Medusa: um fluxo de trabalho para classificação taxonômica e anotação funcional de metagenomas

Abstract

Metagenomics involves the study of the microbial community found in a sample extracted from a given environment. This environment may be a cave wall, a portion of ocean water, the human gut, or any source containing microorganisms of interest. Such studies unravel details about the taxonomic composition and the functions performed by microbial communities. As a complete metagenomic analysis requires different tools for different purposes, the selection and setup of these tools remain challenging. Furthermore, the chosen toolset will affect the accuracy, the formatting, and the functional identifiers reported in the results, impacting the results interpretation and the biological answer obtained. The work presented here aims to propose a pipeline to be used in taxonomic and functional metagenomic analyses. To this end, state-of-the-art tools available in the literature were surveyed, and mock datasets were created to perform benchmarks. As a result, suited tools were selected for each analysis step, and a sensitive and flexible metagenomic analysis pipeline was designed. MEDUSA, an efficient pipeline to conduct comprehensive metagenomic analyses, performs preprocessing, assembly, alignment, taxonomic classification, and functional annotation on shotgun data, supporting user-built dictionaries to transfer annotations to any functional identifier. MEDUSA includes several tools, such as Fastp, Bowtie2, DIAMOND, Kaiju, MEGAHIT, and a novel tool implemented in Python to transfer annotations to BLAST/DIAMOND alignment results. These tools are installed via Conda, and the workflow is managed by Snakemake, easing the setup and execution. Compared with MEGAN 6 Community Edition, MEDUSA correctly identifies more species, especially the less abundant, and is more suited for functional analysis using Gene Ontology identifiers.