1
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Kajihara KT, Hynson NA. Networks as tools for defining emergent properties of microbiomes and their stability. MICROBIOME 2024; 12:184. [PMID: 39342398 PMCID: PMC11439251 DOI: 10.1186/s40168-024-01868-z] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 05/03/2024] [Accepted: 07/04/2024] [Indexed: 10/01/2024]
Abstract
The potential promise of the microbiome to ameliorate a wide range of societal and ecological challenges, from disease prevention and treatment to the restoration of entire ecosystems, hinges not only on microbiome engineering but also on the stability of beneficial microbiomes. Yet the properties of microbiome stability remain elusive and challenging to discern due to the complexity of interactions and often intractable diversity within these communities of bacteria, archaea, fungi, and other microeukaryotes. Networks are powerful tools for the study of complex microbiomes, with the potential to elucidate structural patterns of stable communities and generate testable hypotheses for experimental validation. However, the implementation of these analyses introduces a cascade of dichotomies and decision trees due to the lack of consensus on best practices. Here, we provide a road map for network-based microbiome studies with an emphasis on discerning properties of stability. We identify important considerations for data preparation, network construction, and interpretation of network properties. We also highlight remaining limitations and outstanding needs for this field. This review also serves to clarify the varying schools of thought on the application of network theory for microbiome studies and to identify practices that enhance the reproducibility and validity of future work. Video Abstract.
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Affiliation(s)
- Kacie T Kajihara
- Pacific Biosciences Research Center, University of Hawai'i at Mānoa, Honolulu, HI, 96822, USA.
| | - Nicole A Hynson
- Pacific Biosciences Research Center, University of Hawai'i at Mānoa, Honolulu, HI, 96822, USA
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2
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Teixeira M, Silva F, Ferreira RM, Pereira T, Figueiredo C, Oliveira HP. A review of machine learning methods for cancer characterization from microbiome data. NPJ Precis Oncol 2024; 8:123. [PMID: 38816569 PMCID: PMC11139966 DOI: 10.1038/s41698-024-00617-7] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/15/2024] [Accepted: 05/17/2024] [Indexed: 06/01/2024] Open
Abstract
Recent studies have shown that the microbiome can impact cancer development, progression, and response to therapies suggesting microbiome-based approaches for cancer characterization. As cancer-related signatures are complex and implicate many taxa, their discovery often requires Machine Learning approaches. This review discusses Machine Learning methods for cancer characterization from microbiome data. It focuses on the implications of choices undertaken during sample collection, feature selection and pre-processing. It also discusses ML model selection, guiding how to choose an ML model, and model validation. Finally, it enumerates current limitations and how these may be surpassed. Proposed methods, often based on Random Forests, show promising results, however insufficient for widespread clinical usage. Studies often report conflicting results mainly due to ML models with poor generalizability. We expect that evaluating models with expanded, hold-out datasets, removing technical artifacts, exploring representations of the microbiome other than taxonomical profiles, leveraging advances in deep learning, and developing ML models better adapted to the characteristics of microbiome data will improve the performance and generalizability of models and enable their usage in the clinic.
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Affiliation(s)
- Marco Teixeira
- Institute for Systems and Computer Engineering, Technology and Science, Porto, Portugal.
- Faculty of Engineering, University of Porto, Porto, Portugal.
| | - Francisco Silva
- Institute for Systems and Computer Engineering, Technology and Science, Porto, Portugal
- Faculty of Science, University of Porto, Porto, Portugal
| | - Rui M Ferreira
- Ipatimup - Institute of Molecular Pathology and Immunology of the University of Porto, Porto, Portugal
- Instituto de Investigação e Inovação em Saúde, University of Porto, Porto, Portugal
| | - Tania Pereira
- Institute for Systems and Computer Engineering, Technology and Science, Porto, Portugal
- Faculty of Sciences and Technology, University of Coimbra, Coimbra, Portugal
| | - Ceu Figueiredo
- Ipatimup - Institute of Molecular Pathology and Immunology of the University of Porto, Porto, Portugal
- Instituto de Investigação e Inovação em Saúde, University of Porto, Porto, Portugal
- Faculty of Medicine, University of Porto, Porto, Portugal
| | - Hélder P Oliveira
- Institute for Systems and Computer Engineering, Technology and Science, Porto, Portugal
- Faculty of Science, University of Porto, Porto, Portugal
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3
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Abstract
The microbiome represents a hidden world of tiny organisms populating not only our surroundings but also our own bodies. By enabling comprehensive profiling of these invisible creatures, modern genomic sequencing tools have given us an unprecedented ability to characterize these populations and uncover their outsize impact on our environment and health. Statistical analysis of microbiome data is critical to infer patterns from the observed abundances. The application and development of analytical methods in this area require careful consideration of the unique aspects of microbiome profiles. We begin this review with a brief overview of microbiome data collection and processing and describe the resulting data structure. We then provide an overview of statistical methods for key tasks in microbiome data analysis, including data visualization, comparison of microbial abundance across groups, regression modeling, and network inference. We conclude with a discussion and highlight interesting future directions.
