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{"id":"abdillIntegration168000Samples2025","abstract":"The factors shaping human microbiome variation are a major focus of biomedical research. While other fields have used large sequencing compendia to extract insights requiring otherwise impractical sample sizes, the microbiome field has lacked a comparably sized resource for the 16S rRNA gene amplicon sequencing commonly used to quantify microbiome composition. To address this gap, we processed 168,464 publicly available human gut microbiome samples with a uniform pipeline. We use this compendium to evaluate geographic and technical effects on microbiome variation. We find that regions such as Central and Southern Asia differ significantly from the more thoroughly characterized microbiomes of Europe and Northern America and that composition alone can be used to predict a sample’s region of origin. We also find strong associations between microbiome variation and technical factors such as primers and DNA extraction. We anticipate this growing work, the Human Microbiome Compendium, will enable advanced applied and methodological research.","accessed":{"date-parts":[["2025",5,5]]},"author":[{"family":"Abdill","given":"Richard J."},{"family":"Graham","given":"Samantha P."},{"family":"Rubinetti","given":"Vincent"},{"family":"Ahmadian","given":"Mansooreh"},{"family":"Hicks","given":"Parker"},{"family":"Chetty","given":"Ashwin"},{"family":"McDonald","given":"Daniel"},{"family":"Ferretti","given":"Pamela"},{"family":"Gibbons","given":"Elizabeth"},{"family":"Rossi","given":"Marco"},{"family":"Krishnan","given":"Arjun"},{"family":"Albert","given":"Frank W."},{"family":"Greene","given":"Casey S."},{"family":"Davis","given":"Sean"},{"family":"Blekhman","given":"Ran"}],"citation-key":"abdillIntegration168000Samples2025","container-title":"Cell","container-title-short":"Cell","DOI":"10.1016/j.cell.2024.12.017","ISSN":"0092-8674","issue":"4","issued":{"date-parts":[["2025",2,20]]},"note":"Read_Status: New\nRead_Status_Date: 2025-05-06T13:59:51.848Z","page":"1100-1118.e17","source":"ScienceDirect","title":"Integration of 168,000 samples reveals global patterns of the human gut microbiome","type":"article-journal","URL":"https://www.sciencedirect.com/science/article/pii/S0092867424014302","volume":"188"},
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{"id":"aitchisonConciseGuideCompositional","author":[{"family":"Aitchison","given":"John"}],"citation-key":"aitchisonConciseGuideCompositional","language":"en","source":"Zotero","title":"A Concise Guide to Compositional Data Analysis","type":"article-journal"},
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{"id":"bashanUniversalityHumanMicrobial2016","abstract":"The recent realization that human-associated microbial communities play a crucial role in determining our health and well-being, has led to the ongoing development of microbiome-based therapies such as fecal microbiota transplantation,. Thosemicrobial communities are very complex, dynamic and highly personalized ecosystems,, exhibiting a high degree of inter-individual variability in both species assemblages and abundance profiles. It is not known whether the underlying ecological dynamics, which can be parameterized by growth rates, intra- and inter-species interactions in population dynamics models, are largely host-independent (i.e. “universal”) or host-specific. If the inter-individual variability reflects host-specific dynamics due to differences in host lifestyle, physiology, or genetics, then generic microbiome manipulations may have unintended consequences, rendering them ineffectual or even detrimental. Alternatively, microbial ecosystems of different subjects may follow a universal dynamics with the inter-individual variability mainly stemming from differences in the sets of colonizing species,. Here we developed a novel computational method to characterize human microbial dynamics. Applying this method to cross-sectional data from two large-scale metagenomic studies, the Human Microbiome Project, and the Student Microbiome Project, we found that both gut and mouth microbiomes display pronounced universal dynamics, whereas communities associated with certain skin sites are likely shaped by differences in the host environment. Interestingly, the universality of gut microbial dynamics is not observed in subjects with recurrent Clostridium difficile infection but is observed in the same set of subjects after fecal microbiota transplantation. These results fundamentally improve our understanding of forces and processes shaping human microbial ecosystems, paving the way to design general microbiome-based therapies.","accessed":{"date-parts":[["2025",5,5]]},"author":[{"family":"Bashan","given":"Amir"},{"family":"Gibson","given":"Travis E."},{"family":"Friedman","given":"Jonathan"},{"family":"Carey","given":"Vincent J."},{"family":"Weiss","given":"Scott T."},{"family":"Hohmann","given":"Elizabeth L."},{"family":"Liu","given":"Yang-Yu"}],"citation-key":"bashanUniversalityHumanMicrobial2016","container-title":"Nature","container-title-short":"Nature","DOI":"10.1038/nature18301","ISSN":"0028-0836","issue":"7606","issued":{"date-parts":[["2016",6,8]]},"note":"Read_Status: New\nRead_Status_Date: 2025-05-06T13:59:51.849Z","page":"259–262","PMCID":"PMC4902290","PMID":"27279224","source":"PubMed Central","title":"Universality of Human Microbial Dynamics","type":"article-journal","URL":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4902290/","volume":"534"},
