Critical review of 16S rRNA gene sequencing workflow in microbiome studies: From primer selection to advanced data analysis

Molecular Oral Microbiology, Journal Year: 2023, Volume and Issue: 38(5), P. 347 - 399

Published: Oct. 1, 2023

Abstract The multi‐batch reanalysis approach of jointly reevaluating gene/genome sequences from different works has gained particular relevance in the literature recent years. large amount 16S ribosomal ribonucleic acid (rRNA) gene sequence data stored public repositories and information taxonomic databases same far exceeds that related to complete genomes. This review is intended guide researchers new studying microbiota, particularly oral using rRNA sequencing those who want expand update their knowledge optimise decision‐making improve research results. First, we describe advantages disadvantages as a phylogenetic marker latest findings on impact primer pair selection diversity assignment outcomes microbiome studies. Strategies for based these results are introduced. Second, identified key factors consider selecting technology platform. process particularities main steps processing gene‐derived described detail enable choose most appropriate bioinformatics pipeline analysis methods available evidence. We then produce an overview types advanced analyses, both widely used approaches. Several indices, metrics software microbial communities included, highlighting disadvantages. Considering principles clinical metagenomics, conclude future should focus rigorous analytical approaches, such developing predictive models identify microbiome‐based biomarkers classify health disease states. Finally, address batch effect concept microbiome‐specific accounting or correcting them.

Language: Английский

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A systematic discussion and comparison of the construction methods of synthetic microbial community

Synthetic and Systems Biotechnology, Journal Year: 2024, Volume and Issue: 9(4), P. 775 - 783

Published: June 20, 2024

Synthetic microbial community has widely concerned in the fields of agriculture, food and environment over past few years. However, there is little consensus on method to synthetic from construction functional verification. Here, we review concept, characteristics, history applications community, summarizing several methods for construction, such as isolation culture, core microbiome mining, automated design, gene editing. In addition, also systematically summarized design concepts, technological thresholds, applicable scenarios various methods, highlighted their advantages limitations. Ultimately, this provides four efficient, detailed, easy-to-understand -follow steps with major implications agricultural practices, production, environmental governance.

Language: Английский

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Cross-validation for training and testing co-occurrence network inference algorithms

BMC Bioinformatics, Journal Year: 2025, Volume and Issue: 26(1)

Published: March 6, 2025

Microorganisms are found in almost every environment, including soil, water, air and inside other organisms, such as animals plants. While some microorganisms cause diseases, most of them help biological processes decomposition, fermentation nutrient cycling. Much research has been conducted on the study microbial communities various environments how their interactions relationships can provide insight into diseases. Co-occurrence network inference algorithms us understand complex associations micro-organisms, especially bacteria. Existing employ techniques correlation, regularized linear regression, conditional dependence, which have different hyper-parameters that determine sparsity network. These form intricate ecological networks fundamental to ecosystem functioning host health. Understanding these is crucial for developing targeted interventions both environmental clinical settings. The emergence high-throughput sequencing technologies generated unprecedented amounts microbiome data, necessitating robust computational methods validation. Previous evaluating quality inferred include using external consistency across sub-samples, several drawbacks limit applicability real composition data sets. We propose a novel cross-validation method evaluate co-occurrence algorithms, new applying existing predict test data. Our demonstrates superior performance handling compositional addressing challenges high dimensionality inherent datasets. proposed framework also provides estimates stability. empirical shows useful hyper-parameter selection (training) comparing between (testing). This advancement represents significant step forward analysis, providing researchers with reliable tool understanding interactions. method's extends beyond studies fields where from high-dimensional crucial, gene regulatory food webs. establishes standard validation inference, potentially accelerating discoveries ecology human

Language: Английский

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The rhizosphere microbiome can sustainably protect field-grown tomato crops against soil-borne pathogens and plant parasitic nematodes

Canadian Journal of Plant Pathology, Journal Year: 2025, Volume and Issue: unknown, P. 1 - 14

Published: April 4, 2025

Language: Английский

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animalcules: interactive microbiome analytics and visualization in R

Microbiome, Journal Year: 2021, Volume and Issue: 9(1)

Published: March 28, 2021

Abstract Background Microbial communities that live in and on the human body play a vital role health disease. Recent advances sequencing technologies have enabled study of microbial at unprecedented resolution. However, these data generation presented novel challenges to researchers attempting analyze visualize data. Results To address some challenges, we developed animalcules , an easy-to-use interactive microbiome analysis toolkit for 16S rRNA data, shotgun DNA metagenomics RNA-based metatranscriptomics profiling This combines existing analytics, visualization methods, machine learning models. For example, features traditional analyses such as alpha/beta diversity differential abundance analysis, combined with new methods biomarker identification are. In addition, provides dynamic figures enable users understand their discover insights. can be used standalone command-line R package or explore accompanying Shiny interface. Conclusions We present through either interface facilitated by various functions. It is first supports all rRNA, DNA-based metagenomics, RNA-sequencing based datasets. freely downloaded from GitHub https://github.com/compbiomed/animalcules installed Bioconductor https://www.bioconductor.org/packages/release/bioc/html/animalcules.html .

