Martin S, Ayling M, Patrono L, Caccamo M, Murcia P, Leggett RM. Capturing variation in metagenomic assembly graphs with MetaCortex.
Bioinformatics 2023;
39:6986127. [PMID:
36722204 PMCID:
PMC9889960 DOI:
10.1093/bioinformatics/btad020]
[Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/20/2022] [Revised: 11/10/2022] [Accepted: 01/11/2023] [Indexed: 01/13/2023] Open
Abstract
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 AND IMPLEMENTATION
Source code is freely available to download from https://github.com/SR-Martin/metacortex, is implemented in C and supported on MacOS and Linux. The version used for the results presented in this article is available at doi.org/10.5281/zenodo.7273627.
SUPPLEMENTARY INFORMATION
Supplementary data are available at Bioinformatics online.
Collapse