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      A protocol for characterization of extremely preterm infant gut microbiota in double-blind clinical trials

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          Summary

          16S rRNA gene sequencing enables microbial community profiling, but recovering fecal DNA from extremely premature infants is challenging. Here, we describe an optimized protocol for fecal DNA isolation, library preparation for 16S rRNA gene sequencing, taxonomy assignation, and statistical analyses. The protocol is complemented with a quantitative PCR for probiotic L. reuteri identification. This protocol describes how to characterize preterm infant gut microbiota and relate it to probiotic supplementation and clinical outcomes. It is customizable for other clinical trials.

          For complete details on the use and execution of this protocol, please refer to Martí et al. (2021) and Spreckels et al. (2021).

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          Highlights

          • Customizable protocol for characterization of infant gut microbiota in clinical trials

          • Optimized sample preparation for 16S rRNA gene sequencing for infant fecal samples

          • Quantitative PCR for probiotic Lactobacillus reuteri quantification in infant feces

          • Easy-to-use bioinformatic and statistical analysis pipeline

          Abstract

          16S rRNA gene sequencing enables microbial community profiling, but recovering fecal DNA from extremely premature infants is challenging. Here, we describe an optimized protocol for fecal DNA isolation, library preparation for 16S rRNA gene sequencing, taxonomy assignation, and statistical analyses. The protocol is complemented with a quantitative PCR for probiotic L. reuteri identification. This protocol describes how to characterize preterm infant gut microbiota and relate it to probiotic supplementation and clinical outcomes. It is customizable for other clinical trials.

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          Most cited references21

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          Moderated estimation of fold change and dispersion for RNA-seq data with DESeq2

          In comparative high-throughput sequencing assays, a fundamental task is the analysis of count data, such as read counts per gene in RNA-seq, for evidence of systematic changes across experimental conditions. Small replicate numbers, discreteness, large dynamic range and the presence of outliers require a suitable statistical approach. We present DESeq2, a method for differential analysis of count data, using shrinkage estimation for dispersions and fold changes to improve stability and interpretability of estimates. This enables a more quantitative analysis focused on the strength rather than the mere presence of differential expression. The DESeq2 package is available at http://www.bioconductor.org/packages/release/bioc/html/DESeq2.html. Electronic supplementary material The online version of this article (doi:10.1186/s13059-014-0550-8) contains supplementary material, which is available to authorized users.
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            DADA2: High resolution sample inference from Illumina amplicon data

            We present DADA2, a software package that models and corrects Illumina-sequenced amplicon errors. DADA2 infers sample sequences exactly, without coarse-graining into OTUs, and resolves differences of as little as one nucleotide. In several mock communities DADA2 identified more real variants and output fewer spurious sequences than other methods. We applied DADA2 to vaginal samples from a cohort of pregnant women, revealing a diversity of previously undetected Lactobacillus crispatus variants.
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              phyloseq: An R Package for Reproducible Interactive Analysis and Graphics of Microbiome Census Data

              Background The analysis of microbial communities through DNA sequencing brings many challenges: the integration of different types of data with methods from ecology, genetics, phylogenetics, multivariate statistics, visualization and testing. With the increased breadth of experimental designs now being pursued, project-specific statistical analyses are often needed, and these analyses are often difficult (or impossible) for peer researchers to independently reproduce. The vast majority of the requisite tools for performing these analyses reproducibly are already implemented in R and its extensions (packages), but with limited support for high throughput microbiome census data. Results Here we describe a software project, phyloseq, dedicated to the object-oriented representation and analysis of microbiome census data in R. It supports importing data from a variety of common formats, as well as many analysis techniques. These include calibration, filtering, subsetting, agglomeration, multi-table comparisons, diversity analysis, parallelized Fast UniFrac, ordination methods, and production of publication-quality graphics; all in a manner that is easy to document, share, and modify. We show how to apply functions from other R packages to phyloseq-represented data, illustrating the availability of a large number of open source analysis techniques. We discuss the use of phyloseq with tools for reproducible research, a practice common in other fields but still rare in the analysis of highly parallel microbiome census data. We have made available all of the materials necessary to completely reproduce the analysis and figures included in this article, an example of best practices for reproducible research. Conclusions The phyloseq project for R is a new open-source software package, freely available on the web from both GitHub and Bioconductor.
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                Author and article information

                Contributors
                Journal
                STAR Protoc
                STAR Protoc
                STAR Protocols
                Elsevier
                2666-1667
                09 July 2021
                17 September 2021
                09 July 2021
                : 2
                : 3
                : 100652
                Affiliations
                [1 ]Department of Biomedical and Clinical Sciences, Linköping University, Linköping, Sweden
                [2 ]Department of Genetics, University Medical Centre Groningen, Groningen, the Netherlands
                [3 ]Department of Paediatrics, Linköping University, Linköping, Sweden
                Author notes
                []Corresponding author magali.marti.genero@ 123456liu.se
                [4]

                Technical contact

                [5]

                Lead contact

                Article
                S2666-1667(21)00359-2 100652
                10.1016/j.xpro.2021.100652
                8283139
                34308378
                a29627c4-8c7f-45df-8ece-f9e45cc9d5fe
                © 2021 The Author(s)

                This is an open access article under the CC BY license (http://creativecommons.org/licenses/by/4.0/).

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                Categories
                Protocol

                health sciences,clinical protocol,sequencing,microbiology,molecular biology

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