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Affiliation(s)
- Christine B Peterson
- Department of Biostatistics, The University of Texas MD Anderson Cancer Center, Houston, Texas, USA
| | - Satabdi Saha
- Department of Biostatistics, The University of Texas MD Anderson Cancer Center, Houston, Texas, USA
| | - Kim-Anh Do
- Department of Biostatistics, The University of Texas MD Anderson Cancer Center, Houston, Texas, USA
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4
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Luo M, Zhu J, Jia J, Zhang H, Zhao J. Progress on network modeling and analysis of gut microecology: a review. Appl Environ Microbiol 2024; 90:e0009224. [PMID: 38415584 PMCID: PMC11207142 DOI: 10.1128/aem.00092-24] [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] [Indexed: 02/29/2024] Open
Abstract
The gut microecological network is a complex microbial community within the human body that plays a key role in linking dietary nutrition and host physiology. To understand the complex relationships among microbes and their functions within this community, network analysis has emerged as a powerful tool. By representing the interactions between microbes and their associated omics data as a network, we can gain a comprehensive understanding of the ecological mechanisms that drive the human gut microbiota. In addition, the network-based approach provides a more intuitive analysis of the gut microbiota, simplifying the study of its complex dynamics and interdependencies. This review provides a comprehensive overview of the methods used to construct and analyze networks in the context of gut microecological background. We discuss various types of network modeling approaches, including co-occurrence networks, causal networks, dynamic networks, and multi-omics networks, and describe the analytical techniques used to identify important network properties. We also highlight the challenges and limitations of network modeling in this area, such as data scarcity and heterogeneity, and provide future research directions to overcome these limitations. By exploring these network-based methods, researchers can gain valuable insights into the intricate relationships and functional roles of microbial communities within the gut, ultimately advancing our understanding of the gut microbiota's impact on human health.
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Affiliation(s)
- Meng Luo
- State Key Laboratory of Food Science and Technology, Jiangnan University, Wuxi, Jiangsu, China
- School of Food Science and Technology, Jiangnan University, Wuxi, Jiangsu, China
| | - Jinlin Zhu
- State Key Laboratory of Food Science and Technology, Jiangnan University, Wuxi, Jiangsu, China
- School of Food Science and Technology, Jiangnan University, Wuxi, Jiangsu, China
| | - Jiajia Jia
- Key Laboratory of Advanced Process Control for Light Industry (Ministry of Education), Jiangnan University, Wuxi, China
| | - Hao Zhang
- State Key Laboratory of Food Science and Technology, Jiangnan University, Wuxi, Jiangsu, China
- School of Food Science and Technology, Jiangnan University, Wuxi, Jiangsu, China
- National Engineering Research Center for Functional Food, Jiangnan University, Wuxi, Jiangsu, China
- Wuxi Translational Medicine Research Center, Jiangsu Translational Medicine Research Institute Wuxi Branch, Wuxi, China
- (Yangzhou) Institute of Food Biotechnology, Jiangnan University, Yangzhou, China
| | - Jianxin Zhao
- State Key Laboratory of Food Science and Technology, Jiangnan University, Wuxi, Jiangsu, China
- School of Food Science and Technology, Jiangnan University, Wuxi, Jiangsu, China
- Wuxi Translational Medicine Research Center, Jiangsu Translational Medicine Research Institute Wuxi Branch, Wuxi, China
- (Yangzhou) Institute of Food Biotechnology, Jiangnan University, Yangzhou, China
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5
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Kishore D, Birzu G, Hu Z, DeLisi C, Korolev KS, Segrè D. Inferring microbial co-occurrence networks from amplicon data: a systematic evaluation. mSystems 2023; 8:e0096122. [PMID: 37338270 PMCID: PMC10469762 DOI: 10.1128/msystems.00961-22] [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: 10/25/2022] [Accepted: 04/14/2023] [Indexed: 06/21/2023] Open
Abstract
Microbes commonly organize into communities consisting of hundreds of species involved in complex interactions with each other. 