Automation of QIIME2 Metagenomic Analysis Platform

Fung, Calvin; Rusling, Mathew; Lampeter, Thomas; Love, C. E.; Karim, Anisha; Bongiorno, Christian; Yuan, Long

doi:10.1002/cpz1.254

Cited by 20 publications

(13 citation statements)

References 17 publications

(14 reference statements)

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“…Multiple commands need to be executed to run the analysis using QAP; moreover, it provides more options and different approaches than Snaq follows (Fung et al 2021) hence changing the parameters requires the modification of manifest and configuration files.…”

Section: Resultsmentioning

confidence: 99%

“…In addition, their paper gave detailed explanations of many steps and descriptions of their results. Multiple commands need to be executed to run the analysis using QAP; moreover, it provides more options and different approaches than Snaq follows (Fung et al 2021). Estaki et al provided a comprehensive description of the QIIME2, showing several steps of QIIME2 analysis starting from data import to final steps, including Jupiter notebooks (Estaki et al 2020).…”

Section: Resultsmentioning

confidence: 99%

“…Although some pipelines for the automated analysis of 16S data are available (Estaki et al 2020; Fung et al 2021; Kondratenko et al 2020; Hu and Alexander 2020), none of them provide an easy way to run the analysis multiple times for optimization purposes. Also, they tend to have fixed steps and do not make it easier to change the sequence of steps and parameters used.…”

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

Snaq: A Dynamic Snakemake Pipeline for Microbiome data analysis with QIIME2

Mohsen

Chen

Osorio

et al. 2022

Preprint

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Optimizing a protocol for 16S microbiome data analysis with QIIME2 is a challenging task for biologists with no programming experience. It involves a multi-step process, and multiple parameters and options that need to be tested and determined. In this article, we describe Snaq, a snakemake pipeline that helps automate and optimize 16S data analysis using QIIME2. Snaq offers an informative file naming system and automatically performs the analysis of a data set by downloading and installing the required databases and classifiers, all through a single command-line instruction. It works natively on Linux and Mac and on Windows through the use of containers, and is potentially extendable by adding new rules. This pipeline will substantially reduce the efforts in sending commands and prevent the confusion caused by the accumulation of analysis results due to testing multiple parameters.

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Section: Resultsmentioning

confidence: 99%

Section: Resultsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

Snaq: A Dynamic Snakemake Pipeline for Microbiome data analysis with QIIME2

Mohsen

Chen

Osorio

et al. 2022

Preprint

View full text Add to dashboard Cite

show abstract

“…Taxonomic annotations of feature sequences were processed by a Bayesian classifier using SILVA as a reference database ( Sierra et al., 2020 ). Alpha and beta diversity metrics were evaluated by QIIME2 ( Fung et al., 2021 ). In identifying T2DM versus healthy controls, the Wilcoxon rank sum test was used to determine statistical differences between groups, considering that there were only two groups which did not follow a normal distribution.…”

Section: Methodsmentioning

confidence: 99%

Cross-Cohort Microbiome Analysis of Salivary Biomarkers in Patients With Type 2 Diabetes Mellitus

Gao

Guan

Chen

2022

Front. Cell. Infect. Microbiol.

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Several studies have ascertained differences in salivary microbiota between patients with type 2 diabetes mellitus (T2DM) and healthy populations. However, the predictive accuracy and reproducibility of these 16S rRNA sequencing analyses when applied to other cohorts remain enigmatic. A comprehensive analysis was conducted on the included 470 samples from five researches in publicly available databases. The discrepancy and predictive accuracy of salivary microbiota between T2DM patients and healthy populations were evaluated from multiple perspectives, followed by the identification of salivary biomarkers for DM. Next, a classification model (areas under the curves = 0.92) was developed based on a large sample. The model could be used for clinical diagnosis and prognostic monitoring and as a basis for hypothesis-driven mechanistic researches. Furthermore, the research heterogeneity across geographic regions suggested that microbiological markers might not become a uniform clinical standard in human beings. They rather identify abnormal alterations under the microbiological characteristics of a specific population.

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“…To derive the dissimilarity of microbial communities (beta diversity) from different sample treatments the non-metric multi-dimensional scaling (NMDS) and principal coordinate analysis (PC o A) metric multi-dimensional scaling analyses were conducted. The Bray-Curtis Emperor plot was also generated with QIIME2 plugins including the DADA2 denoising pipeline 36,37 . The statistical significance of the microbial diversity was compared using analysis of molecular variance (AMOVA) and homogeneity of molecular variance (HOMOVA) statistical analyses.…”

Section: Methodsmentioning

confidence: 99%

Alteration of Soil Bacteriome by Prolonged Exposure to Metal Oxide Nanoparticles

Egboluche

Wachira

2022

Preprint

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Metal oxide nanoparticles (MONPs) have found applications in many industrial and consumer products and are inevitably released into the environment, including soil. Soils host diverse microorganisms that are integral to ecosystem function including regulating plant growth. In this study, the influence of Cu2O, Fe3O4 and Ag2O NPs on soil microbial communities was assessed. Microbial community diversity and compositional structure was characterized using quantitative PCR and 16S rRNA gene sequencing. MONPs altered soil bacteria community composition by causing significant reduction in bacterial diversity and change in bacterial abundance. Soils with Cu2O and Ag2O NPs treatments significantly reduce bacterial diversity accompanied by shifts at the Class and Phylum taxonomic levels toward bacteria groups responsible for chitin degradation (Bacteriodetes) and nitrogen fixation (alpha-Proteobacteria). Response of bacterial communities to MONPs exposure is dependent on the exposure time and type of MONPs used. While the mechanisms underlying these observations remain to be elucidated, it is proposed that the known antimicrobial properties of Cu2O and Ag2O NPs cause reduced growth and viability of some bacteria taxa.

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Automation of QIIME2 Metagenomic Analysis Platform

Cited by 20 publications

References 17 publications

Snaq: A Dynamic Snakemake Pipeline for Microbiome data analysis with QIIME2

Snaq: A Dynamic Snakemake Pipeline for Microbiome data analysis with QIIME2

Cross-Cohort Microbiome Analysis of Salivary Biomarkers in Patients With Type 2 Diabetes Mellitus

Alteration of Soil Bacteriome by Prolonged Exposure to Metal Oxide Nanoparticles

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