 07/26/2021] [Indexed: 01/11/2023] Open
Abstract
Single-cell RNA sequencing has enabled to capture the gene activities at single-cell resolution, thus allowing reconstruction of cell-type-specific gene regulatory networks (GRNs). The available algorithms for reconstructing GRNs are commonly designed for bulk RNA-seq data, and few of them are applicable to analyze scRNA-seq data by dealing with the dropout events and cellular heterogeneity. In this paper, we represent the joint gene expression distribution of a gene pair as an image and propose a novel supervised deep neural network called DeepDRIM which utilizes the image of the target TF-gene pair and the ones of the potential neighbors to reconstruct GRN from scRNA-seq data. Due to the consideration of TF-gene pair's neighborhood context, DeepDRIM can effectively eliminate the false positives caused by transitive gene-gene interactions. We compared DeepDRIM with nine GRN reconstruction algorithms designed for either bulk or single-cell RNA-seq data. It achieves evidently better performance for the scRNA-seq data collected from eight cell lines. The simulated data show that DeepDRIM is robust to the dropout rate, the cell number and the size of the training data. We further applied DeepDRIM to the scRNA-seq gene expression of B cells from the bronchoalveolar lavage fluid of the patients with mild and severe coronavirus disease 2019. We focused on the cell-type-specific GRN alteration and observed targets of TFs that were differentially expressed between the two statuses to be enriched in lysosome, apoptosis, response to decreased oxygen level and microtubule, which had been proved to be associated with coronavirus infection.
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Affiliation(s)
- Jiaxing Chen
- Department of Computer Science, Hong Kong Baptist University, Waterloo Road, Kowloon Tong, Hong Kong
| | - ChinWang Cheong
- Department of Computer Science, Hong Kong Baptist University, Waterloo Road, Kowloon Tong, Hong Kong
| | - Liang Lan
- Department of Computer Science, Hong Kong Baptist University, Waterloo Road, Kowloon Tong, Hong Kong
| | - Xin Zhou
- Department of Biomedical Engineering, Vanderbilt University, Vanderbilt Place Nashville, 37235, TN, USA
| | - Jiming Liu
- Department of Computer Science, Hong Kong Baptist University, Waterloo Road, Kowloon Tong, Hong Kong
| | - Aiping Lyu
- School of Chinese Medicine, Hong Kong Baptist University, Waterloo Road, Kowloon Tong, Hong Kong
| | - William K Cheung
- Department of Computer Science, Hong Kong Baptist University, Waterloo Road, Kowloon Tong, Hong Kong
| | - Lu Zhang
- Department of Computer Science, Hong Kong Baptist University, Waterloo Road, Kowloon Tong, Hong Kong
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21
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Lingjærde C, Lien TG, Borgan Ø, Bergholtz H, Glad IK. Tailored graphical lasso for data integration in gene network reconstruction. BMC Bioinformatics 2021; 22:498. [PMID: 34654363 PMCID: PMC8518261 DOI: 10.1186/s12859-021-04413-z] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/21/2020] [Accepted: 09/30/2021] [Indexed: 11/20/2022] Open
Abstract
BACKGROUND Identifying gene interactions is a topic of great importance in genomics, and approaches based on network models provide a powerful tool for studying these. Assuming a Gaussian graphical model, a gene association network may be estimated from multiomic data based on the non-zero entries of the inverse covariance matrix. Inferring such biological networks is challenging because of the high dimensionality of the problem, making traditional estimators unsuitable. The graphical lasso is constructed for the estimation of sparse inverse covariance matrices in such situations, using [Formula: see text]-penalization on the matrix entries. The weighted graphical lasso is an extension in which prior biological information from other sources is integrated into the model. There are however issues with this approach, as it naïvely forces the prior information into the network estimation, even if it is misleading or does not agree with the data at hand. Further, if an associated network based on other data is used as the prior, the method often fails to utilize the information effectively. RESULTS We propose a novel graphical lasso approach, the tailored graphical lasso, that aims to handle prior information of unknown accuracy more effectively. We provide an R package implementing the method, tailoredGlasso. Applying the method to both simulated and real multiomic data sets, we find that it outperforms the unweighted and weighted graphical lasso in terms of all performance measures we consider. In fact, the graphical lasso and weighted graphical lasso can be considered special cases of the tailored graphical lasso, and a parameter determined by the data measures the usefulness of the prior information. We also find that among a larger set of methods, the tailored graphical is the most suitable for network inference from high-dimensional data with prior information of unknown accuracy. With our method, mRNA data are demonstrated to provide highly useful prior information for protein-protein interaction networks. CONCLUSIONS The method we introduce utilizes useful prior information more effectively without involving any risk of loss of accuracy should the prior information be misleading.
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Affiliation(s)
- Camilla Lingjærde
- MRC Biostatistics Unit, University of Cambridge, Forvie Site, Robinson Way, Cambridge, CB2 0SR, UK.
| | - Tonje G Lien
- Department of Cancer Genetics, Institute for Cancer Research, Oslo University Hospital, Ullernchausseen 70, 0310, Oslo, Norway
| | - Ørnulf Borgan
- Department of Mathematics, University of Oslo, PO Box 1053 Blindern, 0316, Oslo, Norway
| | - Helga Bergholtz
- Department of Cancer Genetics, Institute for Cancer Research, Oslo University Hospital, Ullernchausseen 70, 0310, Oslo, Norway
| | - Ingrid K Glad
- Department of Mathematics, University of Oslo, PO Box 1053 Blindern, 0316, Oslo, Norway
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22
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Wang T, Zhang X. Genome-wide dynamic network analysis reveals the potential genes for MeJA-induced growth-to-defense transition. BMC PLANT BIOLOGY 2021; 21:450. [PMID: 34615468 PMCID: PMC8493714 DOI: 10.1186/s12870-021-03185-1] [Citation(s) in RCA: 8] [Impact Index Per Article: 2.7] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 02/05/2021] [Accepted: 08/23/2021] [Indexed: 05/13/2023]
Abstract
BACKGROUND Methyl jasmonate (MeJA), which has been identified as a lipid-derived stress hormone, mediates plant resistance to biotic/abiotic stress. Understanding MeJA-induced plant defense provides insight into how they responding to environmental stimuli. RESULT In this work, the dynamic network analysis method was used to quantitatively identify the tipping point of growth-to-defense transition and detect the associated genes. As a result, 146 genes were detected as dynamic network biomarker (DNB) members and the critical defense transition was identified based on dense time-series RNA-seq data of MeJA-treated Arabidopsis thaliana. The GO functional analysis showed that these DNB genes were significantly enriched in defense terms. The network analysis between DNB genes and differentially expressed genes showed that the hub genes including SYP121, SYP122, WRKY33 and MPK11 play a vital role in plant growth-to-defense transition. CONCLUSIONS Based on the dynamic network analysis of MeJA-induced plant resistance, we provide an important guideline for understanding the growth-to-defense transition of plants' response to environment stimuli. This study also provides a database with the key genes of plant defense induced by MeJA.
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Affiliation(s)
- Tengfei Wang
- CAS Key Laboratory of Plant Germplasm Enhancement and Specialty Agriculture, Wuhan Botanical Garden, Chinese Academy of Sciences, 430074, Wuhan, China
- Center of Economic Botany, Core Botanical Gardens, Chinese Academy of Sciences, 430074, Wuhan, China
- University of Chinese Academy of Sciences, 100049, Beijing, China
| | - Xiujun Zhang
- CAS Key Laboratory of Plant Germplasm Enhancement and Specialty Agriculture, Wuhan Botanical Garden, Chinese Academy of Sciences, 430074, Wuhan, China.
- Center of Economic Botany, Core Botanical Gardens, Chinese Academy of Sciences, 430074, Wuhan, China.
