AUTHOR=Abdala Asbun Alejandro , Besseling Marc A. , Balzano Sergio , van Bleijswijk Judith D. L. , Witte Harry J. , Villanueva Laura , Engelmann Julia C. TITLE=Cascabel: A Scalable and Versatile Amplicon Sequence Data Analysis Pipeline Delivering Reproducible and Documented Results JOURNAL=Frontiers in Genetics VOLUME=11 YEAR=2020 URL=https://www.frontiersin.org/journals/genetics/articles/10.3389/fgene.2020.489357 DOI=10.3389/fgene.2020.489357 ISSN=1664-8021 ABSTRACT=

Marker gene sequencing of the rRNA operon (16S, 18S, ITS) or cytochrome c oxidase I (CO1) is a popular means to assess microbial communities of the environment, microbiomes associated with plants and animals, as well as communities of multicellular organisms via environmental DNA sequencing. Since this technique is based on sequencing a single gene, or even only parts of a single gene rather than the entire genome, the number of reads needed per sample to assess the microbial community structure is lower than that required for metagenome sequencing. This makes marker gene sequencing affordable to nearly any laboratory. Despite the relative ease and cost-efficiency of data generation, analyzing the resulting sequence data requires computational skills that may go beyond the standard repertoire of a current molecular biologist/ecologist. We have developed Cascabel, a scalable, flexible, and easy-to-use amplicon sequence data analysis pipeline, which uses Snakemake and a combination of existing and newly developed solutions for its computational steps. Cascabel takes the raw data as input and delivers a table of operational taxonomic units (OTUs) or Amplicon Sequence Variants (ASVs) in BIOM and text format and representative sequences. Cascabel is a highly versatile software that allows users to customize several steps of the pipeline, such as selecting from a set of OTU clustering methods or performing ASV analysis. In addition, we designed Cascabel to run in any linux/unix computing environment from desktop computers to computing servers making use of parallel processing if possible. The analyses and results are fully reproducible and documented in an HTML and optional pdf report. Cascabel is freely available at Github: https://github.com/AlejandroAb/CASCABEL.