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{"id":"faustOpenChallengesMicrobial2021","abstract":"Microbial network construction is a popular explorative data analysis technique in microbiome research. Although a large number of microbial network construction tools has been developed to date, there are several issues concerning the construction and interpretation of microbial networks that have received less attention. The purpose of this perspective is to draw attention to these underexplored challenges of microbial network construction and analysis.","accessed":{"date-parts":[["2025",5,5]]},"author":[{"family":"Faust","given":"Karoline"}],"citation-key":"faustOpenChallengesMicrobial2021","container-title":"The ISME Journal","container-title-short":"The ISME Journal","DOI":"10.1038/s41396-021-01027-4","ISSN":"1751-7362","issue":"11","issued":{"date-parts":[["2021",11,1]]},"note":"Read_Status: New\nRead_Status_Date: 2025-05-06T13:59:51.849Z","page":"3111–3118","source":"Silverchair","title":"Open challenges for microbial network construction and analysis","type":"article-journal","URL":"https://doi.org/10.1038/s41396-021-01027-4","volume":"15"},
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{"id":"gusevaDiversityComplexityMicrobial2022","abstract":"Network analysis has been used for many years in ecological research to analyze organismal associations, for example in food webs, plant-plant or plant-animal interactions. Although network analysis is widely applied in microbial ecology, only recently has it entered the realms of soil microbial ecology, shown by a rapid rise in studies applying co-occurrence analysis to soil microbial communities. While this application offers great potential for deeper insights into the ecological structure of soil microbial ecosystems, it also brings new challenges related to the specific characteristics of soil datasets and the type of ecological questions that can be addressed. In this Perspectives Paper we assess the challenges of applying network analysis to soil microbial ecology due to the small-scale heterogeneity of the soil environment and the nature of soil microbial datasets. We review the different approaches of network construction that are commonly applied to soil microbial datasets and discuss their features and limitations. Using a test dataset of microbial communities from two depths of a forest soil, we demonstrate how different experimental designs and network constructing algorithms affect the structure of the resulting networks, and how this in turn may influence ecological conclusions. We will also reveal how assumptions of the construction method, methods of preparing the dataset, and definitions of thresholds affect the network structure. Finally, we discuss the particular questions in soil microbial ecology that can be approached by analyzing and interpreting specific network properties. Targeting these network properties in a meaningful way will allow applying this technique not in merely descriptive, but in hypothesis-driven research. Analysing microbial networks in soils opens a window to a better understanding of the complexity of microbial communities. However, this approach is unfortunately often used to draw conclusions which are far beyond the scientific evidence it can provide, which has damaged its reputation for soil microbial analysis. In this Perspectives Paper, we would like to sharpen the view for the real potential of microbial co-occurrence analysis in soils, and at the same time raise awareness regarding its limitations and the many ways how it can be misused or misinterpreted.","accessed":{"date-parts":[["2025",5,6]]},"author":[{"family":"Guseva","given":"Ksenia"},{"family":"Darcy","given":"Sean"},{"family":"Simon","given":"Eva"},{"family":"Alteio","given":"Lauren V."},{"family":"Montesinos-Navarro","given":"Alicia"},{"family":"Kaiser","given":"Christina"}],"citation-key":"gusevaDiversityComplexityMicrobial2022","container-title":"Soil Biology and Biochemistry","container-title-short":"Soil Biology and Biochemistry","DOI":"10.1016/j.soilbio.2022.108604","ISSN":"0038-0717","issued":{"date-parts":[["2022",6,1]]},"note":"Read_Status: New\nRead_Status_Date: 2025-05-06T14:00:11.153Z","page":"108604","source":"ScienceDirect","title":"From diversity to complexity: Microbial networks in soils","title-short":"From diversity to complexity","type":"article-journal","URL":"https://www.sciencedirect.com/science/article/pii/S003807172200061X","volume":"169"},
