Abstract
Taxonomic profiling among a large number of samples is a fundamental task during amplicon sequencing analysis. The heterogeneity and technical noises in the sample handling, library preparation, and sequencing present a major challenge to how the biological conclusions are drawn from the data analysis, and accordingly, many tools have been developed to address specific issues related to each step of the data analysis. Nowadays, several sophisticated computational pipelines with flexible parameters are made available to provide one-stop comprehensive solutions by integrating various tools, which significantly mitigate the burden imposed by the complexity of the metagenomics data analysis. This chapter discusses the best practices related to the data generation and describes bioinformatics approaches to achieving greater accuracy from data processing. It offers two independent stepwise pipelines using mothur and DADA2 in a parallel way, presents the basic principles in the key steps of the analysis, and enables the comparisons between the two pipelines straightforwardly.
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References
Hugerth LW, Andersson AF (2017) Analysing microbial community composition through amplicon sequencing: from sampling to hypothesis testing. Front Microbiol 8:1561
Golebiewski M, Tretyn A (2020) Generating amplicon reads for microbial community assessment with next-generation sequencing. J Appl Microbiol 128:330–354
Bharti R, Grimm DG (2021) Current challenges and best-practice protocols for microbiome analysis. Brief Bioinform 22:178–193
Prodan A, Tremaroli V, Brolin H, Zwinderman AH, Nieuwdorp M, Levin E (2020) Comparing bioinformatic pipelines for microbial 16S rRNA amplicon sequencing. PLoS One 15:e0227434
Weissbecker C, Schnabel B, Heintz-Buschart A (2020) Dadasnake, a Snakemake implementation of DADA2 to process amplicon sequencing data for microbial ecology. Gigascience 9
Schloss PD, Westcott SL, Ryabin T, Hall JR, Hartmann M, Hollister EB, Lesniewski RA, Oakley BB, Parks DH, Robinson CJ, Sahl JW, Stres B, Thallinger GG, Van Horn DJ, Weber CF (2009) Introducing mothur: open-source, platform-independent, community-supported software for describing and comparing microbial communities. Appl Environ Microbiol 75:7537–7541
Callahan BJ, McMurdie PJ, Rosen MJ, Han AW, Johnson AJ, Holmes SP (2016) DADA2: high-resolution sample inference from Illumina amplicon data. Nat Methods 13:581–583
Abellan-Schneyder I, Matchado MS, Reitmeier S, Sommer A, Sewald Z, Baumbach J, List M, Neuhaus K (2021) Primer, pipelines, parameters: issues in 16S rRNA gene sequencing. mSphere 6
Pollock J, Glendinning L, Wisedchanwet T, Watson M (2018) The madness of microbiome: attempting to find consensus “best practice” for 16S microbiome studies. Appl Environ Microbiol 84
Gruning B, Dale R, Sjodin A, Chapman BA, Rowe J, Tomkins-Tinch CH, Valieris R, Koster J, Bioconda T (2018) Bioconda: sustainable and comprehensive software distribution for the life sciences. Nat Methods 15:475–476
Cock PJ, Fields CJ, Goto N, Heuer ML, Rice PM (2010) The Sanger FASTQ file format for sequences with quality scores, and the Solexa/Illumina FASTQ variants. Nucleic Acids Res 38:1767–1771
Martin M (2011) Cutadapt removes adapter sequences from high-throughput sequencing reads. EMBnet J 17:10–12
Kozich JJ, Westcott SL, Baxter NT, Highlander SK, Schloss PD (2013) Development of a dual-index sequencing strategy and curation pipeline for analyzing amplicon sequence data on the MiSeq Illumina sequencing platform. Appl Environ Microbiol 79:5112–5120
McMurdie PJ, Holmes S (2013) Phyloseq: an R package for reproducible interactive analysis and graphics of microbiome census data. PLoS One 8:e61217
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Wang, D. (2023). Amplicon Sequencing Pipelines in Metagenomics. In: Mitra, S. (eds) Metagenomic Data Analysis. Methods in Molecular Biology, vol 2649. Humana, New York, NY. https://doi.org/10.1007/978-1-0716-3072-3_4
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DOI: https://doi.org/10.1007/978-1-0716-3072-3_4
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