MOSHPIT tutorials

MOSHPIT tutorials#

MOSHPIT (MOdular SHotgun metagenome Pipelines with Integrated provenance Tracking) is a toolkit of plugins for whole metagenome assembly, annotation, and analysis built on the microbiome multi-omics data science framework QIIME 2. MOSHPIT enables flexible, modular, fully reproducible workflows for read-based or assembly-based analysis of metagenome data.

The following main plugins comprise the core of the MOSHPIT toolkit:

  • q2-assembly: contains actions for (meta)genome and quality control, genome indexing and red mapping

  • q2-annotate: provides actions for contig binning and quality control, taxonomic and functional annotations of contigs and MAGs, human host removal.

Additionally, you may want to check out these other QIIME 2 plugins for antimicrobial resistance gene (ARG) detection and viromics applications. These plugins are not covered in this tutorial. They have their own installation instructions and tutorials (see the wiki page on the respective GitHub repositories). You can use these plugins with some of the artifacts produced by q2-assembly and q2-annotate:

  • q2-rgi: antimicrobial resistance gene annotation of MAGs and metagenomic reads with RGI and CARD

  • q2-amrfinderplus: ARG detection using the AMRFinderPlus tool

  • q2-viromics: detection of viral sequences and their quality control.

This tutorial will guide you through the process of analyzing metagenomic data using QIIME 2 framework and MOSHPIT. The tutorial is divided into several chapters, each focusing on a different aspect of metagenomic data analysis. We will use a small published dataset to demonstrate the capabilities of most of the methods available in MOSHPIT.

We will begin by setting up our computational environment and fetching all the necessary data (see Setup and Data retrieval). Then, we will move to quality control and filtering of the raw reads (see Quality control). Once we have our clean dataset, we can start by recovering metagenome-assembled genomes (MAGs) (see here), followed by taxonomic classification of reads and MAGs themselves (see Taxonomic classification). Finally, we will estimate perform functional annotation of the dereplicated MAGs (see Functional annotation).

Let’s dive in!