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23
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Shang J, Wang J, Sun Y, Li F, Liu JX, Zhang H. Multiscale part mutual information for quantifying nonlinear direct associations in networks. Bioinformatics 2021; 37:2920-2929. [PMID: 33730153 DOI: 10.1093/bioinformatics/btab182] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/10/2020] [Revised: 02/15/2021] [Accepted: 03/15/2021] [Indexed: 02/02/2023] Open
Abstract
MOTIVATION For network-assisted analysis, which has become a popular method of data mining, network construction is a crucial task. Network construction relies on the accurate quantification of direct associations among variables. The existence of multiscale associations among variables presents several quantification challenges, especially when quantifying nonlinear direct interactions. RESULTS In this study, the multiscale part mutual information (MPMI), based on part mutual information (PMI) and nonlinear partial association (NPA), was developed for effectively quantifying nonlinear direct associations among variables in networks with multiscale associations. First, we defined the MPMI in theory and derived its five important properties. Second, an experiment in a three-node network was carried out to numerically estimate its quantification ability under two cases of strong associations. Third, experiments of the MPMI and comparisons with the PMI, NPA and conditional mutual information were performed on simulated datasets and on datasets from DREAM challenge project. Finally, the MPMI was applied to real datasets of glioblastoma and lung adenocarcinoma to validate its effectiveness. Results showed that the MPMI is an effective alternative measure for quantifying nonlinear direct associations in networks, especially those with multiscale associations. AVAILABILITY AND IMPLEMENTATION The source code of MPMI is available online at https://github.com/CDMB-lab/MPMI. SUPPLEMENTARY INFORMATION Supplementary data are available at Bioinformatics online.
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Affiliation(s)
- Junliang Shang
- School of Computer Science, Qufu Normal University, Rizhao 276826, China
| | - Jing Wang
- School of Computer Science, Qufu Normal University, Rizhao 276826, China
| | - Yan Sun
- School of Computer Science, Qufu Normal University, Rizhao 276826, China
| | - Feng Li
- School of Computer Science, Qufu Normal University, Rizhao 276826, China
| | - Jin-Xing Liu
- School of Computer Science, Qufu Normal University, Rizhao 276826, China
| | - Honghai Zhang
- College of Life Science, Qufu Normal University, Qufu 273165, China
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24
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Liu W, Jiang Y, Peng L, Sun X, Gan W, Zhao Q, Tang H. Inferring Gene Regulatory Networks Using the Improved Markov Blanket Discovery Algorithm. Interdiscip Sci 2021; 14:168-181. [PMID: 34495484 DOI: 10.1007/s12539-021-00478-9] [Citation(s) in RCA: 32] [Impact Index Per Article: 10.7] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/07/2021] [Revised: 08/22/2021] [Accepted: 08/24/2021] [Indexed: 11/26/2022]
Abstract
Inferring gene regulatory networks (GRNs) from microarray data can help us understand the mechanisms of life and eventually develop effective therapies. Currently, many computational methods have been used in inferring GRNs. However, owing to high-dimensional data and small samples, these methods often tend to introduce redundant regulatory relationships. Therefore, a novel network inference method based on the improved Markov blanket discovery algorithm, IMBDANET, is proposed to infer GRNs. Specifically, for each target gene, data processing inequality was applied to the Markov blanket discovery algorithm for the accurate differentiation of direct regulatory genes from indirect regulatory genes. Finally, direct regulatory genes were used in constructing GRNs, and the network structure was optimized according to the importance degree score. Experimental results on six public network datasets show that the proposed method can be effectively used to infer GRNs.
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Affiliation(s)
- Wei Liu
- School of Computer Science, Xiangtan University, Xiangtan, 411105, China
- Key Laboratory of Intelligent Computing and Information Processing of Ministry of Education, Xiangtan University, Xiangtan, 411105, China
| | - Yi Jiang
- School of Computer Science, Xiangtan University, Xiangtan, 411105, China
| | - Li Peng
- School of Computer Science and Engineering, Hunan University of Science and Technology, Xiangtan, 411201, China
| | - Xingen Sun
- School of Computer Science, Xiangtan University, Xiangtan, 411105, China
| | - Wenqing Gan
- School of Computer Science, Xiangtan University, Xiangtan, 411105, China
| | - Qi Zhao
- School of Computer Science and Software Engineering, University of Science and Technology Liaoning, Anshan, 114051, China.
| | - Huanrong Tang
- School of Computer Science, Xiangtan University, Xiangtan, 411105, China.
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25
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Zhang Q, Wang D, Han K, Huang DS. Predicting TF-DNA Binding Motifs from ChIP-seq Datasets Using the Bag-Based Classifier Combined With a Multi-Fold Learning Scheme. IEEE/ACM TRANSACTIONS ON COMPUTATIONAL BIOLOGY AND BIOINFORMATICS 2021; 18:1743-1751. [PMID: 32946398 DOI: 10.1109/tcbb.2020.3025007] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/11/2023]
Abstract
The rapid development of high-throughput sequencing technology provides unique opportunities for studying of transcription factor binding sites, but also brings new computational challenges. Recently, a series of discriminative motif discovery (DMD) methods have been proposed and offer promising solutions for addressing these challenges. However, because of the huge computation cost, most of them have to choose approximate schemes that either sacrifice the accuracy of motif representation or tune motif parameter indirectly. In this paper, we propose a bag-based classifier combined with a multi-fold learning scheme (BCMF) to discover motifs from ChIP-seq datasets. First, BCMF formulates input sequences as a labeled bag naturally. Then, a bag-based classifier, combining with a bag feature extracting strategy, is applied to construct the objective function, and a multi-fold learning scheme is used to solve it. Compared with the existing DMD tools, BCMF features three improvements: 1) Learning position weight matrix (PWM) directly in a continuous space; 2) Proposing to represent a positive bag with a feature fused by its k "most positive" patterns. 3) Applying a more advanced learning scheme. The experimental results on 134 ChIP-seq datasets show that BCMF substantially outperforms existing DMD methods (including DREME, HOMER, XXmotif, motifRG, EDCOD and our previous work).
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26
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Integrated Inference of Asymmetric Protein Interaction Networks Using Dynamic Model and Individual Patient Proteomics Data. Symmetry (Basel) 2021. [DOI: 10.3390/sym13061097] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/23/2023] Open
Abstract
Recent advances in experimental biology studies have produced large amount of molecular activity data. In particular, individual patient data provide non-time series information for the molecular activities in disease conditions. The challenge is how to design effective algorithms to infer regulatory networks using the individual patient datasets and consequently address the issue of network symmetry. This work is aimed at developing an efficient pipeline to reverse-engineer regulatory networks based on the individual patient proteomic data. The first step uses the SCOUT algorithm to infer the pseudo-time trajectory of individual patients. Then the path-consistent method with part mutual information is used to construct a static network that contains the potential protein interactions. To address the issue of network symmetry in terms of undirected symmetric network, a dynamic model of ordinary differential equations is used to further remove false interactions to derive asymmetric networks. In this work a dataset from triple-negative breast cancer patients is used to develop a protein-protein interaction network with 15 proteins.
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27
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Zhou Y, Wang S, Yan H, Pang B, Zhang X, Pang L, Wang Y, Xu J, Hu J, Lan Y, Ping Y. Identifying Key Somatic Copy Number Alterations Driving Dysregulation of Cancer Hallmarks in Lower-Grade Glioma. Front Genet 2021; 12:654736. [PMID: 34163522 PMCID: PMC8215700 DOI: 10.3389/fgene.2021.654736] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/17/2021] [Accepted: 04/26/2021] [Indexed: 01/17/2023] Open
Abstract
Somatic copy-number alterations (SCNAs) are major contributors to cancer development that are pervasive and highly heterogeneous in human cancers. However, the driver roles of SCNAs in cancer are insufficiently characterized. We combined network propagation and linear regression models to design an integrative strategy to identify driver SCNAs and dissect the functional roles of SCNAs by integrating profiles of copy number and gene expression in lower-grade glioma (LGG). We applied our strategy to 511 LGG patients and identified 98 driver genes that dysregulated 29 cancer hallmark signatures, forming 143 active gene-hallmark pairs. We found that these active gene-hallmark pairs could stratify LGG patients into four subtypes with significantly different survival times. The two new subtypes with similar poorest prognoses were driven by two different gene sets (one including EGFR, CDKN2A, CDKN2B, INFA8, and INFA5, and the other including CDK4, AVIL, and DTX3), respectively. The SCNAs of the two gene sets could disorder the same cancer hallmark signature in a mutually exclusive manner (including E2F_TARGETS and G2M_CHECKPOINT). Compared with previous methods, our strategy could not only capture the known cancer genes and directly dissect the functional roles of their SCNAs in LGG, but also discover the functions of new driver genes in LGG, such as IFNA5, IFNA8, and DTX3. Additionally, our method can be applied to a variety of cancer types to explore the pathogenesis of driver SCNAs and improve the treatment and diagnosis of cancer.
