ArticleBioinformatics (Oxford, England)2023
Capturing variation in metagenomic assembly graphs with MetaCortex.
Article in Bioinformatics (Oxford, England), 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 5 papers.
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Who cites it
5 citing papers in PubMed.
- Computational Tools and Resources for Long-read Metagenomic Sequencing Using Nanopore and PacBio.Genomics, proteomics & bioinformatics · 2025Review
- Metagenomic assemblies tend to break around antibiotic resistance genes.BMC genomics · 2024Article
- Graph-based self-supervised learning for repeat detection in metagenomic assembly.Genome research · 2024Article
- Exploring high-quality microbial genomes by assembling short-reads with long-range connectivity.Nature communications · 2024Article
- KombOver: Efficient k-core and K-truss based characterization of perturbations within the human gut microbiome.Pacific Symposium on Biocomputing. Pacific Symposium on Biocomputing · 2024Article
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Authors and funding
6 authors.
Funding
Abstract
motivationThe 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.
resultsWe 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.
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