Qiime Artifact eXtractor (qax): A Fast and Versatile Tool to Interact with Qiime2 Archives.

Andrea Telatin

doi:10.3390/biotech10010005

Abstract

Qiime2 is one of the most popular software tools used for analysis of output from metabarcoding experiments (e.g., sequencing of 16S, 18S, or ITS amplicons). Qiime2 introduced a novel and innovative data exchange format: the ‘Qiime2 artifact’. Qiime2 artifacts are structured compressed archives containing a dataset and its associated metadata. Examples of datasets are FASTQ reads, representative sequences in FASTA format, a phylogenetic tree in Newick format, while examples of metadata are the command that generated the artifact, information on the execution environment, citations on the used software, and all the metadata of the artifacts used to produce it. While artifacts can improve the shareability and reproducibility of Qiime2 workflows, they are less easily integrated with general bioinformatics pipelines. Accessing metadata in the artifacts also requires full Qiime2 installation. Qiime Artifact eXtractor (qax) allows users to easily interface with Qiime2 artifacts from the command line, without needing the full Qiime2 environment installed (or activated).

Highlights

The bioinformatic analysis of amplicon sequences generated by metabarcoding experiments is a complex workflow involving several steps
One of the most-used packages for the analysis of amplicon sequences is Qiime2 (Quantitative Insights into Microbial Ecology) [2], an extensible platform that includes all the tools required for the analysis of raw reads in a coherent and highly structured environment
Qax has a modular architecture with a core parsing unit based on the standard ‘zip/zipfiles’ module, and shared across the subprograms of the suite, namely: list, extract, cite, provenance and make

Summary

Introduction

The bioinformatic analysis of amplicon sequences generated by metabarcoding experiments is a complex workflow involving several steps (from the initial trimming the locus-specific primers from the raw reads to the generation of a contingency table). One of the most-used packages for the analysis of amplicon sequences is Qiime (Quantitative Insights into Microbial Ecology) [2], an extensible platform that includes all the tools required for the analysis of raw reads in a coherent and highly structured environment. Qiime provides the framework to integrate different tools (e.g., cutadapt [3] to remove the primers, and DADA2 [4] to identify the representative sequences) in a coherent workflow, sometimes providing alternative tools to perform the same task (e.g., deblur [5] as an alternative to DADA2).

Methods

Results

Conclusion