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      iNAP: An integrated network analysis pipeline for microbiome studies

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          Abstract

          Integrated network analysis pipeline (iNAP) is an online analysis pipeline for generating and analyzing comprehensive ecological networks in microbiome studies. It is implemented in two sections, that is, network construction and network analysis, and integrates many open‐access tools. Network construction contains multiple feasible alternatives, including correlation‐based approaches (Pearson's correlation and Spearman's rank correlation along with random matrix theory, and sparse correlations for compositional data) and conditional dependence‐based methods (extended local similarity analysis and sparse inverse covariance estimation for ecological association inference), while network analysis provides topological structures at different levels and the potential effects of environmental factors on network structures. Considering the full workflow, from microbiome data set to network result, iNAP contains the molecular ecological network analysis pipeline and interdomain ecological network analysis pipeline (IDENAP), which correspond to the intradomain and interdomain associations of microbial species at multiple taxonomic levels. Here, we describe the detailed workflow by taking IDENAP as an example and show the comprehensive steps to assist researchers to conduct the relevant analyses using their own data sets. Afterwards, some auxiliary tools facilitating the pipeline are introduced to effectively aid in the switch from local analysis to online operations. Therefore, iNAP, as an easy‐to‐use platform that provides multiple network‐associated tools and approaches, can enable researchers to better understand the organization of microbial communities. iNAP is available at http://mem.rcees.ac.cn:8081 with free registration.

          Abstract

          Integrated network analysis pipeline (iNAP) contains two network analyses pipelines for intradomain and interdomain associations of microbial species at multiple taxonomic levels, that is, molecular ecological network analysis pipeline and interdomain ecological network analysis pipeline. iNAP provides multiple approaches and methods for network analysis of microbiome studies, including SparCC, eLSA, SPIEC‐EASI, and RMT‐based Pearson's or Spearman's correlations. Through free registration and easy operations, iNAP can be easily operated by researchers without any prerequisite bioinformatics or statistical language‐based programming skills for network analyses of microbiome studies.

          Highlights

          • Integrated network analysis pipeline (iNAP) contains two network analyses pipelines for intradomain and interdomain associations of microbial species at multiple taxonomic levels, that is, molecular ecological network analysis pipeline and interdomain ecological network analysis pipeline.

          • iNAP provides multiple approaches and methods for network analysis of microbiome studies, including SparCC, eLSA, SPIEC‐EASI, and RMT‐based Pearson's or Spearman's correlations.

          • Through free registration and easy operations, iNAP can be easily operated by researchers without any prerequisite bioinformatics or statistical language‐based programming skills for network analyses of microbiome studies.

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          Most cited references26

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          Cytoscape: a software environment for integrated models of biomolecular interaction networks.

          Cytoscape is an open source software project for integrating biomolecular interaction networks with high-throughput expression data and other molecular states into a unified conceptual framework. Although applicable to any system of molecular components and interactions, Cytoscape is most powerful when used in conjunction with large databases of protein-protein, protein-DNA, and genetic interactions that are increasingly available for humans and model organisms. Cytoscape's software Core provides basic functionality to layout and query the network; to visually integrate the network with expression profiles, phenotypes, and other molecular states; and to link the network to databases of functional annotations. The Core is extensible through a straightforward plug-in architecture, allowing rapid development of additional computational analyses and features. Several case studies of Cytoscape plug-ins are surveyed, including a search for interaction pathways correlating with changes in gene expression, a study of protein complexes involved in cellular recovery to DNA damage, inference of a combined physical/functional interaction network for Halobacterium, and an interface to detailed stochastic/kinetic gene regulatory models.
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            Galaxy: a comprehensive approach for supporting accessible, reproducible, and transparent computational research in the life sciences

            Increased reliance on computational approaches in the life sciences has revealed grave concerns about how accessible and reproducible computation-reliant results truly are. Galaxy http://usegalaxy.org, an open web-based platform for genomic research, addresses these problems. Galaxy automatically tracks and manages data provenance and provides support for capturing the context and intent of computational methods. Galaxy Pages are interactive, web-based documents that provide users with a medium to communicate a complete computational analysis.
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              • Record: found
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              Molecular ecological network analyses

