Reproducible, interactive, scalable and extensible microbiome data science using QIIME 2

Nature Biotechnology, Journal Year: 2019, Volume and Issue: 37(8), P. 852 - 857

Published: July 24, 2019

Language: Английский

---

ggtree: an r package for visualization and annotation of phylogenetic trees with their covariates and other associated data

Methods in Ecology and Evolution, Journal Year: 2016, Volume and Issue: 8(1), P. 28 - 36

Published: Aug. 16, 2016

Summary We present an r package, ggtree , which provides programmable visualization and annotation of phylogenetic trees. can read more tree file formats than other softwares, including newick nexus NHX phylip jplace formats, support phylo, multiphylo, phylo4, phylo4d, obkdata phyloseq objects defined in packages. It also extract the tree/branch/node‐specific data from analysis outputs beast epa hyphy paml phylodog pplacer r8s raxml revbayes software, allows using these to annotate tree. The package colouring a by numerical/categorical node attributes, manipulating rotating, collapsing zooming out clades, highlighting user selected clades or operational taxonomic units exploration large into portion. A two‐dimensional be drawn scaling width based on attribute nodes. annotated with associated numerical matrix (as heat map), multiple sequence alignment, subplots silhouette images. is released under artistic‐2.0 license . source code documents are freely available through bioconductor ( http://www.bioconductor.org/packages/ggtree ).

Language: Английский

---

Waste Not, Want Not: Why Rarefying Microbiome Data Is Inadmissible

PLoS Computational Biology, Journal Year: 2014, Volume and Issue: 10(4), P. e1003531 - e1003531

Published: April 3, 2014

Current practice in the normalization of microbiome count data is inefficient statistical sense. For apparently historical reasons, common approach either to use simple proportions (which does not address heteroscedasticity) or rarefying counts, even though both these approaches are inappropriate for detection differentially abundant species. Well-established theory available that simultaneously accounts library size differences and biological variability using an appropriate mixture model. Moreover, specific implementations DNA sequencing read (based on a Negative Binomial model instance) already RNA-Seq focused R packages such as edgeR DESeq. Here we summarize supporting simulations empirical demonstrate substantial improvements provided by relevant framework over rarefying. We show how rarefied counts result high rate false positives tests species across sample classes. Regarding sample-wise clustering, also procedure often discards samples can be accurately clustered alternative methods. further compare different methods with recently-described zero-inflated Gaussian mixture, implemented package called metagenomeSeq. find metagenomeSeq performs well when there adequate number replicates, but it nevertheless tends toward higher positive rate. Based results well-established theory, advocate investigators avoid altogether. have microbiome-specific extensions tools package, phyloseq.

Language: Английский

---

Simple statistical identification and removal of contaminant sequences in marker-gene and metagenomics data

Microbiome, Journal Year: 2018, Volume and Issue: 6(1)

Published: Dec. 1, 2018

The accuracy of microbial community surveys based on marker-gene and metagenomic sequencing (MGS) suffers from the presence contaminants—DNA sequences not truly present in sample. Contaminants come various sources, including reagents. Appropriate laboratory practices can reduce contamination, but do eliminate it. Here we introduce decontam ( https://github.com/benjjneb/decontam ), an open-source R package that implements a statistical classification procedure identifies contaminants MGS data two widely reproduced patterns: appear at higher frequencies low-concentration samples are often found negative controls. Decontam classified amplicon sequence variants (ASVs) human oral dataset consistently with prior microscopic observations taxa inhabiting environment previous reports contaminant taxa. In metagenomics measurements dilution series, substantially reduced technical variation arising different protocols. application to recently published datasets corroborated extended their conclusions little evidence existed for indigenous placenta microbiome some low-frequency seemingly associated preterm birth were contaminants. improves quality by identifying removing DNA sequences. integrates easily existing workflows allows researchers generate more accurate profiles communities no additional cost.

