AutoTA: Galaxy Workflows for Reproducible and Automated Taxonomic Analysis using Qiime2

Abstract

AbstractMetagenomic sequencing allows systematic characterization of microbial populations isolated from various environments of interest by bypassing the culturing of the isolates. Concomitant to advancement in sequencing techniques, analysis methods and softwares have also grown to be sophisticated and efficient. Qiime2 is a collection of python scripts which enables end-to-end analysis of metagenomic data. However, usage of latest and more complex databases for classification is hindered by requirement of high compute power. To aid cloud-based analysis, we present workflows for diversity analysis and taxonomic assignment which are the two most common and initial steps in a metagenomics experiments. The workflows are made in Galaxy which makes testing and analysing multiple datasets faster, in parallel, reproducible and flexible. The workflows can be integrated into a local Galaxy instance or used completely on the web which is of great importance to non-bioinformaticians and bench scientists.