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      COVID‐19 associated oral and oropharyngeal microbiome: Systematic review and meta‐analysis

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          Abstract

          Three years into the coronavirus disease 2019 (COVID‐19) pandemic, there are still growing concerns with

          • the emergence of different variants,

          • unknown long‐ and short‐term effects of the virus, and

          • potential biological mechanisms underlying etiopathogenesis and increased risk for morbidity and mortality.

          The role of the microbiome in human physiology and the initiation and progression of several oral and systemic diseases have been actively studied in the past decade. With the proof of viral transmission, carriage, and a potential role in etiopathogenesis, saliva and the oral environment have been a focus of COVID‐19 research beyond diagnostic purposes. The oral environment hosts diverse microbial communities and contributes to human oral and systemic health. Several investigations have identified disruptions in the oral microbiome in COVID‐19 patients. However, all these studies are cross‐sectional in nature and present heterogeneity in study design, techniques, and analysis. Therefore, in this undertaking, we (a) systematically reviewed the current literature associating COVID‐19 with changes in the microbiome; (b) performed a re‐analysis of publicly available data as a means to standardize the analysis, and (c) reported alterations in the microbial characteristics in COVID‐19 patients compared to negative controls. Overall, we identified that COVID‐19 is associated with oral microbial dysbiosis with significant reduction in diversity. However, alterations in specific bacterial members differed across the study. Re‐analysis from our pipeline shed light on Neisseria as the potential key microbial member associated with COVID‐19.

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          Most cited references55

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          The SILVA ribosomal RNA gene database project: improved data processing and web-based tools

          SILVA (from Latin silva, forest, http://www.arb-silva.de) is a comprehensive web resource for up to date, quality-controlled databases of aligned ribosomal RNA (rRNA) gene sequences from the Bacteria, Archaea and Eukaryota domains and supplementary online services. The referred database release 111 (July 2012) contains 3 194 778 small subunit and 288 717 large subunit rRNA gene sequences. Since the initial description of the project, substantial new features have been introduced, including advanced quality control procedures, an improved rRNA gene aligner, online tools for probe and primer evaluation and optimized browsing, searching and downloading on the website. Furthermore, the extensively curated SILVA taxonomy and the new non-redundant SILVA datasets provide an ideal reference for high-throughput classification of data from next-generation sequencing approaches.
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            fastp: an ultra-fast all-in-one FASTQ preprocessor

            Abstract Motivation Quality control and preprocessing of FASTQ files are essential to providing clean data for downstream analysis. Traditionally, a different tool is used for each operation, such as quality control, adapter trimming and quality filtering. These tools are often insufficiently fast as most are developed using high-level programming languages (e.g. Python and Java) and provide limited multi-threading support. Reading and loading data multiple times also renders preprocessing slow and I/O inefficient. Results We developed fastp as an ultra-fast FASTQ preprocessor with useful quality control and data-filtering features. It can perform quality control, adapter trimming, quality filtering, per-read quality pruning and many other operations with a single scan of the FASTQ data. This tool is developed in C++ and has multi-threading support. Based on our evaluation, fastp is 2–5 times faster than other FASTQ preprocessing tools such as Trimmomatic or Cutadapt despite performing far more operations than similar tools. Availability and implementation The open-source code and corresponding instructions are available at https://github.com/OpenGene/fastp.
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              Improved metagenomic analysis with Kraken 2

              Although Kraken’s k-mer-based approach provides a fast taxonomic classification of metagenomic sequence data, its large memory requirements can be limiting for some applications. Kraken 2 improves upon Kraken 1 by reducing memory usage by 85%, allowing greater amounts of reference genomic data to be used, while maintaining high accuracy and increasing speed fivefold. Kraken 2 also introduces a translated search mode, providing increased sensitivity in viral metagenomics analysis.
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                Author and article information

                Journal
                Periodontology 2000
                Periodontology 2000
                Wiley
                0906-6713
                1600-0757
                June 05 2023
                Affiliations
                [1 ] Department of Periodontics The University of Iowa College of Dentistry and Dental Clinics Iowa USA
                [2 ] Iowa Institute for Oral Health Research The University of Iowa College of Dentistry and Dental Clinics Iowa City Iowa USA
                [3 ] Division of Biostatistics and Computational Biology The University of Iowa College of Dentistry and Dental Clinics Iowa City Iowa USA
                [4 ] Department of Preventive and Community Dentistry The University of Iowa College of Dentistry and Dental Clinics Iowa City Iowa USA
                [5 ] Division of Periodontology University of California at San Francisco School of Dentistry San Francisco California USA
                [6 ] Periodontology Division, Centre for Dental Education and Research All India Institute of Medical Sciences New Delhi India
                [7 ] Section of Biosystems and Function Periodontics University of California at Los Angeles School of Dentistry Los Angeles California USA
                Article
                10.1111/prd.12489
                54812f49-c559-43ed-8080-cc5e3de37b0b
                © 2023

                http://creativecommons.org/licenses/by-nc-nd/4.0/

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