DADA2: High-resolution sample inference from Illumina amplicon data
Benjamin J. Callahan,Paul J. McMurdie,Michael J. Rosen,Andrew W. Han,Amy Jo A. Johnson,Susan Holmes +5 more
TLDR
The open-source software package DADA2 for modeling and correcting Illumina-sequenced amplicon errors is presented, revealing a diversity of previously undetected Lactobacillus crispatus variants.Abstract:
We present the open-source software package DADA2 for modeling and correcting Illumina-sequenced amplicon errors (https://github.com/benjjneb/dada2). DADA2 infers sample sequences exactly and resolves differences of as little as 1 nucleotide. In several mock communities, DADA2 identified more real variants and output fewer spurious sequences than other methods. We applied DADA2 to vaginal samples from a cohort of pregnant women, revealing a diversity of previously undetected Lactobacillus crispatus variants.read more
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DNA-SIP based genome-centric metagenomics identifies key long-chain fatty acid-degrading populations in anaerobic digesters with different feeding frequencies
TL;DR: The results suggest that syntrophic populations in anaerobic digesters can have different adaptive capacities, and that selection for divergent populations may be achieved by adjusting reactor operating conditions to maximize biomethane recovery.
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Metagenomics methods for the study of plant-associated microbial communities: A review.
TL;DR: There is need to adopt advanced genomic features such as k-mers of random size, which do not depend on annotation and can represent other sequence alternatives, in processing plant associated metagenomes with concentration on endophytes.
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Exact sequence variants should replace operational taxonomic units in marker gene data analysis
TL;DR: It is argued that the improvements in reusability, reproducibility and comprehensiveness are sufficiently great that SVs should replace OTUs as the standard unit of marker gene analysis and reporting.
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Machine learning applications in microbial ecology, human microbiome studies, and environmental monitoring.
TL;DR: In this paper, the authors review current trends in machine learning applications in microbial ecology as well as some of the important challenges and opportunities for more broad application of machine learning to understand microbial communities.
Journal ArticleDOI
Changes in the composition and function of bacterial communities during vermicomposting may explain beneficial properties of vermicompost.
Jorge Domínguez,Manuel Aira,Allison R. Kolbe,María Gómez-Brandón,Marcos Pérez-Losada,Marcos Pérez-Losada +5 more
TL;DR: Increases in taxonomic diversity were accompanied by increases in functional diversity of the bacterial community, including metabolic capacity, streptomycin and salicylic acid synthesis, and nitrification, which highlight the role of bacterial succession during the vermicomposting process and provide evidence of microbial functions that may explain the beneficial effects of vermicumpost on soil and plants.
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TL;DR: The Human Microbiome Project Consortium reported the first results of their analysis of microbial communities from distinct, clinically relevant body habitats in a human cohort; the insights into the microbial communities of a healthy population lay foundations for future exploration of the epidemiology, ecology and translational applications of the human microbiome as discussed by the authors.
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