              Background Understanding the interaction among different species within a community and their responses to environmental changes is a central goal in ecology. However, defining the network structure in a microbial community is very challenging due to their extremely high diversity and as-yet uncultivated status. Although recent advance of metagenomic technologies, such as high throughout sequencing and functional gene arrays, provide revolutionary tools for analyzing microbial community structure, it is still difficult to examine network interactions in a microbial community based on high-throughput metagenomics data. Results Here, we describe a novel mathematical and bioinformatics framework to construct ecological association networks named molecular ecological networks (MENs) through Random Matrix Theory (RMT)-based methods. Compared to other network construction methods, this approach is remarkable in that the network is automatically defined and robust to noise, thus providing excellent solutions to several common issues associated with high-throughput metagenomics data. We applied it to determine the network structure of microbial communities subjected to long-term experimental warming based on pyrosequencing data of 16 S rRNA genes. We showed that the constructed MENs under both warming and unwarming conditions exhibited topological features of scale free, small world and modularity, which were consistent with previously described molecular ecological networks. Eigengene analysis indicated that the eigengenes represented the module profiles relatively well. In consistency with many other studies, several major environmental traits including temperature and soil pH were found to be important in determining network interactions in the microbial communities examined. To facilitate its application by the scientific community, all these methods and statistical tools have been integrated into a comprehensive Molecular Ecological Network Analysis Pipeline (MENAP), which is open-accessible now (http://ieg2.ou.edu/MENA). Conclusions The RMT-based molecular ecological network analysis provides powerful tools to elucidate network interactions in microbial communities and their responses to environmental changes, which are fundamentally important for research in microbial ecology and environmental microbiology.
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                Author and article information

                Contributors
                yedeng@rcees.ac.cn
                Journal
                Imeta
                Imeta
                10.1002/(ISSN)2770-596X
                IMT2
                iMeta
                John Wiley and Sons Inc. (Hoboken )
                2770-5986
                2770-596X
                16 March 2022
                June 2022
                : 1
                : 2 ( doiID: 10.1002/imt2.v1.2 )
                : e13
                Affiliations
                [ 1 ] CAS Key Laboratory of Environmental Biotechnology, Research Center for Eco‐Environmental Sciences Chinese Academy of Sciences Beijing China
                [ 2 ] Collegeof Resources and Environment University of Chinese Academy of Sciences Beijing China
                [ 3 ] Institute for Marine Science and Technology Shandong University Qingdao China
                [ 4 ] College of Horticulture Hunan Agricultural University Changsha China
                [ 5 ] West China Hospital of Stomatology, State Key Laboratory of Oral Diseases, National Clinical Research Center for Oral Diseases Sichuan University Chengdu China
                Author notes
                [*] [* ] Correspondence Ye Deng, CAS Key Laboratory of Environmental Biotechnology, Research Center for Eco‐Environmental Sciences, Chinese Academy of Sciences, 18 Shuangqing Road, Haidian District, Beijing 100085, China.

                Email: yedeng@ 123456rcees.ac.cn

                Author information
                http://orcid.org/0000-0002-7584-0632
                Article
                IMT213
                10.1002/imt2.13
                10989900
                38868563
                9db03428-0823-4747-acb4-583531271397
                © 2022 The Authors. iMeta published by John Wiley & Sons Australia, Ltd on behalf of iMeta Science.

                This is an open access article under the terms of the http://creativecommons.org/licenses/by/4.0/ License, which permits use, distribution and reproduction in any medium, provided the original work is properly cited.

                History
                : 28 January 2022
                : 19 January 2022
                : 29 January 2022
                Page count
                Figures: 4, Tables: 1, Pages: 11, Words: 5802
                Funding
                Funded by: National Key Research and Development Program
                Award ID: 2019YFC1905001
                Funded by: Key Projects of Sichuan Provincial Department of Science and Technology
                Award ID: 2020YFSY0008
                Funded by: Wenshan Tobacco Company of Yunnan Province of China
                Award ID: 2021530000241033
                Funded by: China Postdoctoral Science Foundation , doi 10.13039/501100002858;
                Award ID: 2021M703410
                Categories
                Protocol
                Protocol
                Custom metadata
                2.0
                June 2022
                Converter:WILEY_ML3GV2_TO_JATSPMC version:6.4.0 mode:remove_FC converted:25.03.2024

                interaction,microbial association,microbiome,network analyses

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