Accessible, curated metagenomic data through ExperimentHub

Pasolli, Edoardo; Schiffer, Lucas; Manghi, Paolo; Renson, Audrey; Obenchain, Valerie; Truong, Duy Tin; Beghini, Francesco; Malik, Faizan; Ramos, Marcel; Dowd, Jennifer Beam; Huttenhower, Curtis; Morgan, Martin; Segata, Nicola; Waldron, Levi

doi:10.1038/nmeth.4468

Cited by 372 publications

(353 citation statements)

References 6 publications

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“…To measure the ability of each method to produce consistent or replicable results in independent data, we looked at six datasets 22,23,[26][27][28] (Supplementary Table S3), with different alpha and beta diversity, as well as different amounts of DA between two experimental conditions (Supplementary Figure S5). Each dataset was randomly split in two equally sized subsets and each method was separately applied to each subset.…”

Section: Consistencymentioning

confidence: 99%

“…While some tools exist to guide researchers 15 , a general consensus on the best approach is still missing, especially regarding the methods' capability of controlling false discoveries. In this study, we benchmark several statistical models and methods developed for metagenomics 13,14 , bulk RNA-seq [16][17][18] and, for the first time, single-cell RNA-seq 7,8,[19][20][21] on a collection of manually curated 16S 22 and WMS 23 real data as well as on a comprehensive set of simulations. We also consider use of the Dirichlet Multinomial Distribution for explicit compositional analysis (i.e., reflecting the fact that counts represent a proportion of the total rather than absolute counts), and the use of geometric mean normalization for reducing the impact of compositionality 24 .…”

Section: Introductionmentioning

confidence: 99%

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Assessment of statistical methods from single cell, bulk RNA-seq and metagenomics applied to microbiome data

Calgaro

Romualdi

Risso

et al. 2020

Preprint

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The correct identification of differentially abundant microbial taxa between experimental conditions is a methodological and computational challenge. Recent work has shown that commonly used methods do not control the false discovery rate due to the peculiarity of these data (e.g. high sparsity), leading to an abundance of false positive results.Since single-cell RNA-seq shares some of these peculiarities, we apply methods developed for single cell differential expression to microbiome data. We compare these approaches to methods developed for bulk RNA-seq and microbiome data, in terms of suitability of distributional assumptions, ability to control false discoveries, consistency, replicability, and power. We benchmark these methods using 100 manually curated datasets from 16S and whole metagenome shotgun sequencing. A simulation framework is developed to assess the impact of experimental design in power analysis.Our analyses suggest that DESeq2 and limma-voom show the best performance. We recommend a careful exploratory data analysis prior to application of any inferential model and we present a framework to help scientists make an informed choice of analysis methods in a dataset-specific manner.

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Section: Consistencymentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Assessment of statistical methods from single cell, bulk RNA-seq and metagenomics applied to microbiome data

Calgaro

Romualdi

Risso

et al. 2020

Preprint

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“…In our meta-analysis, we considered and curated 6 publicly available gut metagenomic datasets ( Fig. 1a and Additional file 1: Table S1) spanning 22 non-human primate (NHP) species from 14 different countries in 5 continents (Additional file 2: Figure S1) and metagenomic samples from healthy individuals from 47 datasets included in the curatedMetagenomicData package [79]. In total, our study considers 203 metagenomic samples from the gut of NHPs and 9428 human metagenomes from different body sites.…”

Section: Analyzed Datasetsmentioning

confidence: 99%

“…The non-human primate microbiome datasets analyzed in the current study are available in the Sequence Read Archive (SRA) under BioProjects PRJNA382701 [30], PRJNA271618 [37], and PRJNA485217 [40]; in the European Nucleotide Archive (ENA) under accessions ERP104379 [41] and PRJEB22765 [39]; and in MG-RAST project "Douc Illumina" http://metagenomics.anl.gov/linkin.cgi?project=2562, under IDs 4506440.3 and 4506441.3 (PN1) and 4514928.3 and 4514929.3 (PN2) [38]. All human microbiome datasets are available in the curatedMetagenomicData package [79]. MAGs reconstructed in this study are available in ENA under accession PRJEB35610 [96] and at http://segatalab.cibio.unitn.it/data/Manara_et_al.html.…”