16S ribosomal RNA (16S rRNA) amplicon profiling provides snapshots that reveal the phylogenies and abundance profiles of these microbial communities. These snapshots, when collected from multiple samples, can reveal the co-occurrence of microbes, providing a glimpse into the network of associations in these communities. However, the inference of networks from 16S data involves numerous steps, each requiring specific tools and parameter choices. Moreover, the extent to which these steps affect the final network is still unclear. In this study, we perform a meticulous analysis of each step of a pipeline that can convert 16S sequencing data into a network of microbial associations. Through this process, we map how different choices of algorithms and parameters affect the co-occurrence network and identify the steps that contribute substantially to the variance. We further determine the tools and parameters that generate robust co-occurrence networks and develop consensus network algorithms based on benchmarks with mock and synthetic data sets. The Microbial Co-occurrence Network Explorer, or MiCoNE (available at https://github.com/segrelab/MiCoNE) follows these default tools and parameters and can help explore the outcome of these combinations of choices on the inferred networks. We envisage that this pipeline could be used for integrating multiple data sets and generating comparative analyses and consensus networks that can guide our understanding of microbial community assembly in different biomes. IMPORTANCE Mapping the interrelationships between different species in a microbial community is important for understanding and controlling their structure and function. The surge in the high-throughput sequencing of microbial communities has led to the creation of thousands of data sets containing information about microbial abundances. These abundances can be transformed into co-occurrence networks, providing a glimpse into the associations within microbiomes. However, processing these data sets to obtain co-occurrence information relies on several complex steps, each of which involves numerous choices of tools and corresponding parameters. These multiple options pose questions about the robustness and uniqueness of the inferred networks. In this study, we address this workflow and provide a systematic analysis of how these choices of tools affect the final network and guidelines on appropriate tool selection for a particular data set. We also develop a consensus network algorithm that helps generate more robust co-occurrence networks based on benchmark synthetic data sets.
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Affiliation(s)
- Dileep Kishore
- Bioinformatics Program, Boston University, Boston, Massachusetts, USA
- Biological Design Center, Boston University, Boston, Massachusetts, USA
- Biosciences Division, Oak Ridge National Laboratory, Oak Ridge, Tennessee, USA
| | - Gabriel Birzu
- Department of Physics, Boston University, Boston, Massachusetts, USA
- Department of Applied Physics, Stanford University, Stanford, California, USA
| | - Zhenjun Hu
- Bioinformatics Program, Boston University, Boston, Massachusetts, USA
| | - Charles DeLisi
- Bioinformatics Program, Boston University, Boston, Massachusetts, USA
- Department of Physics, Boston University, Boston, Massachusetts, USA
- Department of Biomedical Engineering, Boston University, Boston, Massachusetts, USA
| | - Kirill S. Korolev
- Bioinformatics Program, Boston University, Boston, Massachusetts, USA
- Biological Design Center, Boston University, Boston, Massachusetts, USA
- Department of Physics, Boston University, Boston, Massachusetts, USA
| | - Daniel Segrè
- Bioinformatics Program, Boston University, Boston, Massachusetts, USA
- Biological Design Center, Boston University, Boston, Massachusetts, USA
- Department of Physics, Boston University, Boston, Massachusetts, USA
- Department of Biomedical Engineering, Boston University, Boston, Massachusetts, USA
- Department of Biology, Boston University, Boston, Massachusetts, USA
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6
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Fabbrini M, Scicchitano D, Candela M, Turroni S, Rampelli S. Connect the dots: sketching out microbiome interactions through networking approaches. MICROBIOME RESEARCH REPORTS 2023; 2:25. [PMID: 38058764 PMCID: PMC10696587 DOI: 10.20517/mrr.2023.25] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 04/14/2023] [Revised: 07/05/2023] [Accepted: 07/12/2023] [Indexed: 12/08/2023]
Abstract
Microbiome networking analysis has emerged as a powerful tool for studying the complex interactions among microorganisms in various ecological niches, including the human body and several environments. This analysis has been used extensively in both human and environmental studies, revealing key taxa and functional units peculiar to the ecosystem considered. In particular, it has been mainly used to investigate the effects of environmental stressors, such as pollution, climate change or therapies, on host-associated microbial communities and ecosystem function. In this review, we discuss the latest advances in microbiome networking analysis, including methods for constructing and analyzing microbiome networks, and provide a case study on how to use these tools. These analyses typically involve constructing a network that represents interactions among microbial taxa or functional units, such as genes or metabolic pathways. Such networks can be based on a variety of data sources, including 16S rRNA sequencing, metagenomic sequencing, and metabolomics data. Once constructed, these networks can be analyzed to identify key nodes or modules important for the stability and function of the microbiome. By providing insights into essential ecological features of microbial communities, microbiome networking analysis has the potential to transform our understanding of the microbial world and its impact on human health and the environment.
