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               REFERENCES
               1.       Sorbie A, Delgado Jiménez R, Weiler M, Benakis C. Protocol for microbiota analysis of a murine stroke model. STAR Protoc
                   2023;4:101969.  DOI  PubMed  PMC
               2.       Love CJ, Gubert C, Kodikara S, Kong G, Lê Cao KA, Hannan AJ. Microbiota DNA isolation, 16S rRNA amplicon sequencing, and
                   bioinformatic analysis for bacterial microbiome profiling of rodent fecal samples. STAR Protoc 2022;3:101772.  DOI  PubMed  PMC
               3.       Ghosh TS, Das M. Chapter two - emerging tools for understanding the human microbiome. Prog Mol Biol Transl Sci 2022;191:29-51.
                   DOI
               4.       Amir A. Microbiome analysis using 16S amplicon sequencing: from samples to ASVs. In: Shomron N, editor. Deep sequencing data
                   analysis. Methods in molecular biology. New York; 2021. pp. 123-41.  DOI
               5.       Hornung BVH, Zwittink RD, Kuijper EJ. Issues and current standards of controls in microbiome research. FEMS Microbiol Ecol
                   2019:95.  DOI  PubMed  PMC
               6.       Kim D, Hofstaedter CE, Zhao C, et al. Optimizing methods and dodging pitfalls in microbiome research. Microbiome 2017;5:52.  DOI
                   PubMed  PMC
               7.       Bokulich NA, Ziemski M, Robeson MS 2nd, Kaehler BD. Measuring the microbiome: best practices for developing and benchmarking
                   microbiomics methods. Comput Struct Biotechnol J 2020;18:4048-62.  DOI  PubMed  PMC
               8.       Parada AE, Needham DM, Fuhrman JA. Every base matters: assessing small subunit rRNA primers for marine microbiomes with
                   mock communities, time series and global field samples. Environ Microbiol 2016;18:1403-14.  DOI  PubMed
               9.       Quince C, Lanzén A, Curtis TP, et al. Accurate determination of microbial diversity from 454 pyrosequencing data. Nat Methods
                   2009;6:639-41.  DOI
               10.      Karstens L, Asquith M, Davin S, et al. Controlling for contaminants in low-biomass 16S rRNA gene sequencing experiments.
                   mSystems 2019:4.  DOI  PubMed  PMC
               11.      Salter SJ, Cox MJ, Turek EM, et al. Reagent and laboratory contamination can critically impact sequence-based microbiome analyses.
                   BMC Biol 2014;12:87.  DOI  PubMed  PMC
               12.      Minich JJ, Sanders JG, Amir A, Humphrey G, Gilbert JA, Knight R. Quantifying and understanding well-to-well contamination in
                   microbiome research. mSystems 2019:4.  DOI  PubMed  PMC
               13.      Minich JJ, Zhu Q, Janssen S, et al. KatharoSeq enables high-throughput microbiome analysis from low-biomass samples. mSystems
                   2018:3.  DOI  PubMed  PMC
               14.      Bokulich NA, Kaehler BD, Rideout JR, et al. Optimizing taxonomic classification of marker-gene amplicon sequences with QIIME 2's
                   q2-feature-classifier plugin. Microbiome 2018;6:90.  DOI  PubMed  PMC
               15.      Bolyen E, Rideout JR, Dillon MR, et al. Reproducible, interactive, scalable and extensible microbiome data science using QIIME 2.
                   Nat Biotechnol 2019;37:852-7.  DOI
               16.      Weinstein MM, Prem A, Jin M, Tang S, Bhasin JM. FIGARO: an efficient and objective tool for optimizing microbiome rRNA gene
                   trimming parameters. bioRxiv ;2019:610394.  DOI
               17.      Callahan BJ, McMurdie PJ, Rosen MJ, Han AW, Johnson AJ, Holmes SP. DADA2: High-resolution sample inference from Illumina
                   amplicon data. Nat Methods 2016;13:581-3.  DOI  PubMed  PMC
               18.      Wright ES. Using DECIPHER v2.0 to analyze big biological sequence data in R. Available from: https://pdfs.semanticscholar.org/
                   687f/973e9b1416a1289a86e58474e7259bdb57f1.pdf [Last accessed on 26 Apr 2023].
               19.      Wickham H, Averick M, Bryan J, et al. Welcome to the Tidyverse. J Open Res Softw 2019;4:1686.  DOI
               20.      Lahti L, Shetty SA. Tools for microbiome analysis in R. Available from: https://bioconductor.org/packages/release/bioc/html/
                   microbiome.html [Last accessed on 26 Apr 2023].
               21.      McMurdie PJ, Holmes S. phyloseq: an R package for reproducible interactive analysis and graphics of microbiome census data. PLoS
                   One 2013;8:e61217.  DOI  PubMed  PMC
               22.      Pedersen TL. Patchwork: the composer of plots. Available from: https://github.com/thomasp85/patchwork [Last accessed on 26 Apr
                   2023].
               23.      Murali A, Bhargava A, Wright ES. IDTAXA: a novel approach for accurate taxonomic classification of microbiome sequences.
                   Microbiome 2018;6:140.  DOI  PubMed  PMC
               24.      Ramiro-Garcia J, Hermes GDA, Giatsis C, et al. NG-Tax, a highly accurate and validated pipeline for analysis of 16S rRNA amplicons
                   from complex biomes. F1000Res 2016;5:1791.  DOI  PubMed  PMC
               25.      Shetty SA, Kostopoulos I, Geerlings SY, Smidt H, de Vos WM, Belzer C. Dynamic metabolic interactions and trophic roles of human
                   gut microbes identified using a minimal microbiome exhibiting ecological properties. ISME J 2022;16:2144-59.  DOI  PubMed  PMC
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