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Affiliation(s)
- Yao Zhou
- College of Bioinformatics Science and Technology, Harbin Medical University, Harbin, China
| | - Shuai Wang
- College of Bioinformatics Science and Technology, Harbin Medical University, Harbin, China
| | - Haoteng Yan
- College of Bioinformatics Science and Technology, Harbin Medical University, Harbin, China
| | - Bo Pang
- College of Bioinformatics Science and Technology, Harbin Medical University, Harbin, China
| | - Xinxin Zhang
- College of Bioinformatics Science and Technology, Harbin Medical University, Harbin, China
| | - Lin Pang
- College of Bioinformatics Science and Technology, Harbin Medical University, Harbin, China
| | - Yihan Wang
- College of Bioinformatics Science and Technology, Harbin Medical University, Harbin, China
| | - Jinyuan Xu
- College of Bioinformatics Science and Technology, Harbin Medical University, Harbin, China
| | - Jing Hu
- College of Bioinformatics Science and Technology, Harbin Medical University, Harbin, China
| | - Yujia Lan
- College of Bioinformatics Science and Technology, Harbin Medical University, Harbin, China
| | - Yanyan Ping
- College of Bioinformatics Science and Technology, Harbin Medical University, Harbin, China
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28
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Inferring and analyzing gene regulatory networks from multi-factorial expression data: a complete and interactive suite. BMC Genomics 2021; 22:387. [PMID: 34039282 PMCID: PMC8152307 DOI: 10.1186/s12864-021-07659-2] [Citation(s) in RCA: 23] [Impact Index Per Article: 7.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/17/2020] [Accepted: 04/28/2021] [Indexed: 11/29/2022] Open
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. Supplementary Information The online version contains supplementary material available at (10.1186/s12864-021-07659-2).
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29
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Mahmoodi SH, Aghdam R, Eslahchi C. An order independent algorithm for inferring gene regulatory network using quantile value for conditional independence tests. Sci Rep 2021; 11:7605. [PMID: 33828122 PMCID: PMC8027014 DOI: 10.1038/s41598-021-87074-5] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/24/2020] [Accepted: 03/24/2021] [Indexed: 10/31/2022] Open
Abstract
In recent years, due to the difficulty and inefficiency of experimental methods, numerous computational methods have been introduced for inferring the structure of Gene Regulatory Networks (GRNs). The Path Consistency (PC) algorithm is one of the popular methods to infer the structure of GRNs. However, this group of methods still has limitations and there is a potential for improvements in this field. For example, the PC-based algorithms are still sensitive to the ordering of nodes i.e. different node orders results in different network structures. The second is that the networks inferred by these methods are highly dependent on the threshold used for independence testing. Also, it is still a challenge to select the set of conditional genes in an optimal way, which affects the performance and computation complexity of the PC-based algorithm. We introduce a novel algorithm, namely Order Independent PC-based algorithm using Quantile value (OIPCQ), which improves the accuracy of the learning process of GRNs and solves the order dependency issue. The quantile-based thresholds are considered for different orders of CMI tests. For conditional gene selection, we consider the paths between genes with length equal or greater than 2 while other well-known PC-based methods only consider the paths of length 2. We applied OIPCQ on the various networks of the DREAM3 and DREAM4 in silico challenges. As a real-world case study, we used OIPCQ to reconstruct SOS DNA network obtained from Escherichia coli and GRN for acute myeloid leukemia based on the RNA sequencing data from The Cancer Genome Atlas. The results show that OIPCQ produces the same network structure for all the permutations of the genes and improves the resulted GRN through accurately quantifying the causal regulation strength in comparison with other well-known PC-based methods. According to the GRN constructed by OIPCQ, for acute myeloid leukemia, two regulators BCLAF1 and NRSF reported previously are significantly important. However, the highest degree nodes in this GRN are ZBTB7A and PU1 which play a significant role in cancer, especially in leukemia. OIPCQ is freely accessible at https://github.com/haammim/OIPCQ-and-OIPCQ2 .
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Affiliation(s)
- Sayyed Hadi Mahmoodi
- Department of Computer and Data Sciences, Faculty of Mathematical Sciences, Shahid Beheshti University, Tehran, Iran
| | - Rosa Aghdam
- Department of Computer and Data Sciences, Faculty of Mathematical Sciences, Shahid Beheshti University, Tehran, Iran. .,School of Biological Science, Institute for Research in Fundamental Sciences (IPM), Tehran, Iran.
| | - Changiz Eslahchi
- Department of Computer and Data Sciences, Faculty of Mathematical Sciences, Shahid Beheshti University, Tehran, Iran. .,School of Biological Science, Institute for Research in Fundamental Sciences (IPM), Tehran, Iran.
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30
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Zhang Y, Chang X, Liu X. Inference of gene regulatory networks using pseudo-time series data. Bioinformatics 2021; 37:2423-2431. [PMID: 33576787 DOI: 10.1093/bioinformatics/btab099] [Citation(s) in RCA: 12] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/04/2020] [Revised: 01/18/2021] [Accepted: 02/10/2021] [Indexed: 11/12/2022] Open
Abstract
MOTIVATION Inferring gene regulatory networks (GRNs) from high-throughput data is an important and challenging problem in systems biology. Although numerous GRN methods have been developed, most have focused on the verification of the specific data set. However, it is difficult to establish directed topological networks that are both suitable for time-series and non-time-series datasets due to the complexity and diversity of biological networks. RESULTS Here, we proposed a novel method, GNIPLR (Gene networks inference based on projection and lagged regression) to infer GRNs from time-series or non-time-series gene expression data. GNIPLR projected gene data twice using the LASSO projection (LSP) algorithm and the linear projection (LP) approximation to produce a linear and monotonous pseudo-time series, and then determined the direction of regulation in combination with lagged regression analyses. The proposed algorithm was validated using simulated and real biological data. Moreover, we also applied the GNIPLR algorithm to the liver hepatocellular carcinoma (LIHC) and bladder urothelial carcinoma (BLCA) cancer expression datasets. These analyses revealed significantly higher accuracy and AUC values than other popular methods. AVAILABILITY The GNIPLR tool is freely available at https://github.com/zyllluck/GNIPLR. SUPPLEMENTARY INFORMATION Supplementary data are available at Bioinformatics online.
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Affiliation(s)
- Yuelei Zhang
- Hangzhou Institute for Advanced Study, University of Chinese Academy of Sciences, Hangzhou, 310012, China.,Institute of Statistics and Applied Mathematics, Anhui University of Finance and Economics, Bengbu, 233030, China.,School of Mathematics and Statistics, Shandong University, Weihai, Shandong, 264209, China
| | - Xiao Chang
- Institute of Statistics and Applied Mathematics, Anhui University of Finance and Economics, Bengbu, 233030, China
| | - Xiaoping Liu
- Hangzhou Institute for Advanced Study, University of Chinese Academy of Sciences, Hangzhou, 310012, China.,School of Mathematics and Statistics, Shandong University, Weihai, Shandong, 264209, China
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31
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Zhao M, He W, Tang J, Zou Q, Guo F. A comprehensive overview and critical evaluation of gene regulatory network inference technologies. Brief Bioinform 2021; 22:6128842. [PMID: 33539514 DOI: 10.1093/bib/bbab009] [Citation(s) in RCA: 27] [Impact Index Per Article: 9.0] [Reference Citation Analysis] [Abstract] [Key Words] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/28/2020] [Revised: 12/11/2020] [Accepted: 01/06/2021] [Indexed: 12/12/2022] Open
Abstract
Gene regulatory network (GRN) is the important mechanism of maintaining life process, controlling biochemical reaction and regulating compound level, which plays an important role in various organisms and systems. Reconstructing GRN can help us to understand the molecular mechanism of organisms and to reveal the essential rules of a large number of biological processes and reactions in organisms. Various outstanding network reconstruction algorithms use specific assumptions that affect prediction accuracy, in order to deal with the uncertainty of processing. In order to study why a certain method is more suitable for specific research problem or experimental data, we conduct research from model-based, information-based and machine learning-based method classifications. There are obviously different types of computational tools that can be generated to distinguish GRNs. Furthermore, we discuss several classical, representative and latest methods in each category to analyze core ideas, general steps, characteristics, etc. We compare the performance of state-of-the-art GRN reconstruction technologies on simulated networks and real networks under different scaling conditions. Through standardized performance metrics and common benchmarks, we quantitatively evaluate the stability of various methods and the sensitivity of the same algorithm applying to different scaling networks. The aim of this study is to explore the most appropriate method for a specific GRN, which helps biologists and medical scientists in discovering potential drug targets and identifying cancer biomarkers.
