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QIIME 2 is a platform for microbiome bioinformatics that serves researchers around the world. Microbiomes are communities of microorganisms that are ubiquitous in our bodies and on our planet. Advances in DNA sequencing and bioinformatics technologies in the past few decades have provided a new lens into the microbial world, and developing our understanding of microbiomes promises to improve diverse aspects of human health, assist with environmental sustainability and feeding of 8 billiondoi:10.5281/zenodo.3840347 fatcat:nyc3v5u7yffytjduevgzmwgyeu
more »... , and maybe even save us from a post-antibiotic world. The QIIME project has existed for just over 10 years, and has been cited in nearly 20,000 microbiome research studies. QIIME 2 succeeded QIIME 1 on Jan 1 2018. QIIME 2 is a plugin-based platform that enables reproducible microbiome research through different interface types.
In this presentation I cover two aspects of the QIIME 2 microbiome bioinformatics project that were funded in round one of EOSS. First, I present our work to support plugin developers in QIIME 2 which is currently focused around the QIIME 2 Library. At the completion of our CZI project, the QIIME 2 Library will build and publish conda packages for plugin developers. It will also support automated testing of plugins and their documentation against release and development builds of QIIME 2. Next,doi:10.5281/zenodo.4321396 fatcat:22hxkls7kfgcfo2sed4prpblga
more »... I present a recap of our first ever fully online QIIME 2 workshop, which we hosted in October of 2020. This is something we've wanted to do for years, and the pandemic and our CZI funding enabled this. We had 75 participants in the workshop from 20 countries on six continents, and got very good feedback. The most unique aspect of this was that the five-day workshop was focused on hands-on tutorials, which participants ran on an AWS cluster that we built for this workshop.
High-throughput sequencing of small-subunit (SSU) rRNA genes has revolutionized understanding of microbial communities and facilitated investigations into ecological dynamics at unprecedented scales. Such extensive SSU rRNA gene sequence libraries, constructed from DNA extracts of environmental or host-associated samples, often contain a substantial proportion of unclassified sequences, many representing organisms with novel taxonomy (taxonomic "blind spots") and potentially unique ecology.doi:10.1128/msystems.00133-16 pmid:28028549 pmcid:PMC5183599 fatcat:zkqziwwa5zbuzj6sadick6hudi
more »... ed, these novel taxonomic lineages are associated with so-called microbial "dark matter," which is the genomic potential of these lineages. Unfortunately, characterization beyond "unclassified" is challenging due to relatively short read lengths and large data set sizes. Here we demonstrate how mining of phylogenetically novel sequences from microbial ecosystems can be automated using SSUnique, a software pipeline that filters unclassified and/or rare operational taxonomic units (OTUs) from 16S rRNA gene sequence libraries by screening against consensus structural models for SSU rRNA. Phylogenetic position is inferred against a reference data set, and additional characterization of novel clades is also included, such as targeted probe/primer design and mining of assembled metagenomes for genomic context. We show how SSUnique reproduced a previous analysis of phylogenetic novelty from an Arctic tundra soil and demonstrate the recovery of highly novel clades from data sets associated with both the Earth Microbiome Project (EMP) and Human Microbiome Project (HMP). We anticipate that SSUnique will add to the expanding computational toolbox supporting high-throughput sequencing approaches for the study of microbial ecology and phylogeny. IMPORTANCE Extensive SSU rRNA gene sequence libraries, constructed from DNA extracts of environmental or host-associated samples, often contain many unclassified sequences, many representing organisms with novel taxonomy (taxonomic "blind spots") and potentially unique ecology. This novelty is poorly explored in standard workflows, which narrows the breadth and discovery potential of such studies. Here we present the SSUnique analysis pipeline, which will promote the exploration of unclassified diversity in microbiome research and, importantly, enable the discovery of substantial novel taxonomic lineages through the analysis of a large variety of existing data sets.
