Decoupling function and taxonomy in the global ocean microbiome

Science, Год журнала: 2016, Номер 353(6305), С. 1272 - 1277

Опубликована: Сен. 15, 2016

Microbial metabolism powers biogeochemical cycling in Earth’s ecosystems. The taxonomic composition of microbial communities varies substantially between environments, but the ecological causes this variation remain largely unknown. We analyzed and functional community profiles to determine factors that shape marine bacterial archaeal across global ocean. By classifying >30,000 microorganisms into metabolic groups, we were able disentangle from variation. find environmental conditions strongly influence distribution groups by shaping niches, only weakly within individual groups. Hence, structure constitute complementary roughly independent “axes variation” shaped markedly different processes.

Язык: Английский

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Best practices for analysing microbiomes

Nature Reviews Microbiology, Год журнала: 2018, Номер 16(7), С. 410 - 422

Опубликована: Май 23, 2018

Язык: Английский

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Using MicrobiomeAnalyst for comprehensive statistical, functional, and meta-analysis of microbiome data

Nature Protocols, Год журнала: 2020, Номер 15(3), С. 799 - 821

Опубликована: Янв. 15, 2020

Язык: Английский

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Microbial community structure and its functional implications

Nature, Год журнала: 2009, Номер 459(7244), С. 193 - 199

Опубликована: Май 1, 2009

Язык: Английский

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Marine microbial community dynamics and their ecological interpretation

Nature Reviews Microbiology, Год журнала: 2015, Номер 13(3), С. 133 - 146

Опубликована: Фев. 9, 2015

Язык: Английский

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Characterization of the Gut Microbiome Using 16S or Shotgun Metagenomics

Frontiers in Microbiology, Год журнала: 2016, Номер 7

Опубликована: Апрель 20, 2016

The advent of next generation sequencing (NGS) has enabled investigations the gut microbiome with unprecedented resolution and throughput. This stimulated development sophisticated bioinformatics tools to analyze massive amounts data generated. Researchers therefore need a clear understanding key concepts required for design, execution interpretation NGS experiments on microbiomes. We conducted literature review used our own determine which approaches work best. two main analyzing microbiome, 16S ribosomal RNA (rRNA) gene amplicons shotgun metagenomics, are illustrated analyses libraries designed highlight their strengths weaknesses. Several methods taxonomic classification bacterial sequences discussed. present simulations assess number that perform reliable appraisals community structure. To extent fluctuations in diversity populations correlate health disease, we emphasize various techniques analysis communities within samples (α-diversity) between (β-diversity). Finally, demonstrate infer metabolic capabilities bacteria from these data.

Язык: Английский

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Dirichlet Multinomial Mixtures: Generative Models for Microbial Metagenomics

PLoS ONE, Год журнала: 2012, Номер 7(2), С. e30126 - e30126

Опубликована: Фев. 3, 2012

We introduce Dirichlet multinomial mixtures (DMM) for the probabilistic modelling of microbial metagenomics data. This data can be represented as a frequency matrix giving number times each taxa is observed in sample. The samples have different size, and sparse, communities are diverse skewed to rare taxa. Most methods used previously classify or cluster ignored these features. describe community by vector probabilities. These vectors generated from one finite mixture components with hyperparameters. Observed through sampling. into distinct 'metacommunities', and, hence, determine envirotypes enterotypes, groups similar composition. model also deduce impact treatment classification. wrote software fitting DMM models using 'evidence framework' (http://code.google.com/p/microbedmm/). includes Laplace approximation evidence. applied human gut microbe genera frequencies Obese Lean twins. From evidence four clusters fit this best. Two were dominated Bacteroides homogenous; two had more variable could not find significant body mass on structure. However, twins likely derive high variance clusters. propose that obesity associated microbiota but increases chance an individual derives disturbed enterotype. example 'Anna Karenina principle (AKP)' communities: states having many configurations than undisturbed. verify showing study inflammatory bowel disease (IBD) phenotypes, ileal Crohn's (ICD) community.

Язык: Английский

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Airway microbiota and bronchial hyperresponsiveness in patients with suboptimally controlled asthma

Journal of Allergy and Clinical Immunology, Год журнала: 2010, Номер 127(2), С. 372 - 381.e3

Опубликована: Дек. 31, 2010

Язык: Английский

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ampvis2: an R package to analyse and visualise 16S rRNA amplicon data

bioRxiv (Cold Spring Harbor Laboratory), Год журнала: 2018, Номер unknown

Опубликована: Апрель 11, 2018

Abstract Summary Microbial community analysis using 16S rRNA gene amplicon sequencing is the backbone of many microbial ecology studies. Several approaches and pipelines exist for processing raw data generated through DNA convert into OTU-tables. Here we present ampvis2, an R package designed in OTU-table format with focus on simplicity, reproducibility, sample metadata integration, a minimal set intuitive commands. Unique features include flexible heatmaps simplified ordination. By generating plots ggplot2 package, ampvis2 produces publication-ready figures that can be easily customised. Furthermore, includes interactive visualisation, which convenient larger, more complex data. Availability implemented statistical language released under GNU A-GPL license. Documentation website source code maintained at: https://github.com/MadsAlbertsen/ampvis2 Contact Mads Albertsen ( [email protected] )

Язык: Английский

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Sequencing and beyond: integrating molecular 'omics' for microbial community profiling

Nature Reviews Microbiology, Год журнала: 2015, Номер 13(6), С. 360 - 372

Опубликована: Апрель 27, 2015

Язык: Английский

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