ArticleComputational and structural biotechnology journal2023
Unitig level assembly graph based metagenome-assembled genome refiner (UGMAGrefiner): A tool to increase completeness and resolution of metagenome-assembled genomes.
Article in Computational and structural biotechnology journal, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 4 papers.
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Who cites it
4 citing papers in PubMed, 4 citations in OpenAlex.
- Integrated Genome-Wide Association Study and Machine Learning Approach for Characterizing the Determinants of Biofilm Formation in Staphylococcus aureus.Interdisciplinary sciences, computational life sciences · 2026Article
- Using GWAS and Machine Learning to Identify and Predict Genetic Variants Associated with Foodborne Bacteria Phenotypic Traits.Methods in molecular biology (Clifton, N.J.) · 2025Review
- Updated perspectives on the contribution of the microbiome to the pathogenesis of mucositis using the MASCC/ISOO framework.Supportive care in cancer : official journal of the Multinational Association of Supportive Care in Cancer · 2024Review
- Solving genomic puzzles: computational methods for metagenomic binning.Briefings in bioinformatics · 2024Review
Corrections and comments
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Authors and funding
3 authors at 1 institution in 1 country.
Funding
No grant is acknowledged in the PubMed record.
Abstract
De novo assembly of next generation metagenomic reads is widely used to provide taxonomic and functional information of genomes in a microbial community. As strains are functionally specific, recovery of strain-resolved genomes is important but still a challenge. Unitigs and assembly graphs are mid-products generated during the assembly of reads into contigs, and they provide higher resolution for sequences connection information. In this study, we propose a new approach UGMAGrefiner (a unitig level assembly graph-based metagenome-assembled Genome refiner), which uses the connection and coverage information from unitig level assembly graphs to recruit unbinned unitigs to MAGs, adjust binning result, and infer unitigs shared by multiple MAGs. In two simulated datasets (Simdata and CAMI data) and one real dataset (GD02), it outperforms two state-of-the-art assembly graph-based binning refine tools in the refinement of MAGs' quality by stably increasing the completeness of genomes. UGMAGrefiner can identify genome specific clusters of genomes with below 99% average nucleotide identity for homologous sequences. For MAGs mixed with 99% similarity genome clusters, it could distinguish 8 out of 9 genomes in Simdata and 8 out of 12 genomes in CAMI data. In GD02 data, it could identify 16 new unitig clusters representing genome specific regions of mixed genomes and 4 unitig clusters representing new genomes from total 135 MAGs for further functional analysis. UGMAGrefiner provides an efficient way to obtain more complete MAGs and study genome specific functions. It will be useful to improve taxonomic and functional information of genomes after de novo assembly.
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.