Acknowledgements Caporaso Lab Evan Bolyen Matthew Dillon Keegan Evans Liz Gehret ...doi:10.5281/zenodo.5648973 fatcat:2rihgrribjeiffetx5xfm7dsei
Studies of the intestinal microbiome commonly utilize stool samples to measure microbial composition in the distal gut. However, collection of stool can be difficult from some subjects under certain experimental conditions. In this study we validate the use of swabs of fecal matter to approximate measurements of microbiota in stool using 16S rRNA gene Illumina amplicon sequencing, and evaluate the effects of shipping time at ambient temperatures on accuracy. Results indicate that swab samplesdoi:10.1101/524512 fatcat:upevlhmeave7timqchnhxxlbqm
more »... liably replicate stool microbiota bacterial composition, alpha diversity, and beta diversity when swabs are processed quickly (< 2 days), but sample quality quickly degrades after this period, accompanied by increased abundances of Enterobacteriaceae. Fresh swabs appear to be a viable alternative to stool sampling when standard collection methods are challenging, but extended exposure to ambient temperatures prior to processing threatens sample integrity.
Our understanding of the link between the human microbiome and disease, including obesity, inflammatory bowel disease, arthritis and autism, is rapidly expanding. Improvements in the throughput and accuracy of DNA sequencing of the genomes of microbial communities associated with human samples, complemented by analysis of transcriptomes, proteomes, metabolomes and immunomes, and mechanistic experiments in model systems, have vastly improved our ability to understand the structure and functiondoi:10.1038/nm.4517 pmid:29634682 fatcat:detu7ajuubhuvok5afr2kurcue
more »... the microbiome in both diseased and healthy states. However, many challenges remain. In this Review, we focus on studies in humans to describe these challenges, and propose strategies that leverage existing knowledge to move rapidly from correlation to causation, and ultimately to translation.
Taxonomic classification of marker-gene DNA sequences is a key step in the analysis of microbial ecology data. Accurate species-level characterisation of microbial communities by sequencing standard marker-gene sequence fragments has proved elusive. We show that knowing a sample's Earth Microbiome Project Ontology (EMPO) habitat type always increases taxonomic classification accuracy, usually to the point where classification accuracy at species level exceeds the genus-level classificationdoi:10.1101/406611 fatcat:fb5mqln7n5bgddbau55v2c46su
more »... acy achieved with EMPO-ignorant methods. This improvement comes from setting EMPO habitat type-specific taxonomic class weights for a naive Bayes taxonomic classifier to the average microbial composition for that habitat. We provide q2-clawback, a QIIME 2 plugin for compiling taxonomic class weights from a given collection of samples or by directly downloading data from the Qiita microbial study management platform. The weights output by q2-clawback are compatible with the q2-feature-classifier taxonomic classifier. We show that taxonomic weights averaged across the EMPO 3 habitat types (representing the observed global frequencies of microbial taxa in the Earth Microbiome Project database) typically increase sequence classification accuracy, providing an avenue for improving classification of samples of unknown or uncharacterized sample types for which custom class weights cannot be assembled. q2-clawback also provides a utility for estimating how effectively a new set of taxonomic class weights will improve taxonomic classification accuracy. Apart from the important accuracy improvements in taxonomic classification, our results represent a meta-analysis of marker-gene amplicon sequence data across 7,204 studies to reveal useful patterns of taxonomic abundance.
Biology & Philosophy
While in general, the intra-individual microbiome variation is less than inter-individual, the amount of variability over long time periods (Caporaso et al. 2011a) gives rise to the idea of microbial ... 2010; Chu et al. 2010; Fierer et al. 2012a) , and salinity plays a crucial role in structuring both free-living bacterial and archaeal communities across many environments (Lozupone and Knight 2007; Caporaso ...doi:10.1007/s10539-013-9364-4 pmid:23483075 pmcid:PMC3586164 fatcat:bscbmycydnhidiseuym6mw2g7e
Studies of the intestinal microbiome commonly utilize stool samples to measure the microbial composition in the distal gut. However, collection of stool can be difficult from some subjects under certain experimental conditions. Sampling of fecal material using sterile swabs can streamline sample collection, handling, and processing. In this study, we validate the use of swabs of fecal matter to approximate measurements of microbiota in stool using 16S rRNA gene Illumina amplicon sequencing anddoi:10.1128/msphere.00208-19 pmid:30971445 pmcid:PMC6458435 fatcat:g3obl3oi3faw3jkgya62k4w27u
more »... valuate the effects of shipping time at ambient temperatures on accuracy. The results indicate that swab samples reliably replicate the stool microbiota bacterial composition, alpha diversity, and beta diversity when swabs are processed quickly (≤2 days) but that sample quality quickly degrades after this period and is accompanied by increased abundances of Enterobacteriaceae. Fresh swabs appear to be a viable alternative to stool sampling when standard collection methods are challenging, but extended exposure to ambient temperatures prior to processing threatens sample integrity. IMPORTANCE Collection of fecal swab samples simplifies handling, processing, and archiving compared to collection of stool. This study confirms that fecal swabs reliably replicate the bacterial composition and diversity of stool samples, provided that the swabs are processed shortly after collection. These findings support the use of fecal swabs, when shipping and handling are done properly, to streamline measurements of intestinal microbiota.