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Affiliation(s)
- Mengyuan Zhao
- School of Computer Science and Technology, College of Intelligence and Computing, Tianjin University, Tianjin, China
| | - Wenying He
- School of Computer Science and Technology, College of Intelligence and Computing, Tianjin University, Tianjin, China
| | - Jijun Tang
- University of South Carolina, Tianjin, China
| | - Quan Zou
- Institute of Fundamental and Frontier Sciences, University of Electronic Science and Technology of China, Chengdu, China
| | - Fei Guo
- School of Computer Science and Technology, College of Intelligence and Computing, Tianjin University, Tianjin, China
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32
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Zhang XF, Ou-Yang L, Yan T, Hu XT, Yan H. A Joint Graphical Model for Inferring Gene Networks Across Multiple Subpopulations and Data Types. IEEE TRANSACTIONS ON CYBERNETICS 2021; 51:1043-1055. [PMID: 31794418 DOI: 10.1109/tcyb.2019.2952711] [Citation(s) in RCA: 7] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/10/2023]
Abstract
Reconstructing gene networks from gene expression data is a long-standing challenge. In most applications, the observations can be divided into several distinct but related subpopulations and the gene expression measurements can be collected from multiple data types. Most existing methods are designed to estimate a single gene network from a single dataset. These methods may be suboptimal since they do not exploit the similarities and differences among different subpopulations and data types. In this article, we propose a joint graphical model to estimate the multiple gene networks simultaneously. Our model decomposes each subpopulation-specific gene network as a sum of common and unique components and imposes a group lasso penalty on gene networks corresponding to different data types. The gene network variations across subpopulations can be learned automatically by the decompositions of networks, and the similarities and differences among data types can be captured by the group lasso penalty. The simulation studies demonstrate that our method outperforms the state-of-the-art methods. We also apply our method to the cancer genome atlas breast cancer datasets to reconstruct subtype-specific gene networks. Hub nodes in the estimated subnetworks unique to individual cancer subtypes rediscover well-known genes associated with breast cancer subtypes and provide interesting predictions.
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33
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A Data-Driven and Data-Based Framework for Online Voltage Stability Assessment Using Partial Mutual Information and Iterated Random Forest. ENERGIES 2021. [DOI: 10.3390/en14030715] [Citation(s) in RCA: 9] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 11/16/2022]
Abstract
Due to the rapid development of phasor measurement units (PMUs) and the wide area of interconnection of modern power systems, the security of power systems is confronted with severe challenges. A novel framework based on data for static voltage stability margin (VSM) assessment of power systems is presented. The proposed framework can select the key operation variables as input features for the assessment based on partial mutual information (PMI). Before the feature selection procedure is completed by PMI, a feature preprocessing approach is applied to remove redundant and irrelevant features to improve computational efficiency. Using the selected key variables, a voltage stability assessment (VSA) model based on iterated random forest (IRF) can rapidly provide the relative VSM results. The proposed framework is examined on the IEEE 30-bus system and a practical 1648-bus system, and a desirable assessment performance is demonstrated. In addition, the robustness and computational speed of the proposed framework are also verified. Some impact factors for power system operation are studied in a robustness examination, such as topology change, variation of peak/minimum load, and variation of generator/load power distribution.
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34
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Hütt MT, Lesne A. Gene Regulatory Networks: Dissecting Structure and Dynamics. SYSTEMS MEDICINE 2021. [DOI: 10.1016/b978-0-12-801238-3.11467-9] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/25/2022] Open
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35
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Zheng R, Li M, Chen X, Zhao S, Wu FX, Pan Y, Wang J. An Ensemble Method to Reconstruct Gene Regulatory Networks Based on Multivariate Adaptive Regression Splines. IEEE/ACM TRANSACTIONS ON COMPUTATIONAL BIOLOGY AND BIOINFORMATICS 2021; 18:347-354. [PMID: 30794516 DOI: 10.1109/tcbb.2019.2900614] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/09/2023]
Abstract
Gene regulatory networks (GRNs) play a key role in biological processes. However, GRNs are diverse under different biological conditions. Reconstructing gene regulatory networks (GRNs) from gene expression has become an important opportunity and challenge in the past decades. Although there are a lot of existing methods to infer the topology of GRNs, such as mutual information, random forest, and partial least squares, the accuracy is still low due to the noise and high dimension of the expression data. In this paper, we introduce an ensemble Multivariate Adaptive Regression Splines (MARS) based method to reconstruct the directed GRNs from multifactorial gene expression data, called PBMarsNet. PBMarsNet incorporates part mutual information (PMI) to pre-weight the candidate regulatory genes and then uses MARS to detect the nonlinear regulatory links. Moreover, we apply bootstrap to run the MARS multiple times and average the outputs of each MARS as the final score of regulatory links. The results on DREAM4 challenge and DREAM5 challenge datasets show PBMarsNet has a superior performance and generalization over other state-of-the-art methods.
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Dai H, Jin QQ, Li L, Chen LN. Reconstructing gene regulatory networks in single-cell transcriptomic data analysis. Zool Res 2020; 41:599-604. [PMID: 33124218 PMCID: PMC7671911 DOI: 10.24272/j.issn.2095-8137.2020.215] [Citation(s) in RCA: 6] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/03/2020] [Accepted: 10/20/2020] [Indexed: 11/07/2022] Open
Abstract
Gene regulatory networks play pivotal roles in our understanding of biological processes/mechanisms at the molecular level. Many studies have developed sample-specific or cell-type-specific gene regulatory networks from single-cell transcriptomic data based on a large amount of cell samples. Here, we review the state-of-the-art computational algorithms and describe various applications of gene regulatory networks in biological studies.
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Affiliation(s)
- Hao Dai
- Key Laboratory of Systems Biology, Shanghai Institute of Biochemistry and Cell Biology, Center for Excellence in Molecular Cell Science, Chinese Academy of Sciences, Shanghai 200031, China
- Institute of Brain-Intelligence Technology, Zhangjiang Laboratory, Shanghai 201210, China
| | - Qi-Qi Jin
- Key Laboratory of Systems Biology, Shanghai Institute of Biochemistry and Cell Biology, Center for Excellence in Molecular Cell Science, Chinese Academy of Sciences, Shanghai 200031, China
- University of Chinese Academy of Sciences, Beijing 100049, China
- School of Life Science and Technology, ShanghaiTech University, Shanghai 201210, China
| | - Lin Li
- Key Laboratory of Systems Biology, Shanghai Institute of Biochemistry and Cell Biology, Center for Excellence in Molecular Cell Science, Chinese Academy of Sciences, Shanghai 200031, China
- University of Chinese Academy of Sciences, Beijing 100049, China
| | - Luo-Nan Chen
- Key Laboratory of Systems Biology, Shanghai Institute of Biochemistry and Cell Biology, Center for Excellence in Molecular Cell Science, Chinese Academy of Sciences, Shanghai 200031, China
- School of Life Science and Technology, ShanghaiTech University, Shanghai 201210, China
- Key Laboratory of Systems Biology, Hangzhou Institute for Advanced Study, University of Chinese Academy of Sciences, Chinese Academy of Sciences, Hangzhou, Zhejiang 310024, China
- Center for Excellence in Animal Evolution and Genetics, Chinese Academy of Sciences, Kunming, Yunnan 650223, China
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37
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Liu W, Sun X, Peng L, Zhou L, Lin H, Jiang Y. RWRNET: A Gene Regulatory Network Inference Algorithm Using Random Walk With Restart. Front Genet 2020; 11:591461. [PMID: 33101398 PMCID: PMC7545090 DOI: 10.3389/fgene.2020.591461] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/04/2020] [Accepted: 09/02/2020] [Indexed: 11/30/2022] Open
Abstract
Inferring gene regulatory networks from expression data is essential in identifying complex regulatory relationships among genes and revealing the mechanism of certain diseases. Various computation methods have been developed for inferring gene regulatory networks. However, these methods focus on the local topology of the network rather than on the global topology. From network optimisation standpoint, emphasising the global topology of the network also reduces redundant regulatory relationships. In this study, we propose a novel network inference algorithm using Random Walk with Restart (RWRNET) that combines local and global topology relationships. The method first captures the local topology through three elements of random walk and then combines the local topology with the global topology by Random Walk with Restart. The Markov Blanket discovery algorithm is then used to deal with isolated genes. The proposed method is compared with several state-of-the-art methods on the basis of six benchmark datasets. Experimental results demonstrated the effectiveness of the proposed method.
