Microbial Community Ecology and Physiology

The paper predicts microbial community composition based on metabolic similarities across environments by analyzing growth rates and secretion profiles of proteobacterial strains.

The paper analyzes subsurface microbiomes globally, finding a marine-terrestrial phylogenetic divide and revealing that marine subsurface environments host significant microbial diversity.

This paper evaluates nanopore sequencing for bacterial community analysis in marine sediments, finding similar results to short-read sequencing despite higher error rates.

The paper examines microbial community and activity patterns in the Guaymas Basin subsurface along thermal and geochemical gradients using metagenomic and metatranscriptomic analyses.

The paper investigates the bacterial microbiota of Ostreobium algae under different salinities, finding that host genotype structures the bacterial community more than salinity.

The paper quantifies microbial niche breadth using environmental sequencing data, revealing generalist and specialist strategies across the tree of life.

The paper uses metatranscriptomics to study microbial community shifts in coastal waters, finding seasonal changes in composition and function linked to temperature and pCO2.

The paper analyzes microbial community changes in Amazon mangroves along a highway, finding biodiversity loss in impacted areas.

The paper customizes a protein sequence database to improve systems-level metabolic pathway analysis in marine planktonic ecosystems using metatranscriptome data.

The paper analyzes bacterial communities in arsenic-contaminated tailings dam effluent using metagenomic sequencing and compares them to groundwater samples.

The paper characterizes the microbial community of Auka vents, revealing high diversity, novel metabolic traits, and evidence of long-distance species transfer and thermophilic evolution.

The paper analyzes bacterial communities in Yamuna River water samples from Agra city using metagenomics to identify dominant taxa and assess heavy metal contamination.

The paper applies statistical methods from quantitative microbiology to improve diversity analysis of amplicon sequencing data by addressing random error and model limitations.

The paper introduces a deep learning framework to predict microbial community compositions from species assemblages using synthetic and experimental data.

The paper evaluates the use of repeated rarefying to normalize amplicon sequencing data for microbial community diversity analysis, showing its utility in representing sequence proportions and characterizing rarefaction effects.

The paper introduces MicNet Toolbox, a python tool for visualizing microbial communities and analyzing co-occurrence networks using UMAP, HDBSCAN, and an enhanced SparCC algorithm.

The paper analyzes the microbiome of wrackbeds and seaweed flies along the North Sea-Baltic Sea transition, finding shifts in microbial communities and functionality linked to polysaccharide degradation.

The paper presents high-resolution global ocean metagenomes collected as part of the Bio-GO-SHIP program to study microbial community dynamics and their link to biogeochemical fluxes.

The paper profiles host-associated bacteria of Porphyra purpurea using metagenomic datasets and MetaPhlAn 3.0, showing differences in taxonomic resolution compared to 16S rRNA analysis.

This paper predicts microbial composition from environmental features using a deep latent space model, achieving high correlation in maize rhizosphere microbiomes.

The paper examines soil bacterial community diversity and composition across elevation and seasons in western Himalayan alpine habitats, finding greater variation with elevation than seasons.

The paper constructs a sulfur metabolism database (SMDB) to analyze sulfate reduction in subtropical mangroves, identifying Desulfobacterales and Chromatiales as key taxa involved.

The paper identifies a novel Thaumarchaeota genus in the Atacama Desert hyperarid core, revealing its role in carbon and nitrogen cycling through genomic analysis.

The paper investigates microbial community structure and metabolic potential along a hillslope to riparian zone transect using genome-resolved metagenomics and stable isotopes, finding distinct communities and functions across depth and landscape position.

Tax4Fun2 predicts habitat-specific functional profiles and redundancy from 16S rRNA gene sequences using user-defined genomic information.

POGENOM quantifies population genomic indices from metagenome data, revealing environmental correlates of genomic variation in bacterioplankton across spatial and temporal scales.

This study presents the first metagenomic analysis of a bivalve symbiont, revealing its broad metabolic potential and genomic features.

The paper identifies metabolic complementarities between a brown alga and its associated bacteria, showing that selected bacterial consortia enhance algal growth and metabolite exchange.