Materials and methods Supervised learning models and plugin design The q2-sample-classifier plugin is accessible by multiple user interfaces supported in QIIME 2 (Caporaso et al., 2010) ; a Python 3 ...doi:10.1101/306167 fatcat:4gabzunohfd53bro2xk7t3b6ri
The most commonly used are QIIME (Caporaso et al., 2010b) , RDP (Cole et al., 2009) , mothur (Schloss et al., 2009) , and VAMPS (http://vamps.mbl.edu/). ... The most widely used software packages are QIIME (Caporaso et al., 2010b) (http://www.qiime.org) and mothur (Schloss et al., 2009 ) (http://www.mothur.org). ...doi:10.1016/j.cell.2014.06.037 pmid:25036628 pmcid:PMC5074386 fatcat:vp3jxqyms5f5fba66eadoeyheq
D j ) = P(H ) P(D 1 |H ) P(D 1 ) × P(D 2 |H ) P(D 2 ) × . . . × P(D j |H ) P(D j ) . Multiple confirmed predictions, therefore, multiply their effects on the plausibility of the initial idea, P(H ). ... AR AR261-BI74-07.tex XMLPublish SM (2004/02/24) P1: JRX AR REVIEWS IN ADVANCE10.1146/annurev.biochem.74.082803.133119 188 YARUS CAPORASO KNIGHT Annu. Rev. ...doi:10.1146/annurev.biochem.74.082803.133119 pmid:15952885 fatcat:b2doa6unxja7re3wpk5gik6szy
For j ϭ 1, . . . , 27, we define d j,S ϭ ͭ 0.6 j the simulated data set X S as X S ϭ BD S V T , where D S ϭ diag͑d 1,S , . . . , d 27,S ͒. ... Furthermore, since V has orthogonal columns, it can be seen that mϭ1 q s m 2 ϭ jϭ1 p ͑ mϭ1 q v jm 2 s m 2 ͒. ... Then, the arrow for the jth variable can be configured by the coordinates of e j , given by ͑e j T v 1 ,e j T v 2 ͒. This coordinate system also allows the configuration of future samples. ...doi:10.1128/msystems.00504-19 pmid:31848304 pmcid:PMC6918030 fatcat:xzmpkrl7tjf6have5eoyzapmgm
Citation: Michelou VK, Caporaso JG, Knight R, Palumbi SR (2013) The Ecology of Microbial Communities Associated with Macrocystis pyrifera. PLoS ONE 8(6): e67480. ...doi:10.1371/journal.pone.0067480 pmid:23840715 pmcid:PMC3686729 fatcat:ec4owieaxrhxtp63ojd3hsqjfu
That is, CI ¼ log P n i¼1 P n j¼1 d t i; j ð ÞI d s i; j ð Þ< 0:25 ð Þ w i ð Þw j ð Þwhere CI is the confusion index, d s (i,j) is the sequence dissimilarity between the i th and j th sequences, d t (i ... ,j) is the taxonomic dissimilarity between the i th and j th sequences, w(i) is the weight of the i th sequence, and I(•) is the indicator function. ...doi:10.1038/s41467-019-12669-6 pmid:31604942 pmcid:PMC6789115 fatcat:dkdq6wdoc5c73k2q2xbwzc22va
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