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Affiliation(s)
- Wei Liu
- School of Computer Science, Xiangtan University, Xiangtan, China.,Key Laboratory of Intelligent Computing and Information Processing of Ministry of Education, Xiangtan University, Xiangtan, China
| | - Xingen Sun
- School of Computer Science, Xiangtan University, Xiangtan, China
| | - Li Peng
- School of Computer Science and Engineering, Hunan University of Science and Technology, Xiangtan, China
| | - Lili Zhou
- School of Computer Science, Xiangtan University, Xiangtan, China
| | - Hui Lin
- School of Computer Science, Xiangtan University, Xiangtan, China
| | - Yi Jiang
- School of Computer Science, Xiangtan University, Xiangtan, China
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38
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Shi M, Tan S, Xie XP, Li A, Yang W, Zhu T, Wang HQ. Globally learning gene regulatory networks based on hidden atomic regulators from transcriptomic big data. BMC Genomics 2020; 21:711. [PMID: 33054712 PMCID: PMC7559338 DOI: 10.1186/s12864-020-07079-8] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/02/2020] [Accepted: 09/18/2020] [Indexed: 12/02/2022] Open
Abstract
Background Genes are regulated by various types of regulators and most of them are still unknown or unobserved. Current gene regulatory networks (GRNs) reverse engineering methods often neglect the unknown regulators and infer regulatory relationships in a local and sub-optimal manner. Results This paper proposes a global GRNs inference framework based on dictionary learning, named dlGRN. The method intends to learn atomic regulators (ARs) from gene expression data using a modified dictionary learning (DL) algorithm, which reflects the whole gene regulatory system, and predicts the regulation between a known regulator and a target gene in a global regression way. The modified DL algorithm fits the scale-free property of biological network, rendering dlGRN intrinsically discern direct and indirect regulations. Conclusions Extensive experimental results on simulation and real-world data demonstrate the effectiveness and efficiency of dlGRN in reverse engineering GRNs. A novel predicted transcription regulation between a TF TFAP2C and an oncogene EGFR was experimentally verified in lung cancer cells. Furthermore, the real application reveals the prevalence of DNA methylation regulation in gene regulatory system. dlGRN can be a standalone tool for GRN inference for its globalization and robustness.
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Affiliation(s)
- Ming Shi
- MICB Laboratory, Institute of Intelligent Machines, Hefei Institutes of Physical Science, CAS, 350 Shushanghu Road, Hefei, Anhui, 230031, P. R. China.,Current Address: MOE Key Laboratory of Bioinformatics, Division of Bioinformatics and Center for Synthetic and Systems Biology, TNLIST, Department of Automation, Tsinghua University, Beijing, 100084, China
| | - Sheng Tan
- The CAS Key Laboratory of Innate Immunity and Chronic Disease, Division of Life Sciences and Medicine, University of Science and Technology of China, 96 Jinzhai Road, Hefei, Anhui, 230026, P. R. China
| | - Xin-Ping Xie
- School of Mathematics and Physics, Anhui Jianzhu University, 856 Jinzhai Road, Hefei, Anhui, 230022, P. R. China
| | - Ao Li
- School of Information Science and Technology, University of Science and Technology of China, 96 Jinzhai Road, Hefei, Anhui, 230026, P. R. China
| | - Wulin Yang
- Cancer hospital & Anhui Province Key Laboratory of Medical Physics and Technology, Center of Medical Physics and Technology, Hefei Institutes of Physical Science, CAS, 350 Shushanghu Road, Hefei, Anhui, 230031, P. R. China
| | - Tao Zhu
- Current Address: MOE Key Laboratory of Bioinformatics, Division of Bioinformatics and Center for Synthetic and Systems Biology, TNLIST, Department of Automation, Tsinghua University, Beijing, 100084, China.
| | - Hong-Qiang Wang
- MICB Laboratory, Institute of Intelligent Machines, Hefei Institutes of Physical Science, CAS, 350 Shushanghu Road, Hefei, Anhui, 230031, P. R. China. .,Cancer hospital & Anhui Province Key Laboratory of Medical Physics and Technology, Center of Medical Physics and Technology, Hefei Institutes of Physical Science, CAS, 350 Shushanghu Road, Hefei, Anhui, 230031, P. R. China.
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39
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Sun Y, Li C, Pang S, Yao Q, Chen L, Li Y, Zeng R. Kinase-substrate Edge Biomarkers Provide a More Accurate Prognostic Prediction in ER-negative Breast Cancer. GENOMICS, PROTEOMICS & BIOINFORMATICS 2020; 18:525-538. [PMID: 33450402 PMCID: PMC8377385 DOI: 10.1016/j.gpb.2019.11.012] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 09/19/2018] [Revised: 08/27/2019] [Accepted: 11/11/2019] [Indexed: 11/19/2022]
Abstract
The estrogen receptor (ER)-negative breast cancer subtype is aggressive with few treatment options available. To identify specific prognostic factors for ER-negative breast cancer, this study included 705,729 and 1034 breast invasive cancer patients from the Surveillance, Epidemiology, and End Results (SEER) and The Cancer Genome Atlas (TCGA) databases, respectively. To identify key differential kinase-substrate node and edge biomarkers between ER-negative and ER-positive breast cancer patients, we adopted a network-based method using correlation coefficients between molecular pairs in the kinase regulatory network. Integrated analysis of the clinical and molecular data revealed the significant prognostic power of kinase-substrate node and edge features for both subtypes of breast cancer. Two promising kinase-substrate edge features, CSNK1A1-NFATC3 and SRC-OCLN, were identified for more accurate prognostic prediction in ER-negative breast cancer patients.
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Affiliation(s)
- Yidi Sun
- CAS Key Laboratory of Systems Biology, CAS Center for Excellence in Molecular Cell Science, Institute of Biochemistry and Cell Biology, Shanghai Institutes for Biological Sciences, Chinese Academy of Sciences, Shanghai 200031, China; University of Chinese Academy of Sciences, Shanghai 200031, China
| | - Chen Li
- CAS Key Laboratory of Systems Biology, CAS Center for Excellence in Molecular Cell Science, Institute of Biochemistry and Cell Biology, Shanghai Institutes for Biological Sciences, Chinese Academy of Sciences, Shanghai 200031, China
| | - Shichao Pang
- Deptartment of Statistics, School of Mathematical Sciences, Shanghai Jiao Tong University, Shanghai 200240, China
| | - Qianlan Yao
- School of Life Sciences and Biotechnology, Shanghai Jiao Tong University, Shanghai 200240, China
| | - Luonan Chen
- CAS Key Laboratory of Systems Biology, CAS Center for Excellence in Molecular Cell Science, Institute of Biochemistry and Cell Biology, Shanghai Institutes for Biological Sciences, Chinese Academy of Sciences, Shanghai 200031, China; Department of Life Sciences, ShanghaiTech University, Shanghai 201210, China; CAS Center for Excellence in Animal Evolution and Genetics, Chinese Academy of Sciences, Kunming 650223, China.
| | - Yixue Li
- School of Life Sciences and Biotechnology, Shanghai Jiao Tong University, Shanghai 200240, China; Department of Life Sciences, ShanghaiTech University, Shanghai 201210, China; Bio-Med Big Data Center, Key Laboratory of Computational Biology, CAS-MPG Partner Institute for Computational Biology, Shanghai Institute of Nutrition and Health, Shanghai Institutes for Biological Sciences, Chinese Academy of Sciences, Shanghai 200031, China; Collaborative Innovation Center for Genetics and Development, Fudan University, Shanghai 200032, China; Shanghai Center for Bioinformation Technology, Shanghai Academy of Science & Technology, Shanghai 201203, China.
| | - Rong Zeng
- CAS Key Laboratory of Systems Biology, CAS Center for Excellence in Molecular Cell Science, Institute of Biochemistry and Cell Biology, Shanghai Institutes for Biological Sciences, Chinese Academy of Sciences, Shanghai 200031, China; Department of Life Sciences, ShanghaiTech University, Shanghai 201210, China.
