Nolan K. Newman, Matthew S. Macovsky, Richard R. Rodrigues, Amanda M. Bruce, Jacob W. Pederson, Jyothi Padiadpu, Jigui Shan, Joshua Williams, Sankalp S. Patil, Amiran K. Dzutsev, Natalia Shulzhenko, Giorgio Trinchieri, Kevin Brown, Andrey Morgun
{"title":"跨基因组网络分析(TkNA):推断宿主-微生物群和其他多基因组相互作用因果关系的系统框架。","authors":"Nolan K. Newman, Matthew S. Macovsky, Richard R. Rodrigues, Amanda M. Bruce, Jacob W. Pederson, Jyothi Padiadpu, Jigui Shan, Joshua Williams, Sankalp S. Patil, Amiran K. Dzutsev, Natalia Shulzhenko, Giorgio Trinchieri, Kevin Brown, Andrey Morgun","doi":"10.1038/s41596-024-00960-w","DOIUrl":null,"url":null,"abstract":"We present Transkingdom Network Analysis (TkNA), a unique causal-inference analytical framework that offers a holistic view of biological systems by integrating data from multiple cohorts and diverse omics types. TkNA helps to decipher key players and mechanisms governing host–microbiota (or any multi-omic data) interactions in specific conditions or diseases. TkNA reconstructs a network that represents a statistical model capturing the complex relationships between different omics in the biological system. It identifies robust and reproducible patterns of fold change direction and correlation sign across several cohorts to select differential features and their per-group correlations. The framework then uses causality-sensitive metrics, statistical thresholds and topological criteria to determine the final edges forming the transkingdom network. With the subsequent network’s topological features, TkNA identifies nodes controlling a given subnetwork or governing communication between kingdoms and/or subnetworks. The computational time for the millions of correlations necessary for network reconstruction in TkNA typically takes only a few minutes, varying with the study design. Unlike most other multi-omics approaches that find only associations, TkNA focuses on establishing causality while accounting for the complex structure of multi-omic data. It achieves this without requiring huge sample sizes. Moreover, the TkNA protocol is user friendly, requiring minimal installation and basic familiarity with Unix. Researchers can access the TkNA software at https://github.com/CAnBioNet/TkNA/ . Transkingdom Network Analysis (TkNA) is a unique analytical framework for inferring causal factors underlying host–microbiota and other multi-omic interactions, by integrating data from multiple cohorts and diverse omics types.","PeriodicalId":18901,"journal":{"name":"Nature Protocols","volume":null,"pages":null},"PeriodicalIF":13.1000,"publicationDate":"2024-03-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":"{\"title\":\"Transkingdom Network Analysis (TkNA): a systems framework for inferring causal factors underlying host–microbiota and other multi-omic interactions\",\"authors\":\"Nolan K. Newman, Matthew S. Macovsky, Richard R. Rodrigues, Amanda M. Bruce, Jacob W. 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Transkingdom Network Analysis (TkNA): a systems framework for inferring causal factors underlying host–microbiota and other multi-omic interactions
We present Transkingdom Network Analysis (TkNA), a unique causal-inference analytical framework that offers a holistic view of biological systems by integrating data from multiple cohorts and diverse omics types. TkNA helps to decipher key players and mechanisms governing host–microbiota (or any multi-omic data) interactions in specific conditions or diseases. TkNA reconstructs a network that represents a statistical model capturing the complex relationships between different omics in the biological system. It identifies robust and reproducible patterns of fold change direction and correlation sign across several cohorts to select differential features and their per-group correlations. The framework then uses causality-sensitive metrics, statistical thresholds and topological criteria to determine the final edges forming the transkingdom network. With the subsequent network’s topological features, TkNA identifies nodes controlling a given subnetwork or governing communication between kingdoms and/or subnetworks. The computational time for the millions of correlations necessary for network reconstruction in TkNA typically takes only a few minutes, varying with the study design. Unlike most other multi-omics approaches that find only associations, TkNA focuses on establishing causality while accounting for the complex structure of multi-omic data. It achieves this without requiring huge sample sizes. Moreover, the TkNA protocol is user friendly, requiring minimal installation and basic familiarity with Unix. Researchers can access the TkNA software at https://github.com/CAnBioNet/TkNA/ . Transkingdom Network Analysis (TkNA) is a unique analytical framework for inferring causal factors underlying host–microbiota and other multi-omic interactions, by integrating data from multiple cohorts and diverse omics types.
期刊介绍:
Nature Protocols focuses on publishing protocols used to address significant biological and biomedical science research questions, including methods grounded in physics and chemistry with practical applications to biological problems. The journal caters to a primary audience of research scientists and, as such, exclusively publishes protocols with research applications. Protocols primarily aimed at influencing patient management and treatment decisions are not featured.
The specific techniques covered encompass a wide range, including but not limited to: Biochemistry, Cell biology, Cell culture, Chemical modification, Computational biology, Developmental biology, Epigenomics, Genetic analysis, Genetic modification, Genomics, Imaging, Immunology, Isolation, purification, and separation, Lipidomics, Metabolomics, Microbiology, Model organisms, Nanotechnology, Neuroscience, Nucleic-acid-based molecular biology, Pharmacology, Plant biology, Protein analysis, Proteomics, Spectroscopy, Structural biology, Synthetic chemistry, Tissue culture, Toxicology, and Virology.