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Dadasnake, a Snakemake implementation of DADA2 to process amplicon sequencing data for microbial ecology
BACKGROUND: Amplicon sequencing of phylogenetic marker genes, e.g., 16S, 18S, or ITS ribosomal RNA sequences, is still the most commonly used method to determine the composition of microbial communities. Microbial ecologists often have expert knowledge on their biological question and data analysis...
Autores principales: | , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Oxford University Press
2020
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7702218/ https://www.ncbi.nlm.nih.gov/pubmed/33252655 http://dx.doi.org/10.1093/gigascience/giaa135 |
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author | Weißbecker, Christina Schnabel, Beatrix Heintz-Buschart, Anna |
author_facet | Weißbecker, Christina Schnabel, Beatrix Heintz-Buschart, Anna |
author_sort | Weißbecker, Christina |
collection | PubMed |
description | BACKGROUND: Amplicon sequencing of phylogenetic marker genes, e.g., 16S, 18S, or ITS ribosomal RNA sequences, is still the most commonly used method to determine the composition of microbial communities. Microbial ecologists often have expert knowledge on their biological question and data analysis in general, and most research institutes have computational infrastructures to use the bioinformatics command line tools and workflows for amplicon sequencing analysis, but requirements of bioinformatics skills often limit the efficient and up-to-date use of computational resources. RESULTS: We present dadasnake, a user-friendly, 1-command Snakemake pipeline that wraps the preprocessing of sequencing reads and the delineation of exact sequence variants by using the favorably benchmarked and widely used DADA2 algorithm with a taxonomic classification and the post-processing of the resultant tables, including hand-off in standard formats. The suitability of the provided default configurations is demonstrated using mock community data from bacteria and archaea, as well as fungi. CONCLUSIONS: By use of Snakemake, dadasnake makes efficient use of high-performance computing infrastructures. Easy user configuration guarantees flexibility of all steps, including the processing of data from multiple sequencing platforms. It is easy to install dadasnake via conda environments. dadasnake is available at https://github.com/a-h-b/dadasnake. |
format | Online Article Text |
id | pubmed-7702218 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2020 |
publisher | Oxford University Press |
record_format | MEDLINE/PubMed |
spelling | pubmed-77022182020-12-07 Dadasnake, a Snakemake implementation of DADA2 to process amplicon sequencing data for microbial ecology Weißbecker, Christina Schnabel, Beatrix Heintz-Buschart, Anna Gigascience Technical Note BACKGROUND: Amplicon sequencing of phylogenetic marker genes, e.g., 16S, 18S, or ITS ribosomal RNA sequences, is still the most commonly used method to determine the composition of microbial communities. Microbial ecologists often have expert knowledge on their biological question and data analysis in general, and most research institutes have computational infrastructures to use the bioinformatics command line tools and workflows for amplicon sequencing analysis, but requirements of bioinformatics skills often limit the efficient and up-to-date use of computational resources. RESULTS: We present dadasnake, a user-friendly, 1-command Snakemake pipeline that wraps the preprocessing of sequencing reads and the delineation of exact sequence variants by using the favorably benchmarked and widely used DADA2 algorithm with a taxonomic classification and the post-processing of the resultant tables, including hand-off in standard formats. The suitability of the provided default configurations is demonstrated using mock community data from bacteria and archaea, as well as fungi. CONCLUSIONS: By use of Snakemake, dadasnake makes efficient use of high-performance computing infrastructures. Easy user configuration guarantees flexibility of all steps, including the processing of data from multiple sequencing platforms. It is easy to install dadasnake via conda environments. dadasnake is available at https://github.com/a-h-b/dadasnake. Oxford University Press 2020-11-30 /pmc/articles/PMC7702218/ /pubmed/33252655 http://dx.doi.org/10.1093/gigascience/giaa135 Text en © The Author(s) 2020. Published by Oxford University Press GigaScience. http://creativecommons.org/licenses/by/4.0/ This is an Open Access article distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted reuse, distribution, and reproduction in any medium, provided the original work is properly cited. |
spellingShingle | Technical Note Weißbecker, Christina Schnabel, Beatrix Heintz-Buschart, Anna Dadasnake, a Snakemake implementation of DADA2 to process amplicon sequencing data for microbial ecology |
title | Dadasnake, a Snakemake implementation of DADA2 to process amplicon sequencing data for microbial ecology |
title_full | Dadasnake, a Snakemake implementation of DADA2 to process amplicon sequencing data for microbial ecology |
title_fullStr | Dadasnake, a Snakemake implementation of DADA2 to process amplicon sequencing data for microbial ecology |
title_full_unstemmed | Dadasnake, a Snakemake implementation of DADA2 to process amplicon sequencing data for microbial ecology |
title_short | Dadasnake, a Snakemake implementation of DADA2 to process amplicon sequencing data for microbial ecology |
title_sort | dadasnake, a snakemake implementation of dada2 to process amplicon sequencing data for microbial ecology |
topic | Technical Note |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7702218/ https://www.ncbi.nlm.nih.gov/pubmed/33252655 http://dx.doi.org/10.1093/gigascience/giaa135 |
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