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41
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NetExtractor: Extracting a Cerebellar Tissue Gene Regulatory Network Using Differentially Expressed High Mutual Information Binary RNA Profiles. G3-GENES GENOMES GENETICS 2020; 10:2953-2963. [PMID: 32665353 PMCID: PMC7466957 DOI: 10.1534/g3.120.401067] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Indexed: 11/18/2022]
Abstract
Bigenic expression relationships are conventionally defined based on metrics such as Pearson or Spearman correlation that cannot typically detect latent, non-linear dependencies or require the relationship to be monotonic. Further, the combination of intrinsic and extrinsic noise as well as embedded relationships between sample sub-populations reduces the probability of extracting biologically relevant edges during the construction of gene co-expression networks (GCNs). In this report, we address these problems via our NetExtractor algorithm. NetExtractor examines all pairwise gene expression profiles first with Gaussian mixture models (GMMs) to identify sample sub-populations followed by mutual information (MI) analysis that is capable of detecting non-linear differential bigenic expression relationships. We applied NetExtractor to brain tissue RNA profiles from the Genotype-Tissue Expression (GTEx) project to obtain a brain tissue specific gene expression relationship network centered on cerebellar and cerebellar hemisphere enriched edges. We leveraged the PsychENCODE pre-frontal cortex (PFC) gene regulatory network (GRN) to construct a cerebellar cortex (cerebellar) GRN associated with transcriptionally active regions in cerebellar tissue. Thus, we demonstrate the utility of our NetExtractor approach to detect biologically relevant and novel non-linear binary gene relationships.
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42
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Shi Y, Pan Y, Xu D, Tsang IW. Multiview Alignment and Generation in CCA via Consistent Latent Encoding. Neural Comput 2020; 32:1936-1979. [PMID: 32795232 DOI: 10.1162/neco_a_01309] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/04/2022]
Abstract
Multiview alignment, achieving one-to-one correspondence of multiview inputs, is critical in many real-world multiview applications, especially for cross-view data analysis problems. An increasing amount of work has studied this alignment problem with canonical correlation analysis (CCA). However, existing CCA models are prone to misalign the multiple views due to either the neglect of uncertainty or the inconsistent encoding of the multiple views. To tackle these two issues, this letter studies multiview alignment from a Bayesian perspective. Delving into the impairments of inconsistent encodings, we propose to recover correspondence of the multiview inputs by matching the marginalization of the joint distribution of multiview random variables under different forms of factorization. To realize our design, we present adversarial CCA (ACCA), which achieves consistent latent encodings by matching the marginalized latent encodings through the adversarial training paradigm. Our analysis, based on conditional mutual information, reveals that ACCA is flexible for handling implicit distributions. Extensive experiments on correlation analysis and cross-view generation under noisy input settings demonstrate the superiority of our model.
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Affiliation(s)
- Yaxin Shi
- Australian Artificial Intelligence Institute, University of Technology Sydney NSW 2007, Australia
| | - Yuangang Pan
- Australian Artificial Intelligence Institute, University of Technology Sydney NSW 2007, Australia
| | - Donna Xu
- Australian Artificial Intelligence Institute, University of Technology Sydney NSW 2007, Australia
| | - Ivor W Tsang
- Australian Artificial Intelligence Institute, University of Technology Sydney NSW 2007, Australia
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43
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Malekpour SA, Alizad-Rahvar AR, Sadeghi M. LogicNet: probabilistic continuous logics in reconstructing gene regulatory networks. BMC Bioinformatics 2020; 21:318. [PMID: 32690031 PMCID: PMC7372900 DOI: 10.1186/s12859-020-03651-x] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/16/2019] [Accepted: 07/10/2020] [Indexed: 11/10/2022] Open
Abstract
Background Gene Regulatory Networks (GRNs) have been previously studied by using Boolean/multi-state logics. While the gene expression values are usually scaled into the range [0, 1], these GRN inference methods apply a threshold to discretize the data, resulting in missing information. Most of studies apply fuzzy logics to infer the logical gene-gene interactions from continuous data. However, all these approaches require an a priori known network structure. Results Here, by introducing a new probabilistic logic for continuous data, we propose a novel logic-based approach (called the LogicNet) for the simultaneous reconstruction of the GRN structure and identification of the logics among the regulatory genes, from the continuous gene expression data. In contrast to the previous approaches, the LogicNet does not require an a priori known network structure to infer the logics. The proposed probabilistic logic is superior to the existing fuzzy logics and is more relevant to the biological contexts than the fuzzy logics. The performance of the LogicNet is superior to that of several Mutual Information-based and regression-based tools for reconstructing GRNs. Conclusions The LogicNet reconstructs GRNs and logic functions without requiring prior knowledge of the network structure. Moreover, in another application, the LogicNet can be applied for logic function detection from the known regulatory genes-target interactions. We also conclude that computational modeling of the logical interactions among the regulatory genes significantly improves the GRN reconstruction accuracy.
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Affiliation(s)
- Seyed Amir Malekpour
- School of Biological Sciences, Institute for Research in Fundamental Sciences (IPM), Tehran, Iran.
| | - Amir Reza Alizad-Rahvar
- School of Biological Sciences, Institute for Research in Fundamental Sciences (IPM), Tehran, Iran
| | - Mehdi Sadeghi
- National Institute of Genetic Engineering and Biotechnology, Tehran, Iran
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44
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Development of Stock Networks Using Part Mutual Information and Australian Stock Market Data. ENTROPY 2020; 22:e22070773. [PMID: 33286545 PMCID: PMC7517323 DOI: 10.3390/e22070773] [Citation(s) in RCA: 11] [Impact Index Per Article: 2.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 06/25/2020] [Revised: 07/12/2020] [Accepted: 07/13/2020] [Indexed: 01/07/2023]
Abstract
Complex network is a powerful tool to discover important information from various types of big data. Although substantial studies have been conducted for the development of stock relation networks, correlation coefficient is dominantly used to measure the relationship between stock pairs. Information theory is much less discussed for this important topic, though mutual information is able to measure nonlinear pairwise relationship. In this work we propose to use part mutual information for developing stock networks. The path-consistency algorithm is used to filter out redundant relationships. Using the Australian stock market data, we develop four stock relation networks using different orders of part mutual information. Compared with the widely used planar maximally filtered graph (PMFG), we can generate networks with cliques of large size. In addition, the large cliques show consistency with the structure of industrial sectors. We also analyze the connectivity and degree distributions of the generated networks. Analysis results suggest that the proposed method is an effective approach to develop stock relation networks using information theory.
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45
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Che D, Guo S, Jiang Q, Chen L. PFBNet: a priori-fused boosting method for gene regulatory network inference. BMC Bioinformatics 2020; 21:308. [PMID: 32664870 PMCID: PMC7362553 DOI: 10.1186/s12859-020-03639-7] [Citation(s) in RCA: 8] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/29/2019] [Accepted: 07/02/2020] [Indexed: 02/08/2023] Open
Abstract
BACKGROUND Inferring gene regulatory networks (GRNs) from gene expression data remains a challenge in system biology. In past decade, numerous methods have been developed for the inference of GRNs. It remains a challenge due to the fact that the data is noisy and high dimensional, and there exists a large number of potential interactions. RESULTS We present a novel method, namely priori-fused boosting network inference method (PFBNet), to infer GRNs from time-series expression data by using the non-linear model of Boosting and the prior information (e.g., the knockout data) fusion scheme. Specifically, PFBNet first calculates the confidences of the regulation relationships using the boosting-based model, where the information about the accumulation impact of the gene expressions at previous time points is taken into account. Then, a newly defined strategy is applied to fuse the information from the prior data by elevating the confidences of the regulation relationships from the corresponding regulators. CONCLUSIONS The experiments on the benchmark datasets from DREAM challenge as well as the E.coli datasets show that PFBNet achieves significantly better performance than other state-of-the-art methods (Jump3, GEINE3-lag, HiDi, iRafNet and BiXGBoost).
