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Multi-Omics Data Analysis for Inflammation Disease Research: Correlation Analysis, Causal Analysis and Network Analysis

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Methodologies of Multi-Omics Data Integration and Data Mining

Part of the book series: Translational Bioinformatics ((TRBIO,volume 19))

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Abstract

In recent decades, an enormous amount of research on the human gut microbiota has established that it is strongly associated with human health and is involved in the occurrence and development of a variety of diseases, including inflammation diseases. However, current research has concentrated on the relationship between disease and the human gut microbiota, as well as the interactions between microorganisms. The absence of a more detailed understanding of the mechanism and causation of diseases associated with the gut microbiota restricts clinical diagnosis and treatment. Due to the advancement of sequencing and mass spectrometry techniques, numerous approaches have been used in microbiome research to generate multi-omics datasets that can provide a comprehensive view of the compositions and changes in microbial communities’ genetic, metabolic, and biochemical processes, as well as an in-depth understanding of the gut microbiome and diseases. Nonetheless, the absence of systematic reviews of multi-omics approaches and their application to diseases restricts their application to microbiome research. As such, we took a holistic view of multi-omics approaches in the gut microbiome and discussed how multi-omics approaches could aid in disease diagnosis and treatment in this review. To be clear, we used inflammation disease as a model disease to introduce multi-omics approaches, integrated analysis methods for multi-omics datasets, and their application to inflammation diseases, particularly in terms of treatment methods involving microbiome approaches. Without a doubt, our comprehensive review of multi-omics approaches in inflammation disease and the bioinformatics tools for integrating multi-omics datasets may help identify clades for clinical diagnosis and treatment of inflammation diseases.

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Authors and Affiliations

  1. School of Life Sciences, Anhui Medical University, Hefei, Anhui, China

    Maozhen Han, Na Zhang, Zhangjie Peng, Yujie Mao, Qianqian Yang, Yiyang Chen, Mengfei Ren & Weihua Jia

  2. Key Laboratory of Molecular Biophysics of the Ministry of Education, Hubei Key Laboratory of Bioinformatics and Molecular-imaging, Department of Bioinformatics and Systems Biology, College of Life Science and Technology, Huazhong University of Science and Technology, Wuhan, Hubei, China

    Maozhen Han

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  1. Department of Bioinformatics and Systems Biology, Center of AI biology, College of Life Science and Technology, Huazhong University of Science and Technology, Wuhan, China

    Kang Ning

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© 2023 The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

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Han, M. et al. (2023). Multi-Omics Data Analysis for Inflammation Disease Research: Correlation Analysis, Causal Analysis and Network Analysis. In: Ning, K. (eds) Methodologies of Multi-Omics Data Integration and Data Mining. Translational Bioinformatics, vol 19. Springer, Singapore. https://doi.org/10.1007/978-981-19-8210-1_6

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