AIMC Topic: RNA, Ribosomal, 16S

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Machine learning-based mapping of Acidobacteriota and Planctomycetota using 16 S rRNA gene metabarcoding data across soils in Russia.

Scientific reports
The soil microbiome plays a crucial role in maintaining healthy ecosystems and supporting sustainable agriculture. Studying its biogeographical structure and distribution is essential for understanding the rates and mechanisms of microbially mediated...

TaxaCal: enhancing species-level profiling accuracy of 16S amplicon data.

BMC bioinformatics
BACKGROUND: 16S rRNA amplicon sequencing is a widely used method for microbiome composition analysis due to its cost-effectiveness and lower data requirements compared to metagenomic whole-genome sequencing (WGS). However, inherent limitations in 16S...

Distinguishing critical microbial community shifts from normal temporal variability in human and environmental ecosystems.

Scientific reports
Differentiating significant microbial community changes from normal fluctuations is vital for understanding microbial dynamics in human and environmental ecosystems. This knowledge could enable early warning systems to monitor critical changes affect...

Interpretable machine learning algorithms reveal gut microbiome features associated with atopic dermatitis.

Frontiers in immunology
BACKGROUND: The "gut-skin axis" has been proposed to play an important role in the development and symptoms of atopic dermatitis. Therefore, we have constructed an interpretable machine learning framework to quantitatively screen key gut flora.

Gut microbiota-derived extracellular vesicles form a distinct entity from gut microbiota.

mSystems
Extracellular vesicles (EVs), nanoparticles secreted by both gram-negative and gram-positive bacteria, carry various biomolecules and cross biological barriers. Gut microbiota-derived EVs are currently being investigated as a communication mechanism ...

Accurate prediction of absolute prokaryotic abundance from DNA concentration.

Cell reports methods
Quantification of the absolute microbial abundance in a human stool sample is crucial for a comprehensive understanding of the microbial ecosystem, but this information is lost upon metagenomic sequencing. While several methods exist to measure absol...

The inconsistent pathogenesis of endometriosis and adenomyosis: insights from endometrial metabolome and microbiome.

mSystems
UNLABELLED: Endometriosis (EM) and adenomyosis (AM) are interrelated gynecological disorders characterized by the aberrant presence of endometrial tissue and are frequently linked with chronic pelvic pain and infertility, yet their pathogenetic mecha...

A Comprehensive Evaluation of Taxonomic Classifiers in Marine Vertebrate eDNA Studies.

Molecular ecology resources
Environmental DNA (eDNA) metabarcoding is a widely used tool for surveying marine vertebrate biodiversity. To this end, many computational tools have been released and a plethora of bioinformatic approaches are used for eDNA-based community compositi...

Unique Microbial Characterisation of Oesophageal Squamous Cell Carcinoma Patients with Different Dietary Habits Based on Light Gradient Boosting Machine Learning Classifier.

Nutrients
: The microbiome plays an important role in cancer, but the relationship between dietary habits and the microbiota in oesophageal squamous cell carcinoma (ESCC) is not clear. The aim of this study is to explore the complex relationship between the mi...

Gut microbiota shift in Ghanaian individuals along the migration axis: the RODAM-Pros cohort.

Gut microbes
Migration is associated with a substantial change in environmental exposures and health outcomes. We aimed to investigate the shift in gut microbiota composition and the associations with cardiometabolic outcomes in the RODAM-Pros cohort spanning mul...