MicrobiotaProcess: A comprehensive R package for deep mining microbiome

Xu, Shuaimei; Zhan, Li; Tang, Wenli; Wang, Qianwen; Dai, Zhigang; Zhou, Lang; Feng, Tingze; Chen, Meijun; Wu, Tianzhi; Hu, Erqiang; Guo, Yu

doi:10.1016/j.xinn.2023.100388

Cited by 62 publications

(55 citation statements)

References 68 publications

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“…1a). This object distinguishes itself through its seamless conversion capability to external data structures from different tools 14,15 , rendering microbiomedataset highly adaptable for diverse analytical needs (Fig. 1b).…”

Section: Results Implementationmentioning

confidence: 99%

“…Its appeal stems from its innate simplicity and efficacy in organizing and processing data 10 . In recent times, a plethora of tools have been devised to address distinct omics data processing and analysis needs, including notable initiatives such as the tidymass project 11 , tidyomics project 12 , tidymicro 13 , and MicrobiotaProcess 13,14 . However, a conspicuous gap persists in the form of a standardized, tidyverse-based package for seamless and rigorous microbiome data processing and analysis.…”

Section: Introductionmentioning

confidence: 99%

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microbiomedataset: A tidyverse-style framework for organizing and processing microbiome data

Shen,

Snyder

2023

Preprint

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Microbial communities exert a substantial influence on human health and have been unequivocally associated with a spectrum of human maladies, encompassing conditions such as anxiety1, depression2, hypertension3, cardiovascular diseases4, obesity4,5, diabetes6, inflammatory bowel disease7, and cancer8,9. This intricate interplay between microbiota community structures and host pathophysiology has kindled substantial interest and spurred active research endeavors across various scientific domains. Despite significant strides in sequencing technologies, which have unveiled the vast diversity of microbial populations across diverse ecosystems, the analysis of microbiome data remains a formidable challenge. The complexity inherent in such data, compounded by the absence of standardized data processing and analysis workflows, continues to pose substantial hurdles. The tidyverse paradigm, comprised of a suite of R packages meticulously crafted to facilitate efficient data manipulation and visualization, has garnered considerable acclaim within the data science community10. Its appeal stems from its innate simplicity and efficacy in organizing and processing data10. In recent times, a plethora of tools have been devised to address distinct omics data processing and analysis needs, including notable initiatives such as the tidymass project11, tidyomics project12, tidymicro13, and MicrobiotaProcess13,14. However, a conspicuous gap persists in the form of a standardized, tidyverse-based package for seamless and rigorous microbiome data processing and analysis. To address this burgeoning demand for standardized and reproducible microbiome data analysis, we introduce microbiomedataset, an R package that embraces the tidyverse ethos to furnish a structured framework for the organization and processing of microbiome data. Microbiomedataset offers a comprehensive, customizable solution for the management, structuring, and processing of microbiome data. Importantly, this package seamlessly integrates with established bioinformatics tools, facilitating its incorporation into existing analytical pipelines11,13,14,15. Within this manuscript, we proffer an in-depth overview of the microbiomedataset package, elucidating its multifarious functionalities. Moreover, we substantiate its utility through illustrative case studies employing a publicly available microbiome dataset. It is imperative to underscore that microbiomedataset constitutes an integral component of the larger tidymicrobiome project, accessible via www.tidymicrobiome.org. Tidymicrobiome epitomizes an ecosystem of R packages that share a coherent design philosophy, grammar, and data structure, collectively engendering a robust, reproducible, and object-oriented computational framework. This project's development has been guided by several key tenets: (1) Cross-platform compatibility, (2) Uniformity, shareability, traceability, and reproducibility, and (3) Flexibility and extensibility. We further expound upon the advantages inherent in adopting a tidyverse-style framework for microbiome data analysis, underscoring the pronounced benefits in terms of standardization and reproducibility that microbiomedataset offers. In sum, microbiomedataset furnishes an accessible and efficient avenue for microbiome data analysis, catering to both neophyte and seasoned R users alike.

