|Title (Primary)||Dadasnake, a Snakemake implementation of DADA2 to process amplicon sequencing data for microbial ecology|
|Author||Weißbecker, C.; Schnabel, B.; Heintz-Buschart, A.|
|Data and Software links||http://doi.org/10.5281/zenodo.4190897|
|Keywords||rRNA gene sequence analysis; denoising; exact sequence variants; R; pipeline; microbiome; community structure|
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.
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.
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.
|Persistent UFZ Identifier||https://www.ufz.de/index.php?en=20939&ufzPublicationIdentifier=23965|
|Weißbecker, C., Schnabel, B., Heintz-Buschart, A. (2020):
Dadasnake, a Snakemake implementation of DADA2 to process amplicon sequencing data for microbial ecology
GigaScience 9 (12), giaa135