dr Svetlana Ugarčina Perović, Big Data Biology Lab, Fudan University, Shanghai, China
Abstract: The antibiotic resistance genes (ARGs) in both host-associated and environmental microbiomes – antibiotic resistome – play an important role in the spread of antibiotic resistance. Metagenomics enables high-throughput exploration of microbiomes. One important step in these analyses is ARGs annotation within the metagenomes, for which several tools are in use. However, most of these tools are developed for genomics studies and their databases may pose certain biases. We aimed to compare outputs from different ARGs annotation tools for metagenomes and detect their potential challenges in microbiome studies. We ran >13 000 high-quality metagenomes from 14 habitats (Coelho et al., 2022; https://gmgc.embl.de/) through three ARGs annotation pipelines (with default settings) using DeepARG (CARD, ARDB, UNIPROT; https://bench.cs.vt.edu/deeparg), RGI (CARD; https://card.mcmaster.ca/) and ABRicate (CARD, ResFinder, ResFinderFG, NCBI, MEGARes, ARG ANNOT; https://github.com/tseemann/abricate), and compared their outputs ( https://github.com/pha4ge/hAMRonization ). To facilitate comparison of outputs with different gene_names, we performed ARO normalization (https://github.com/AdeBC/quick_amr_db_harmonisation, based on work by Finlay Maguire) i.e. mapping NCBI, ResFinder and ResFinderFG to CARD's ARO ontology. DeepARG and RGI provide higher coverage (with potential novel ARGs and/or false positives) while ABRicate with different databases has lower coverage but more well validated ARGs. The annotations did not differ only in number of hits but also in the information provided: e.g.using ABRicate, more sulfonamide resistance genes were identified using ResFinderFG version 2.0 as the database (ARGs obtained by functional metagenomics by Gschwind et al.), while using ResFinder resulted in more macrolide-resistance genes. Thus, choice of annotation tool and database should be driven by research questions and ARGs targets.
Biography: Dr Svetlana Ugarčina Perović is a postdoctoral researcher in the Big Data Biology Lab at Fudan University (Shanghai, China) https://www.big-data-biology.org/. Svetlana holds a B.S. and Ph.D. in Environmental Sciences from the University of Novi Sad (Serbia). Her postdoctoral work at the University of Glasgow (the UK) and University of Porto (Portugal) focused on drinking water microbiome in distribution systems and urinary microbiome in women health. Her current interest includes computational approaches in microbial ecology to explore global microbiome and antimicrobial resistome within the EMBARK project. Svetlana is a strong supporter of the open science initiatives, such as the Microbiome Digest, Microbiome Virtual International Forum, National Summer Undergraduate Research Project etc