MetaBakery: a Singularity implementation of bioBakery tools as a skeleton application for efficient HPC deconvolution of microbiome metagenomic sequencing data to machine learning ready information - Murovec, Boštjan; Deutsch, Leon; Osredkar, Damjan; Stres, Blaž (Frontiers in Microbiology, 2024)
In this study, we present MetaBakery (http://metabakery.fe.uni-lj.si), an integrated application designed as a framework for synergistically executing the bioBakery workflow and associated utilities. MetaBakery streamlines the processing of any number of paired or unpaired fastq files, or a mixture of both, with optional compression (gzip, zip, bzip2, xz, or mixed) within a single run. MetaBakery uses programs such as KneadData (https://github.com/bioBakery/kneaddata), MetaPhlAn, HUMAnN and StrainPhlAn as well as integrated utilities and extends the original functionality of bioBakery. In particular, it includes MelonnPan for the prediction of metabolites and Mothur for calculation of microbial alpha diversity. Written in Python 3 and C++ the whole pipeline was encapsulated as Singularity container for efficient execution on various computing infrastructures, including large High-Performance Computing clusters. MetaBakery facilitates crash recovery, efficient re-execution upon parameter changes, and processing of large data sets through subset handling and is offered in three editions with bioBakery ingredients versions 4, 3 and 2 as versatile, transparent and well documented within the MetaBakery Users' Manual (http://metabakery.fe.uni-lj.si/metabakery_manual.pdf). It provides automatic handling of command line parameters, file formats and comprehensive hierarchical storage of output to simplify navigation and debugging. MetaBakery filters out potential human contamination and excludes samples with low read counts. It calculates estimates of alpha diversity and represents a comprehensive and augmented re-implementation of the bioBakery workflow. The robustness and flexibility of the system enables efficient exploration of changing parameters and input datasets, increasing its utility for microbiome analysis. Furthermore, we have shown that the MetaBakery tool can be used in modern biostatistical and machine learning approaches including large-scale microbiome studies.