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.
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.
3. Ghosh TS, Das M. Chapter two - emerging tools for understanding the human microbiome. Prog Mol Biol Transl Sci 2022;191:29-51.
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.
5. Hornung BVH, Zwittink RD, Kuijper EJ. Issues and current standards of controls in microbiome research. FEMS Microbiol Ecol 2019:95.
6. Kim D, Hofstaedter CE, Zhao C, et al. Optimizing methods and dodging pitfalls in microbiome research. Microbiome 2017;5:52.
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.
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.
9. Quince C, Lanzén A, Curtis TP, et al. Accurate determination of microbial diversity from 454 pyrosequencing data. Nat Methods 2009;6:639-41.
10. Karstens L, Asquith M, Davin S, et al. Controlling for contaminants in low-biomass 16S rRNA gene sequencing experiments. mSystems 2019:4.
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.
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.
13. Minich JJ, Zhu Q, Janssen S, et al. KatharoSeq enables high-throughput microbiome analysis from low-biomass samples. mSystems 2018:3.
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.
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.
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.
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.
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].
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.
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.
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.