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Affiliation(s)
- Marco Fabbrini
- Microbiomics Unit, Department of Medical and Surgical Sciences, University of Bologna, Bologna 40138, Italy
- Unit of Microbiome Science and Biotechnology, Department of Pharmacy and Biotechnology, University of Bologna, Bologna 40126, Italy
- Authors contributed equally
| | - Daniel Scicchitano
- Unit of Microbiome Science and Biotechnology, Department of Pharmacy and Biotechnology, University of Bologna, Bologna 40126, Italy
- Authors contributed equally
| | - Marco Candela
- Unit of Microbiome Science and Biotechnology, Department of Pharmacy and Biotechnology, University of Bologna, Bologna 40126, Italy
| | - Silvia Turroni
- Unit of Microbiome Science and Biotechnology, Department of Pharmacy and Biotechnology, University of Bologna, Bologna 40126, Italy
| | - Simone Rampelli
- Unit of Microbiome Science and Biotechnology, Department of Pharmacy and Biotechnology, University of Bologna, Bologna 40126, Italy
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7
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Love CJ, Gubert C, Kodikara S, Kong G, Lê Cao KA, Hannan AJ. Microbiota DNA isolation, 16S rRNA amplicon sequencing, and bioinformatic analysis for bacterial microbiome profiling of rodent fecal samples. STAR Protoc 2022; 3:101772. [PMID: 36313541 PMCID: PMC9597187 DOI: 10.1016/j.xpro.2022.101772] [Citation(s) in RCA: 5] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/27/2022] Open
Abstract
Fecal samples are frequently used to characterize bacterial populations of the gastrointestinal tract. A protocol is provided to profile gut bacterial populations using rodent fecal samples. We describe the optimal procedures for collecting rodent fecal samples, isolating genomic DNA, 16S rRNA gene V4 region sequencing, and bioinformatic analyses. This protocol includes detailed instructions and example outputs to ensure accurate, reproducible results and data visualization. Comprehensive troubleshooting and limitation sections address technical and statistical issues that may arise when profiling microbiota. For complete details on the use and execution of this protocol, please refer to Gubert et al. (2022).
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Affiliation(s)
- Chloe J. Love
- The Florey Institute of Neuroscience and Mental Health, University of Melbourne, Parkville, VIC 3010, Australia
| | - Carolina Gubert
- The Florey Institute of Neuroscience and Mental Health, University of Melbourne, Parkville, VIC 3010, Australia,Corresponding author
| | - Saritha Kodikara
- Department of Anatomy and Physiology, University of Melbourne, Parkville, VIC 3010, Australia,Melbourne Integrative Genomics, School of Mathematics and Statistics, University of Melbourne, Parkville VIC, 3010, Australia
| | - Geraldine Kong
- The Florey Institute of Neuroscience and Mental Health, University of Melbourne, Parkville, VIC 3010, Australia
| | - Kim-Anh Lê Cao
- Department of Anatomy and Physiology, University of Melbourne, Parkville, VIC 3010, Australia
| | - Anthony J. Hannan
- The Florey Institute of Neuroscience and Mental Health, University of Melbourne, Parkville, VIC 3010, Australia,Department of Anatomy and Physiology, University of Melbourne, Parkville, VIC 3010, Australia,Corresponding author
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8
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Lam TJ, Ye Y. Meta-analysis of microbiome association networks reveal patterns of dysbiosis in diseased microbiomes. Sci Rep 2022; 12:17482. [PMID: 36261472 PMCID: PMC9581956 DOI: 10.1038/s41598-022-22541-1] [Citation(s) in RCA: 8] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/22/2022] [Accepted: 10/17/2022] [Indexed: 01/12/2023] Open
Abstract