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{"id":"heNetworkMappingRoot2021","abstract":"Understanding how plants interact with their colonizing microbiota to determine plant phenotypes is a fundamental question in modern plant science. Existing approaches for genome-wide association studies (GWAS) are often focused on the association analysis between host genes and the abundance of individual microbes, failing to characterize the genetic bases of microbial interactions that are thought to be important for microbiota structure, organization, and function. Here, we implement a behavioral model to quantify various patterns of microbe-microbe interactions, i.e., mutualism, antagonism, aggression, and altruism, and map host genes that modulate microbial networks constituted by these interaction types. We reanalyze a root-microbiome data involving 179 accessions of Arabidopsis thaliana and find that the four networks differ structurally in the pattern of bacterial-fungal interactions and microbiome complexity. We identify several fungus and bacterial hubs that play a central role in mediating microbial community assembly surrounding A. thaliana root systems. We detect 1142 significant host genetic variants throughout the plant genome and then implement Bayesian networks (BN) to reconstruct epistatic networks involving all significant SNPs, of which 91 are identified as hub QTLs. Results from gene annotation analysis suggest that most of the hub QTLs detected are in proximity to candidate genes, executing a variety of biological functions in plant growth and development, resilience against pathogens, root development, and abiotic stress resistance. This study provides a new gateway to understand how genetic variation in host plants influences microbial communities and our results could help improve crops by harnessing soil microbes.","accessed":{"date-parts":[["2025",5,6]]},"author":[{"family":"He","given":"Xiaoqing"},{"family":"Zhang","given":"Qi"},{"family":"Li","given":"Beibei"},{"family":"Jin","given":"Yi"},{"family":"Jiang","given":"Libo"},{"family":"Wu","given":"Rongling"}],"citation-key":"heNetworkMappingRoot2021","container-title":"NPJ Biofilms and Microbiomes","container-title-short":"NPJ Biofilms Microbiomes","DOI":"10.1038/s41522-021-00241-4","ISSN":"2055-5008","issued":{"date-parts":[["2021",9,7]]},"note":"Read_Status: New\nRead_Status_Date: 2025-05-06T13:59:20.378Z","page":"72","PMCID":"PMC8423736","PMID":"34493731","source":"PubMed Central","title":"Network mapping of root–microbe interactions in Arabidopsis thaliana","type":"article-journal","URL":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8423736/","volume":"7"},
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{"id":"matchadoNetworkAnalysisMethods2021","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.","accessed":{"date-parts":[["2025",5,6]]},"author":[{"family":"Matchado","given":"Monica Steffi"},{"family":"Lauber","given":"Michael"},{"family":"Reitmeier","given":"Sandra"},{"family":"Kacprowski","given":"Tim"},{"family":"Baumbach","given":"Jan"},{"family":"Haller","given":"Dirk"},{"family":"List","given":"Markus"}],"citation-key":"matchadoNetworkAnalysisMethods2021","container-title":"Computational and Structural Biotechnology Journal","container-title-short":"Computational and Structural Biotechnology Journal","DOI":"10.1016/j.csbj.2021.05.001","ISSN":"2001-0370","issued":{"date-parts":[["2021",1,1]]},"note":"Read_Status: New\nRead_Status_Date: 2025-05-06T13:59:53.425Z","page":"2687–2698","source":"ScienceDirect","title":"Network analysis methods for studying microbial communities: A mini review","title-short":"Network analysis methods for studying microbial communities","type":"article-journal","URL":"https://www.sciencedirect.com/science/article/pii/S2001037021001823","volume":"19"},
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{"id":"PhylogeneticLatentSpace","accessed":{"date-parts":[["2026",5,11]]},"citation-key":"PhylogeneticLatentSpace","note":"Read_Status: New\nRead_Status_Date: 2026-05-12T08:46:21.525Z","title":"Phylogenetic Latent Space Models for Network Data","type":"webpage","URL":"https://arxiv.org/html/2502.11868v2"},
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{"id":"raoFederatedVariationalInference2025","abstract":"We present a one-shot, unsupervised federated learning approach for Bayesian modelbased clustering of large-scale binary and categorical datasets, motivated by the need to identify patient clusters in privacy-sensitive electronic health record (EHR) data. We introduce a principled ‘divide-and-conquer’ inference procedure using variational inference with local merge and delete moves within batches of the data in parallel, followed by ‘global’ merge moves across batches to find global clustering structures. We show that these merge moves require only summaries of the data in each batch, enabling federated learning across local nodes without requiring the full dataset to be shared. Empirical results on simulated and benchmark datasets demonstrate that our method performs well relative to comparator clustering algorithms. We validate the practical utility of the method by applying it to a large-scale British primary care EHR dataset to identify clusters of individuals with common patterns of co-occurring conditions (multimorbidity).","accessed":{"date-parts":[["2026",3,23]]},"author":[{"family":"Rao","given":"Jackie"},{"family":"Crowe","given":"Francesca L."},{"family":"Marshall","given":"Tom"},{"family":"Richardson","given":"Sylvia"},{"family":"Kirk","given":"Paul D. W."}],"citation-key":"raoFederatedVariationalInference2025","DOI":"10.48550/arXiv.2502.12684","issued":{"date-parts":[["2025",11,12]]},"language":"en","note":"Read_Status: New\nRead_Status_Date: 2026-03-27T14:34:39.992Z","number":"arXiv:2502.12684","publisher":"arXiv","source":"arXiv.org","title":"Federated Variational Inference for Bayesian Mixture Models","type":"article","URL":"http://arxiv.org/abs/2502.12684"}
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