Capturing variation in metagenomic assembly graphs with MetaCortex

Martin, S., Ayling, M., Patrono, L., Caccamo, M., Murcia, P. and Leggett, R. M. (2023) Capturing variation in metagenomic assembly graphs with MetaCortex. Bioinformatics, 39(1), btad020. (doi: 10.1093/bioinformatics/btad020) (PMID:36722204) (PMCID:PMC9889960)

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Motivation: The assembly of contiguous sequence from metagenomic samples presents a particular challenge, due to the presence of multiple species, often closely related, at varying levels of abundance. Capturing diversity within species, for example viral haplotypes, or bacterial strain-level diversity, is even more challenging. Results: We present MetaCortex, a metagenome assembler that captures intra-species diversity by searching for signatures of local variation along assembled sequences in the underlying assembly graph and outputting these sequences in sequence graph format. We show that MetaCortex produces accurate assemblies with higher genome coverage and contiguity than other popular metagenomic assemblers on mock viral communities with high levels of strain level diversity, and on simulated communities containing simulated strains. Availability: Source code is freely available to download from, is implemented in C and supported on MacOS and Linux. The version used for the results presented in this paper is available at Supplementary information: Supplementary data are available at Bioinformatics online.

Item Type:Articles
Additional Information:This work was supported by the Biotechnology and Biological Sciences Research Council (BBSRC), part of UK Research and Innovation, through Responsive Mode award BB/M004805/1, Core Capability Grant BB/CCG1720/1, Core Strategic Programme Grant BB/CSP1720/1.
Glasgow Author(s) Enlighten ID:Murcia, Professor Pablo
Authors: Martin, S., Ayling, M., Patrono, L., Caccamo, M., Murcia, P., and Leggett, R. M.
College/School:College of Medical Veterinary and Life Sciences > School of Infection & Immunity > Centre for Virus Research
Journal Name:Bioinformatics
Publisher:Oxford University Press
ISSN (Online):1367-4811
Published Online:12 January 2023
Copyright Holders:Copyright © 2023 The Authors
First Published:First published in Bioinformatics 39(1): btad020
Publisher Policy:Reproduced under a Creative Commons License
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Data DOI:10.5281/zenodo.7273627

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