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Affiliation(s)
- Dandan Che
- Shenzhen Key Lab for High Performance Data Mining, Shenzhen Institutes of Advanced Technology, Chinese Academy of Sciences, Shenzhen, 518000 China
| | - Shun Guo
- Shenzhen Key Lab for High Performance Data Mining, Shenzhen Institutes of Advanced Technology, Chinese Academy of Sciences, Shenzhen, 518000 China
| | - Qingshan Jiang
- Shenzhen Key Lab for High Performance Data Mining, Shenzhen Institutes of Advanced Technology, Chinese Academy of Sciences, Shenzhen, 518000 China
| | - Lifei Chen
- School of Mathematics and Computer Science, Fujian Normal University, Fujian, 350117 China
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46
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Corso M, Perreau F, Mouille G, Lepiniec L. Specialized phenolic compounds in seeds: structures, functions, and regulations. PLANT SCIENCE : AN INTERNATIONAL JOURNAL OF EXPERIMENTAL PLANT BIOLOGY 2020; 296:110471. [PMID: 32540001 DOI: 10.1016/j.plantsci.2020.110471] [Citation(s) in RCA: 38] [Impact Index Per Article: 9.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 10/07/2019] [Revised: 03/11/2020] [Accepted: 03/13/2020] [Indexed: 05/24/2023]
Abstract
Plants produce a huge diversity of specialized metabolites (SM) throughout their life cycle that play important physiological and ecological functions. SM can protect plants and seeds against diseases, predators, and abiotic stresses, or support their interactions with beneficial or symbiotic organisms. They also have strong impacts on human nutrition and health. Despite this importance, the biosynthesis and biological functions of most of the SM remain elusive and their diversity and/or quantity have been reduced in most crops during domestication. Seeds present a large number of SM that are important for their physiological, agronomic, nutritional or industrial qualities and hence, provide interesting models for both studying biosynthesis and producing large amounts of specialized metabolites. For instance, phenolics are abundant and widely distributed in seeds. More specifically, flavonoid pathway has been instrumental for understanding environmental or developmental regulations of specialized metabolic pathways, at the molecular and cellular levels. Here, we summarize current knowledge on seed phenolics as model, and discuss how recent progresses in omics approaches could help to further characterize their diversity, regulations, and the underlying molecular mechanisms involved.
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Affiliation(s)
- Massimiliano Corso
- Institut Jean-Pierre Bourgin, Université Paris-Saclay, INRAE, AgroParisTech, 78000, Versailles, France.
| | - François Perreau
- Institut Jean-Pierre Bourgin, Université Paris-Saclay, INRAE, AgroParisTech, 78000, Versailles, France
| | - Grégory Mouille
- Institut Jean-Pierre Bourgin, Université Paris-Saclay, INRAE, AgroParisTech, 78000, Versailles, France
| | - Loïc Lepiniec
- Institut Jean-Pierre Bourgin, Université Paris-Saclay, INRAE, AgroParisTech, 78000, Versailles, France
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47
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Wu S, Cui T, Zhang X, Tian T. A non-linear reverse-engineering method for inferring genetic regulatory networks. PeerJ 2020; 8:e9065. [PMID: 32391205 PMCID: PMC7195839 DOI: 10.7717/peerj.9065] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/06/2020] [Accepted: 04/05/2020] [Indexed: 12/19/2022] Open
Abstract
Hematopoiesis is a highly complex developmental process that produces various types of blood cells. This process is regulated by different genetic networks that control the proliferation, differentiation, and maturation of hematopoietic stem cells (HSCs). Although substantial progress has been made for understanding hematopoiesis, the detailed regulatory mechanisms for the fate determination of HSCs are still unraveled. In this study, we propose a novel approach to infer the detailed regulatory mechanisms. This work is designed to develop a mathematical framework that is able to realize nonlinear gene expression dynamics accurately. In particular, we intended to investigate the effect of possible protein heterodimers and/or synergistic effect in genetic regulation. This approach includes the Extended Forward Search Algorithm to infer network structure (top-down approach) and a non-linear mathematical model to infer dynamical property (bottom-up approach). Based on the published experimental data, we study two regulatory networks of 11 genes for regulating the erythrocyte differentiation pathway and the neutrophil differentiation pathway. The proposed algorithm is first applied to predict the network topologies among 11 genes and 55 non-linear terms which may be for heterodimers and/or synergistic effect. Then, the unknown model parameters are estimated by fitting simulations to the expression data of two different differentiation pathways. In addition, the edge deletion test is conducted to remove possible insignificant regulations from the inferred networks. Furthermore, the robustness property of the mathematical model is employed as an additional criterion to choose better network reconstruction results. Our simulation results successfully realized experimental data for two different differentiation pathways, which suggests that the proposed approach is an effective method to infer the topological structure and dynamic property of genetic regulations.
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Affiliation(s)
- Siyuan Wu
- School of Mathematics, Monash University, Clayton, VIC, Australia
| | - Tiangang Cui
- School of Mathematics, Monash University, Clayton, VIC, Australia
| | - Xinan Zhang
- School of Mathematics and Statistics, Central China Normal University, Wuhan, PR China
| | - Tianhai Tian
- School of Mathematics, Monash University, Clayton, VIC, Australia
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48
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Silverman EK, Schmidt HHHW, Anastasiadou E, Altucci L, Angelini M, Badimon L, Balligand JL, Benincasa G, Capasso G, Conte F, Di Costanzo A, Farina L, Fiscon G, Gatto L, Gentili M, Loscalzo J, Marchese C, Napoli C, Paci P, Petti M, Quackenbush J, Tieri P, Viggiano D, Vilahur G, Glass K, Baumbach J. Molecular networks in Network Medicine: Development and applications. WILEY INTERDISCIPLINARY REVIEWS-SYSTEMS BIOLOGY AND MEDICINE 2020; 12:e1489. [PMID: 32307915 DOI: 10.1002/wsbm.1489] [Citation(s) in RCA: 113] [Impact Index Per Article: 28.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 11/23/2019] [Revised: 02/29/2020] [Accepted: 03/20/2020] [Indexed: 12/14/2022]
Abstract
Network Medicine applies network science approaches to investigate disease pathogenesis. Many different analytical methods have been used to infer relevant molecular networks, including protein-protein interaction networks, correlation-based networks, gene regulatory networks, and Bayesian networks. Network Medicine applies these integrated approaches to Omics Big Data (including genetics, epigenetics, transcriptomics, metabolomics, and proteomics) using computational biology tools and, thereby, has the potential to provide improvements in the diagnosis, prognosis, and treatment of complex diseases. We discuss briefly the types of molecular data that are used in molecular network analyses, survey the analytical methods for inferring molecular networks, and review efforts to validate and visualize molecular networks. Successful applications of molecular network analysis have been reported in pulmonary arterial hypertension, coronary heart disease, diabetes mellitus, chronic lung diseases, and drug development. Important knowledge gaps in Network Medicine include incompleteness of the molecular interactome, challenges in identifying key genes within genetic association regions, and limited applications to human diseases. This article is categorized under: Models of Systems Properties and Processes > Mechanistic Models Translational, Genomic, and Systems Medicine > Translational Medicine Analytical and Computational Methods > Analytical Methods Analytical and Computational Methods > Computational Methods.