Section: Supplementary Informationmentioning

confidence: 99%

Microbial genomes from non-human primate gut metagenomes expand the primate-associated bacterial tree of life with over 1000 novel species

et al. 2019

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BackgroundHumans have coevolved with microbial communities to establish a mutually advantageous relationship that is still poorly characterized and can provide a better understanding of the human microbiome. Comparative metagenomic analysis of human and non-human primate (NHP) microbiomes offers a promising approach to study this symbiosis. Very few microbial species have been characterized in NHP microbiomes due to their poor representation in the available cataloged microbial diversity, thus limiting the potential of such comparative approaches.ResultsWe reconstruct over 1000 previously uncharacterized microbial species from 6 available NHP metagenomic cohorts, resulting in an increase of the mappable fraction of metagenomic reads by 600%. These novel species highlight that almost 90% of the microbial diversity associated with NHPs has been overlooked. Comparative analysis of this new catalog of taxa with the collection of over 150,000 genomes from human metagenomes points at a limited species-level overlap, with only 20% of microbial candidate species in NHPs also found in the human microbiome. This overlap occurs mainly between NHPs and non-Westernized human populations and NHPs living in captivity, suggesting that host lifestyle plays a role comparable to host speciation in shaping the primate intestinal microbiome. Several NHP-specific species are phylogenetically related to human-associated microbes, such as Elusimicrobia and Treponema, and could be the consequence of host-dependent evolutionary trajectories.ConclusionsThe newly reconstructed species greatly expand the microbial diversity associated with NHPs, thus enabling better interrogation of the primate microbiome and empowering in-depth human and non-human comparative and co-diversification studies.

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“…The ELDERMET [8] established catalogues of microbes and their genes in various body locations. Curated databases exist in which large numbers of standardized microbiome datasets from cases and controls are archived and which may be downloaded, facilitating comparative analysis [9].…”

Section: Introduction To the Mammalian Microbiomementioning

confidence: 99%

The role of the microbiota in sedentary lifestyle disorders and ageing: lessons from the animal kingdom

O’Toole

Shiels

2020

J Intern Med

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A paradox of so‐called developed countries is that, as the major historical causes of human mortality are eliminated or mitigated by medical progress, lifestyle‐related diseases have become major killers. Furthermore, as lifespan is extended by the combined effects of modern medicine, health span is struggling to keep apace because of the burden of noncommunicable diseases linked to diet and sedentary lifestyle. The gut microbiome is now recognized as a plastic environmental risk factor for many of these diseases, the microbiome being defined as the complex community of co‐evolved commensal microbes that breaks down components of a complex diet, modulates innate immunity, and produces signalling molecules and metabolites that can impact on diverse regulatory systems in mammals. Aspects of the so‐called ‘Western’ lifestyle linked to disease risk such as energy dense diet and antibiotic treatment are known to affect the composition and function of the microbiome. Here, we review the detailed mechanisms whereby the gut microbiome may modulate risk of diseases linked to sedentary lifestyle and ageing‐related health loss. We focus on the comparative value of natural animal models such as hibernation for studying metabolic regulation and the challenge of extrapolating from animal models to processes that occur in human ageing.

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Accessible, curated metagenomic data through ExperimentHub

Cited by 372 publications

References 6 publications

Assessment of statistical methods from single cell, bulk RNA-seq and metagenomics applied to microbiome data

Assessment of statistical methods from single cell, bulk RNA-seq and metagenomics applied to microbiome data

Microbial genomes from non-human primate gut metagenomes expand the primate-associated bacterial tree of life with over 1000 novel species

The role of the microbiota in sedentary lifestyle disorders and ageing: lessons from the animal kingdom

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