MOSHPIT: accessible, reproducible metagenome data science on the QIIME 2 framework

Michał Ziemski; Liz Gehret; Anthony Simard; Santiago Castro Dau; Vinzent Risch; Doriela Grabocka; Christos Konstantinos Matzoros; Colin Wood; Paula Momo Cabrera; Rodrigo Hernández-Velázquez; Chloe Herman; Keegan Evans; Michael Scott Robeson; Evan K Bolyen; J. Gregory Caporaso; Nicholas A. Bokulich

doi:10.1101/2025.01.27.635007

BioinformaticsbioRxivJan 27, 2025

Preprint

MOSHPIT: accessible, reproducible metagenome data science on the QIIME 2 framework

Michał Ziemski iD, Liz Gehret, Anthony Simard iD, Santiago Castro Dau iD, Vinzent Risch iD, Doriela Grabocka iD, Christos Konstantinos Matzoros iD, Colin Wood iD, Paula Momo Cabrera iD, Rodrigo Hernández-Velázquez iD, Chloe Herman iD, Keegan Evans, Michael Scott Robeson iD, Evan K Bolyen iD, J. Gregory Caporaso iD, Nicholas A. Bokulich iD

Abstract

Metagenome sequencing has revolutionized functional microbiome analysis across diverse ecosystems, but is fraught with technical hurdles. We introduce MOSHPIT (https://moshpit.readthedocs.io), software built on the QIIME 2 framework (Q2F) that integrates best-in-class CAMI2-validated metagenome tools with robust provenance tracking and multiple user interfaces, enabling streamlined, reproducible metagenome analysis for all expertise levels. By building on Q2F, MOSHPIT enhances scalability, interoperability, and reproducibility in complex workflows, democratizing and accelerating discovery at the frontiers of metagenomics.

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