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Affiliation(s)
- Edwin K Silverman
- Channing Division of Network Medicine, Department of Medicine, Brigham and Women's Hospital and Harvard Medical School, Boston, Massachusetts, USA
| | - Harald H H W Schmidt
- Department of Pharmacology and Personalized Medicine, School of Mental Health and Neuroscience, Faculty of Health, Medicine and Life Science, Maastricht University, Maastricht, The Netherlands
| | - Eleni Anastasiadou
- Department of Experimental Medicine, Sapienza University of Rome, Rome, Italy
| | - Lucia Altucci
- Department of Precision Medicine, University of Campania 'Luigi Vanvitelli', Naples, Italy
| | - Marco Angelini
- Department of Computer, Control and Management Engineering, Sapienza University of Rome, Rome, Italy
| | - Lina Badimon
- Cardiovascular Program-ICCC, IR-Hospital de la Santa Creu i Sant Pau, CiberCV, IIB-Sant Pau, Autonomous University of Barcelona, Barcelona, Spain
| | - Jean-Luc Balligand
- Pole of Pharmacology and Therapeutics (FATH), Institute for Clinical and Experimental Research (IREC), UCLouvain, Brussels, Belgium
| | - Giuditta Benincasa
- Department of Advanced Clinical and Surgical Sciences, University of Campania "Luigi Vanvitelli", Naples, Italy
| | - Giovambattista Capasso
- Department of Translational Medical Sciences, University of Campania "L. Vanvitelli", Naples, Italy.,BIOGEM, Ariano Irpino, Italy
| | - Federica Conte
- Institute for Systems Analysis and Computer Science "Antonio Ruberti", National Research Council, Rome, Italy
| | - Antonella Di Costanzo
- Department of Precision Medicine, University of Campania 'Luigi Vanvitelli', Naples, Italy
| | - Lorenzo Farina
- Department of Computer, Control and Management Engineering, Sapienza University of Rome, Rome, Italy
| | - Giulia Fiscon
- Institute for Systems Analysis and Computer Science "Antonio Ruberti", National Research Council, Rome, Italy
| | - Laurent Gatto
- de Duve Institute, Brussels, Belgium.,Institute for Experimental and Clinical Research (IREC), UCLouvain, Brussels, Belgium
| | - Michele Gentili
- Department of Computer, Control and Management Engineering, Sapienza University of Rome, Rome, Italy
| | - Joseph Loscalzo
- Channing Division of Network Medicine, Department of Medicine, Brigham and Women's Hospital and Harvard Medical School, Boston, Massachusetts, USA.,Division of Cardiovascular Medicine, Brigham and Women's Hospital, Boston, Massachusetts, USA
| | - Cinzia Marchese
- Department of Experimental Medicine, Sapienza University of Rome, Rome, Italy
| | - Claudio Napoli
- Department of Advanced Clinical and Surgical Sciences, University of Campania "Luigi Vanvitelli", Naples, Italy
| | - Paola Paci
- Department of Computer, Control and Management Engineering, Sapienza University of Rome, Rome, Italy
| | - Manuela Petti
- Department of Computer, Control and Management Engineering, Sapienza University of Rome, Rome, Italy
| | - John Quackenbush
- Channing Division of Network Medicine, Department of Medicine, Brigham and Women's Hospital and Harvard Medical School, Boston, Massachusetts, USA.,Department of Biostatistics, Harvard T.H. Chan School of Public Health, Boston, Massachusetts, USA
| | - Paolo Tieri
- CNR National Research Council of Italy, IAC Institute for Applied Computing, Rome, Italy
| | - Davide Viggiano
- BIOGEM, Ariano Irpino, Italy.,Department of Medicine and Health Sciences, University of Molise, Campobasso, Italy
| | - Gemma Vilahur
- Cardiovascular Program-ICCC, IR-Hospital de la Santa Creu i Sant Pau, CiberCV, IIB-Sant Pau, Autonomous University of Barcelona, Barcelona, Spain
| | - Kimberly Glass
- Channing Division of Network Medicine, Department of Medicine, Brigham and Women's Hospital and Harvard Medical School, Boston, Massachusetts, USA.,Department of Biostatistics, Harvard T.H. Chan School of Public Health, Boston, Massachusetts, USA
| | - Jan Baumbach
- Department of Experimental Bioinformatics, TUM School of Life Sciences Weihenstephan, Technical University of Munich, Maximus-von-Imhof-Forum 3, Freising, Germany.,Institute of Mathematics and Computer Science, University of Southern Denmark, Odense, Denmark
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Magnusson R, Gustafsson M. LiPLike: towards gene regulatory network predictions of high certainty. Bioinformatics 2020; 36:2522-2529. [PMID: 31904818 PMCID: PMC7178405 DOI: 10.1093/bioinformatics/btz950] [Citation(s) in RCA: 10] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/29/2019] [Revised: 12/05/2019] [Accepted: 01/03/2020] [Indexed: 12/14/2022] Open
Abstract
MOTIVATION High correlation in expression between regulatory elements is a persistent obstacle for the reverse-engineering of gene regulatory networks. If two potential regulators have matching expression patterns, it becomes challenging to differentiate between them, thus increasing the risk of false positive identifications. RESULTS To allow for gene regulation predictions of high confidence, we propose a novel method, the Linear Profile Likelihood (LiPLike), that assumes a regression model and iteratively searches for interactions that cannot be replaced by a linear combination of other predictors. To compare the performance of LiPLike with other available inference methods, we benchmarked LiPLike using three independent datasets from the Dialogue on Reverse Engineering Assessment and Methods 5 (DREAM5) network inference challenge. We found that LiPLike could be used to stratify predictions of other inference tools, and when applied to the predictions of DREAM5 participants, we observed an average improvement in accuracy of >140% compared to individual methods. Furthermore, LiPLike was able to independently predict networks better than all DREAM5 participants when applied to biological data. When predicting the Escherichia coli network, LiPLike had an accuracy of 0.38 for the top-ranked 100 interactions, whereas the corresponding DREAM5 consensus model yielded an accuracy of 0.11. AVAILABILITY AND IMPLEMENTATION We made LiPLike available to the community as a Python toolbox, available at https://gitlab.com/Gustafsson-lab/liplike. We believe that LiPLike will be used for high confidence predictions in studies where individual model interactions are of high importance, and to remove false positive predictions made by other state-of-the-art gene-gene regulation prediction tools. SUPPLEMENTARY INFORMATION Supplementary data are available at Bioinformatics online.
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Affiliation(s)
- Rasmus Magnusson
- Department of Physics, Chemistry and Biology, Linköping University, Linköping 581 83, Sweden
| | - Mika Gustafsson
- Department of Physics, Chemistry and Biology, Linköping University, Linköping 581 83, Sweden
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Zhang J, Le TD, Liu L, Li J. Inferring and analyzing module-specific lncRNA-mRNA causal regulatory networks in human cancer. Brief Bioinform 2020; 20:1403-1419. [PMID: 29401217 DOI: 10.1093/bib/bby008] [Citation(s) in RCA: 29] [Impact Index Per Article: 7.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/13/2017] [Revised: 01/08/2018] [Indexed: 12/12/2022] Open
Abstract
It is known that noncoding RNAs (ncRNAs) cover ∼98% of the transcriptome, but do not encode proteins. Among ncRNAs, long noncoding RNAs (lncRNAs) are a large and diverse class of RNA molecules, and are thought to be a gold mine of potential oncogenes, anti-oncogenes and new biomarkers. Although only a minority of lncRNAs is functionally characterized, it is clear that they are important regulators to modulate gene expression and involve in many biological functions. To reveal the functions and regulatory mechanisms of lncRNAs, it is vital to understand how lncRNAs regulate their target genes for implementing specific biological functions. In this article, we review the computational methods for inferring lncRNA-mRNA interactions and the third-party databases of storing lncRNA-mRNA regulatory relationships. We have found that the existing methods are based on statistical correlations between the gene expression levels of lncRNAs and mRNAs, and may not reveal gene regulatory relationships which are causal relationships. Moreover, these methods do not consider the modularity of lncRNA-mRNA regulatory networks, and thus, the networks identified are not module-specific. To address the above two issues, we propose a novel method, MSLCRN, to infer and analyze module-specific lncRNA-mRNA causal regulatory networks. We have applied it into glioblastoma multiforme, lung squamous cell carcinoma, ovarian cancer and prostate cancer, respectively. The experimental results show that MSLCRN, as an expression-based method, could be a useful complementary method to study lncRNA regulations.
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