The human gut microbiome is composed of a diverse and dynamic population of microbial species which play key roles in modulating host health and physiology. While individual microbial species have been found to be associated with certain disease states, increasing evidence suggests that higher-order microbial interactions may have an equal or greater contribution to host fitness. To better understand microbial community dynamics, we utilize networks to study interactions through a meta-analysis of microbial association networks between healthy and disease gut microbiomes. Taking advantage of the large number of metagenomes derived from healthy individuals and patients with various diseases, together with recent advances in network inference that can deal with sparse compositional data, we inferred microbial association networks based on co-occurrence of gut microbial species and made the networks publicly available as a resource (GitHub repository named GutNet). Through our meta-analysis of inferred networks, we were able to identify network-associated features that help stratify between healthy and disease states such as the differentiation of various bacterial phyla and enrichment of Proteobacteria interactions in diseased networks. Additionally, our findings show that the contributions of taxa in microbial associations are disproportionate to their abundances and that rarer taxa of microbial species play an integral part in shaping dynamics of microbial community interactions. Network-based meta-analysis revealed valuable insights into microbial community dynamics between healthy and disease phenotypes. We anticipate that the healthy and diseased microbiome association networks we inferred will become an important resource for human-related microbiome research.
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Affiliation(s)
- Tony J Lam
- Luddy School of Informatics, Computing and Engineering, Indiana University, 700 N. Woodlawn Avenue, Bloomington, IN, 47408, USA
| | - Yuzhen Ye
- Luddy School of Informatics, Computing and Engineering, Indiana University, 700 N. Woodlawn Avenue, Bloomington, IN, 47408, USA.
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9
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Zeng Y, Li J, Wei C, Zhao H, Wang T. mbDenoise: microbiome data denoising using zero-inflated probabilistic principal components analysis. Genome Biol 2022; 23:94. [PMID: 35422001 PMCID: PMC9011970 DOI: 10.1186/s13059-022-02657-3] [Citation(s) in RCA: 8] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/08/2021] [Accepted: 03/21/2022] [Indexed: 12/13/2022] Open
Abstract
The analysis of microbiome data has several technical challenges. In particular, count matrices contain a large proportion of zeros, some of which are biological, whereas others are technical. Furthermore, the measurements suffer from unequal sequencing depth, overdispersion, and data redundancy. These nuisance factors introduce substantial noise. We propose an accurate and robust method, mbDenoise, for denoising microbiome data. Assuming a zero-inflated probabilistic PCA (ZIPPCA) model, mbDenoise uses variational approximation to learn the latent structure and recovers the true abundance levels using the posterior, borrowing information across samples and taxa. mbDenoise outperforms state-of-the-art methods to extract the signal for downstream analyses.
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Affiliation(s)
- Yanyan Zeng
- Department of Bioinformatics and Biostatistics, Shanghai Jiao Tong University, Shanghai, China
| | - Jing Li
- Department of Bioinformatics and Biostatistics, Shanghai Jiao Tong University, Shanghai, China
| | - Chaochun Wei
- Department of Bioinformatics and Biostatistics, Shanghai Jiao Tong University, Shanghai, China
| | - Hongyu Zhao
- Department of Biostatistics, Yale University, New Haven, CT, USA.
- SJTU-Yale Joint Center for Biostatistics and Data Science, Shanghai Jiao Tong University, Shanghai, China.
| | - Tao Wang
- Department of Bioinformatics and Biostatistics, Shanghai Jiao Tong University, Shanghai, China.
- SJTU-Yale Joint Center for Biostatistics and Data Science, Shanghai Jiao Tong University, Shanghai, China.
- Department of Statistics, School of Mathematical Sciences, Shanghai Jiao Tong University, Shanghai, China.
- Joint International Research Laboratory of Metabolic & Developmental Sciences, Shanghai Jiao Tong University, Shanghai, China.