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Section: Results Implementationmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

microbiomedataset: A tidyverse-style framework for organizing and processing microbiome data

Shen,

Snyder

2023

Preprint

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“…The Phyloseq objects were rarefied to minimum depths of 14,806 reads and 5,686 reads for 16S and 28S ASVs, respectively. Chao1, Shannon, and Faith's phylogenetic diversity alpha diversity measurements (Figures S3 and S4 in Supporting Information ) were conducted with the packages, btools and MicrobiotaProcess (Xu et al., 2023). The Chao1 index examines taxonomic richness with a particular emphasis on rare taxa (Kim et al., 2017).…”

Section: Methodsmentioning

confidence: 99%

Comparing Sediment Microbial Communities of Arctic Beaver Ponds to Tundra Lakes and Streams

et al. 2023

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In recent decades the habitat of North American beaver (Castor canadensis) has expanded from boreal forests into Arctic tundra ecosystems. Beaver ponds in Arctic watersheds are known to alter stream biogeochemistry, which is likely coupled with changes in the activity and composition of microbial communities inhabiting beaver pond sediments. We investigated bacterial, archaeal, and fungal communities in beaver pond sediments along tundra streams in northwestern Alaska (AK), USA and compared them to those of tundra lakes and streams in north‐central Alaska that are unimpacted by beavers. β‐glucosidase activity assays indicated higher cellulose degradation potential in beaver ponds than in unimpacted streams and lakes within a watershed absent of beavers. Beta diversity analyses showed that dominant lineages of bacteria and archaea in beaver ponds differed from those in tundra lakes and streams, but dominant fungal lineages did not differ between these sample types. Beaver pond sediments displayed lower relative abundances of Crenarchaeota and Euryarchaeota archaea and of bacteria from typically anaerobic taxonomic groups, suggesting differences in rates of fermentative organic matter (OM) breakdown, syntrophy, and methane generation. Beaver ponds also displayed low relative abundances of Chytridiomycota (putative non‐symbiotic) fungi and high relative abundances of ectomycorrhizal (plant symbionts) Basidiomycota fungi, suggesting differences in the occurrence of plant and fungi mutualistic interactions. Beaver ponds also featured microbes with taxonomic identities typically associated with the cycling of nitrogen and sulfur compounds in higher relative abundances than tundra lakes and streams. These findings help clarify the microbiological implications of beavers expanding into high latitude regions.

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“…The remaining reads were assigned taxonomy using the Ribosomal Database Project (RDP trainset 16/release 11.5) 16S rRNA gene sequence database (47). Ecological analyses, such as alpha-diversity (richness, Faith’s phylogenetic diversity) and beta-diversity analyses (unweighted and weighted UniFrac distances), were performed using PhyloSeq and additional R packages(48), and differentially abundant taxa between sample groups were identified by performing pairwise comparisons using LEfSe and MicrobiotaProcess package(49), (50). 16S rRNA gene sequencing data have been uploaded to the European Nucleotide Archive (accession no.…”

Section: Methodsmentioning

confidence: 99%

Sequential early-life viral infections modulate the microbiota and adaptive immune responses to systemic and mucosal vaccination

Li,

Molleston,

Kim

et al. 2023

Preprint

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Increasing evidence points to the microbial exposome as a critical factor in maturing and shaping the host immune system, thereby influencing responses to immune challenges such as infections or vaccines. To investigate the effect of early-life viral exposures on immune development and vaccine responses, we inoculated mice with six distinct viral pathogens in sequence beginning in the neonatal period, and then evaluated their immune signatures before and after intramuscular or intranasal vaccination against SARS-CoV-2. Sequential viral infection drove profound changes in all aspects of the immune system, including increasing circulating leukocytes, altering innate and adaptive immune cell lineages in tissues, and markedly influencing serum cytokine and total antibody levels. Beyond these immune responses changes, these exposures also modulated the composition of the endogenous intestinal microbiota. Although sequentially-infected mice exhibited increased systemic immune activation and T cell responses after intramuscular and intranasal SARS-CoV-2 immunization, we observed decreased vaccine-induced antibody responses in these animals. These results suggest that early-life viral exposures are sufficient to diminish antibody responses to vaccination in mice, and highlight their potential importance of considering prior microbial exposures when investigating vaccine responses.

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MicrobiotaProcess: A comprehensive R package for deep mining microbiome

Cited by 62 publications

References 68 publications

microbiomedataset: A tidyverse-style framework for organizing and processing microbiome data

microbiomedataset: A tidyverse-style framework for organizing and processing microbiome data

Comparing Sediment Microbial Communities of Arctic Beaver Ponds to Tundra Lakes and Streams

Sequential early-life viral infections modulate the microbiota and adaptive immune responses to systemic and mucosal vaccination

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