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      Evaluation of 16S rRNA gene sequencing for species and strain-level microbiome analysis

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          Abstract

          The 16S rRNA gene has been a mainstay of sequence-based bacterial analysis for decades. However, high-throughput sequencing of the full gene has only recently become a realistic prospect. Here, we use in silico and sequence-based experiments to critically re-evaluate the potential of the 16S gene to provide taxonomic resolution at species and strain level. We demonstrate that targeting of 16S variable regions with short-read sequencing platforms cannot achieve the taxonomic resolution afforded by sequencing the entire (~1500 bp) gene. We further demonstrate that full-length sequencing platforms are sufficiently accurate to resolve subtle nucleotide substitutions (but not insertions/deletions) that exist between intragenomic copies of the 16S gene. In consequence, we argue that modern analysis approaches must necessarily account for intragenomic variation between 16S gene copies. In particular, we demonstrate that appropriate treatment of full-length 16S intragenomic copy variants has the potential to provide taxonomic resolution of bacterial communities at species and strain level.

          Abstract

          Here, the authors explore the potential of the 16S gene for discriminating bacterial taxa and show that full-length sequencing combined with appropriate clustering of intragenomic sequence variation can provide accurate representation of bacterial species in microbiome datasets.

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          The Sequence Alignment/Map format and SAMtools

          Summary: The Sequence Alignment/Map (SAM) format is a generic alignment format for storing read alignments against reference sequences, supporting short and long reads (up to 128 Mbp) produced by different sequencing platforms. It is flexible in style, compact in size, efficient in random access and is the format in which alignments from the 1000 Genomes Project are released. SAMtools implements various utilities for post-processing alignments in the SAM format, such as indexing, variant caller and alignment viewer, and thus provides universal tools for processing read alignments. Availability: http://samtools.sourceforge.net Contact: rd@sanger.ac.uk
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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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              Cutadapt removes adapter sequences from high-throughput sequencing reads

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                Author and article information

                Contributors
                Jethro.Johnson@jax.org
                Journal
                Nat Commun
                Nat Commun
                Nature Communications
                Nature Publishing Group UK (London )
                2041-1723
                6 November 2019
                6 November 2019
                2019
                : 10
                : 5029
                Affiliations
                [1 ]ISNI 0000 0004 0374 0039, GRID grid.249880.f, The Jackson Laboratory for Genomic Medicine, ; Farmington, CT 06032 USA
                [2 ]ISNI 0000 0001 2285 7943, GRID grid.261331.4, Ohio State University Comprehensive Cancer Center, ; Columbus, OH 43210 USA
                [3 ]ISNI 0000 0004 0440 749X, GRID grid.413480.a, Department of Pathology and Laboratory Medicine, , Dartmouth-Hitchcock Medical Center, ; Lebanon, NH 03756 USA
                [4 ]Shanghai Institute of Immunology, Shanghai Jaiotong University School of Medicine, Shanghai, China
                [5 ]ISNI 0000 0000 9206 2401, GRID grid.267308.8, Center for Antimicrobial Resistance and Microbial Genomics, , McGovern Medical School, ; Houston, TX 77030 USA
                [6 ]ISNI 0000000419368710, GRID grid.47100.32, Department of Computer Science, , Yale University, ; New Haven, CT USA
                Author information
                http://orcid.org/0000-0001-9155-054X
                http://orcid.org/0000-0003-2314-6435
                http://orcid.org/0000-0002-9566-1033
                Article
                13036
                10.1038/s41467-019-13036-1
                6834636
                31695033
                3f998caa-e3f5-4907-b027-ca41c3c78471
                © The Author(s) 2019

                Open Access This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made. The images or other third party material in this article are included in the article’s Creative Commons license, unless indicated otherwise in a credit line to the material. If material is not included in the article’s Creative Commons license and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this license, visit http://creativecommons.org/licenses/by/4.0/.

                History
                : 9 January 2019
                : 16 October 2019
                Categories
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                Custom metadata
                © The Author(s) 2019

                Uncategorized
                bacterial genes,bacterial techniques and applications,microbiome
                Uncategorized
                bacterial genes, bacterial techniques and applications, microbiome

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