Language: Английский

---

Microbiome Datasets Are Compositional: And This Is Not Optional

Frontiers in Microbiology, Journal Year: 2017, Volume and Issue: 8

Published: Nov. 15, 2017

Datasets collected by high-throughput sequencing (HTS) of 16S rRNA gene amplimers, metagenomes or metatranscriptomes are commonplace and being used to study human disease states, ecological differences between sites, the built environment. There is increasing awareness that microbiome datasets generated HTS compositional because they have an arbitrary total imposed instrument. However, many investigators either unaware this assume specific properties data. The purpose review alert dangers inherent in ignoring nature data, point out derived from studies can should be treated as compositions at all stages analysis. We briefly introduce illustrate pathologies occur when data analyzed inappropriately, finally give guidance resources examples for analysis using

Language: Английский

---

Human gut microbes impact host serum metabolome and insulin sensitivity

Nature, Journal Year: 2016, Volume and Issue: 535(7612), P. 376 - 381

Published: July 1, 2016

Language: Английский

---

Normalization and microbial differential abundance strategies depend upon data characteristics

Microbiome, Journal Year: 2017, Volume and Issue: 5(1)

Published: March 3, 2017

Data from 16S ribosomal RNA (rRNA) amplicon sequencing present challenges to ecological and statistical interpretation. In particular, library sizes often vary over several ranges of magnitude, the data contains many zeros. Although we are typically interested in comparing relative abundance taxa ecosystem two or more groups, can only measure taxon specimens obtained ecosystems. Because comparison specimen is not equivalent ecosystems, this presents a special challenge. Second, because (as well as ecosystem) sum 1, these compositional data. constrained by simplex (sum 1) unconstrained Euclidean space, standard methods analysis applicable. Here, evaluate how impact performance existing normalization differential analyses. Effects on normalization: Most enable successful clustering samples according biological origin when groups differ substantially their overall microbial composition. Rarefying clearly clusters than other techniques do for ordination metrics based presence absence. Alternate measures potentially vulnerable artifacts due size. testing: We build previous work seven proposed using rarefied raw Our simulation studies suggest that false discovery rates abundance-testing increased rarefying itself, although course results loss sensitivity elimination portion available For with large (~10×) differences average size, lowers rate. DESeq2, without addition constant, smaller datasets (<20 per group) but tends towards higher rate samples, very uneven sizes, and/or effects. drawing inferences regarding ecosystem, composition microbiomes (ANCOM) sensitive (for >20 also critically method tested has good control These findings guide which use characteristics given study.

Language: Английский

---

MicrobiomeAnalyst: a web-based tool for comprehensive statistical, visual and meta-analysis of microbiome data

Nucleic Acids Research, Journal Year: 2017, Volume and Issue: 45(W1), P. W180 - W188

Published: April 12, 2017

The widespread application of next-generation sequencing technologies has revolutionized microbiome research by enabling high-throughput profiling the genetic contents microbial communities. How to analyze resulting large complex datasets remains a key challenge in current studies. Over past decade, powerful computational pipelines and robust protocols have been established enable efficient raw data processing annotation. focus shifted toward downstream statistical analysis functional interpretation. Here, we introduce MicrobiomeAnalyst, user-friendly tool that integrates recent progress statistics visualization techniques, coupled with novel knowledge bases, comprehensive common outputs produced from MicrobiomeAnalyst contains four modules - Marker Data Profiling module offers various options for community profiling, comparative prediction based on 16S rRNA marker gene data; Shotgun supports exploratory analysis, metabolic network shotgun metagenomics or metatranscriptomics Taxon Set Enrichment Analysis helps interpret taxonomic signatures via enrichment against >300 taxon sets manually curated literature public databases; finally, Projection Public allows users visually explore their reference pattern discovery biological insights. is freely available at http://www.microbiomeanalyst.ca.

Language: Английский

---

The neuroactive potential of the human gut microbiota in quality of life and depression

Nature Microbiology, Journal Year: 2019, Volume and Issue: 4(4), P. 623 - 632

Published: Feb. 4, 2019

Language: Английский

---

Early infancy microbial and metabolic alterations affect risk of childhood asthma

Science Translational Medicine, Journal Year: 2015, Volume and Issue: 7(307)

Published: Sept. 30, 2015

Asthma is the most prevalent pediatric chronic disease and affects more than 300 million people worldwide. Recent evidence in mice has identified a "critical window" early life where gut microbial changes (dysbiosis) are influential experimental asthma. However, current research yet to establish whether these precede or involved human We compared microbiota of 319 subjects enrolled Canadian Healthy Infant Longitudinal Development (CHILD) Study, show that infants at risk asthma exhibited transient dysbiosis during first 100 days life. The relative abundance bacterial genera Lachnospira, Veillonella, Faecalibacterium, Rothia was significantly decreased children This reduction taxa accompanied by reduced levels fecal acetate dysregulation enterohepatic metabolites. Inoculation germ-free with four ameliorated airway inflammation their adult progeny, demonstrating causal role averting development. These results enhance potential for future microbe-based diagnostics therapies, potentially form probiotics, prevent development other related allergic diseases children.

Language: Английский

---