Language: Английский

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Capturing the dynamics of microbial interactions through individual-specific networks

Frontiers in Microbiology, Journal Year: 2023, Volume and Issue: 14

Published: May 15, 2023

Longitudinal analysis of multivariate individual-specific microbiome profiles over time or across conditions remains dauntin. Most statistical tools and methods that are available to study microbiomes based on cross-sectional data. Over the past few years, several attempts have been made model dynamics bacterial species conditions. However, field needs novel views handling microbial interactions in temporal analyses. This proposes a data framework, MNDA, combines representation learning co-occurrence networks uncover taxon neighborhood dynamics. As use case, we consider cohort newborns with at 6 9 months after birth, extraneous mode delivery diet changes between considered points. Our results show prediction models for these outcomes an MNDA measure local each outperform traditional solely abundances. Furthermore, our unsupervised similarity study, again using notion taxon's dynamic derived from time-matched networks, can reveal different subpopulations individuals, compared standard microbiome-based clustering, potential relevance clinical practice. highlights complementarity abundances downstream analyses opens new avenues personalized stratified medicine

Language: Английский

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The structure and function of rhizosphere bacterial communities: impact of chemical vs. bio-organic fertilizers on root disease, quality, and yield of Codonopsis pilosula

Frontiers in Microbiology, Journal Year: 2024, Volume and Issue: 15

Published: Oct. 21, 2024

Introduction Long-term use of chemical fertilizers (CFs) can cause soil compaction and acidification. In recent years, bio-organic (BOFs) have begun to replace CFs in some vegetables cash crops, but the application or BOFs has resulted crop quality disease occurrence. Methods This study aimed analyze microbial mechanism differences between root disease, quality, yield tuber Chinese herbal medicine. We studied effects CFs, organic fertilizers, commercial BOFs, biocontrol bacteria fungi on rhizosphere community structure function, rot, Codonopsis pilosula at different periods after analyzed correlation. Results discussion Compared emergence rate BOF treatments were increased by 21.12 33.65%, respectively, ash content, water index decreased 17.87, 8.19, 76.60%, respectively. The structural equation model showed that promoted C. influencing environmental factors, while directly drove bacterial reduce improve . There was a stronger interaction stability networks treatments. Microlunatus , Rubrobacter Luteitalea Nakamurella Pedomicrobium identified as effector bacteria, which related prevention increase Microbial functional analysis indicated signal transduction amino acid metabolism might play major role improving early middle growth stages. conclusion, compared obtained lower rot higher changing function community.

Language: Английский

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SpeSpeNet: An interactive and user-friendly tool to create and explore microbial correlation networks

bioRxiv (Cold Spring Harbor Laboratory), Journal Year: 2024, Volume and Issue: unknown

Published: July 22, 2024

Abstract Correlation networks are commonly used to explore microbiome data. In these networks, nodes taxa and edges represent correlations between their abundance patterns across samples. As clusters of correlating (co-abundance clusters) often indicate a shared response environmental drivers, network visualization contributes system understanding. Currently, most tools for creating visualizing co-abundance from data either require the researcher have coding skills, or they not user-friendly, with high time expenditure limited customizability. Furthermore, existing lack focus on relationship drivers structure microbiome, even though many in correlation can be understood through two environment. For reasons we developed SpeSpeNet (Species-Species Network, https://tbb.bio.uu.nl/SpeSpeNet ), practical user-friendly R-shiny tool construct visualize taxonomic tables. The details preprocessing, construction, automated, no programming ability web version, highly customizable, including associations user-provided Here, present demonstrate its utility using three case studies.

Language: Английский

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A novel robust network construction and analysis workflow for mining infant microbiota relationships

mSystems, Journal Year: 2024, Volume and Issue: unknown

Published: Dec. 31, 2024

The gut microbiota plays a crucial role in infant health, with its development during the first 1,000 days influencing health outcomes. Understanding relationships within is essential to linking maturation process these Several network-based methods have been developed analyze developing patterns of microbiota, but evaluating reliability and effectiveness approaches remains challenge. In this study, we created test data pool using public microbiome sets assess performance four different methods, employing repeated sampling strategies. We found that our proposed Probability-Based Co-Detection Model (PBCDM) demonstrated best stability robustness, particularly network attributes such as node counts, average links per node, positive-to-negative link (P/N) ratios. Using PBCDM, constructed microbial co-existence networks for infants at various ages, identifying core genera through novel shearing method. Analysis revealed were more similar between adjacent age ranges, increasing competitive among matured. conclusion, PBCDM-based reflect known features offer promising approach investigating relationships. This methodology could also be applied future studies genomic, metabolic, proteomic data.

Language: Английский

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