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10
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Chen L, Wan H, He Q, He S, Deng M. Statistical Methods for Microbiome Compositional Data Network Inference: A Survey. J Comput Biol 2022; 29:704-723. [PMID: 35404093 DOI: 10.1089/cmb.2021.0406] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/12/2022] Open
Abstract
Microbes can be found almost everywhere in the world. They are not isolated, but rather interact with each other and establish connections with their living environments. Studying these interactions is essential to an understanding of the organization and complex interplay of microbial communities, as well as the structure and dynamics of various ecosystems. A widely used approach toward this objective involves the inference of microbiome interaction networks. However, owing to the compositional, high-dimensional, sparse, and heterogeneous nature of observed microbial data, applying network inference methods to estimate their associations is challenging. In addition, external environmental interference and biological concerns also make it more difficult to deal with the network inference. In this article, we provide a comprehensive review of emerging microbiome interaction network inference methods. According to various research targets, estimated networks are divided into four main categories: correlation networks, conditional correlation networks, mixture networks, and differential networks. Their assumptions, high-level ideas, advantages, as well as limitations, are presented in this review. Since real microbial interactions can be complex and dynamic, no unifying method has, to date, captured all the aspects of interest. In addition, we discuss the challenges now confronting current microbial interaction study and future prospects. Finally, we point out several feasible directions of microbial network inference analysis and highlight that future research requires the joint promotion of statistical computation methods and experimental techniques.
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Affiliation(s)
- Liang Chen
- School of Mathematical Sciences, Peking University, Beijing, China
| | - Hui Wan
- School of Mathematical Sciences, Peking University, Beijing, China
| | - Qiuyan He
- School of Mathematical Sciences, Peking University, Beijing, China
| | - Shun He
- School of Mathematical Sciences, Peking University, Beijing, China
| | - Minghua Deng
- School of Mathematical Sciences, Peking University, Beijing, China.,Center for Statistical Science, Peking University, Beijing, China.,Center for Quantitative Biology, Peking University, Beijing, China
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11
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Matchado MS, Lauber M, Reitmeier S, Kacprowski T, Baumbach J, Haller D, List M. Network analysis methods for studying microbial communities: A mini review. Comput Struct Biotechnol J 2021; 19:2687-2698. [PMID: 34093985 PMCID: PMC8131268 DOI: 10.1016/j.csbj.2021.05.001] [Citation(s) in RCA: 109] [Impact Index Per Article: 36.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/01/2021] [Revised: 05/01/2021] [Accepted: 05/01/2021] [Indexed: 12/20/2022] Open
Abstract
Microorganisms including bacteria, fungi, viruses, protists and archaea live as communities in complex and contiguous environments. They engage in numerous inter- and intra- kingdom interactions which can be inferred from microbiome profiling data. In particular, network-based approaches have proven helpful in deciphering complex microbial interaction patterns. Here we give an overview of state-of-the-art methods to infer intra-kingdom interactions ranging from simple correlation- to complex conditional dependence-based methods. We highlight common biases encountered in microbial profiles and discuss mitigation strategies employed by different tools and their trade-off with increased computational complexity. Finally, we discuss current limitations that motivate further method development to infer inter-kingdom interactions and to robustly and comprehensively characterize microbial environments in the future.
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Affiliation(s)
- Monica Steffi Matchado
- Chair of Experimental Bioinformatics, Technical University of Munich, 85354 Freising, Germany
| | - Michael Lauber
- Chair of Experimental Bioinformatics, Technical University of Munich, 85354 Freising, Germany
| | - Sandra Reitmeier
- ZIEL - Institute for Food & Health, Technical University of Munich, 85354 Freising, Germany
- Chair of Nutrition and Immunology, Technical University of Munich, 85354 Freising, Germany
| | - Tim Kacprowski
- Division Data Science in Biomedicine, Peter L. Reichertz Institute for Medical Informatics, TU Braunschweig and Hannover Medical School, 38106 Brunswick, Germany
- Braunschweig Integrated Centre of Systems Biology (BRICS), 38106 Brunswick, Germany
| | - Jan Baumbach
- Institute of Mathematics and Computer Science, University of Southern Denmark, 5230 Odense, Denmark
- Chair of Computational Systems Biology, University of Hamburg, 22607 Hamburg, Germany
| | - Dirk Haller
- ZIEL - Institute for Food & Health, Technical University of Munich, 85354 Freising, Germany
- Chair of Nutrition and Immunology, Technical University of Munich, 85354 Freising, Germany
| | - Markus List
- Chair of Experimental Bioinformatics, Technical University of Munich, 85354 Freising, Germany
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