ArticleMicrobiome2022
Evaluating metagenomic assembly approaches for biome-specific gene catalogues.
Article in Microbiome, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 32 papers.
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32 citing papers in PubMed, 48 citations in OpenAlex.
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- A review of computational approaches for metagenomics by long-read sequencing.Science China. Life sciences · 2026Review
- A structure-informed deep learning framework for modeling TCR-peptide-HLA interactions.bioRxiv : the preprint server for biology · 2026Article
- Troubleshooting common errors in assemblies of long-read metagenomes.Nature biotechnology · 2026Article
- Computational Microbial and Viral Ecology Analysis.Methods in molecular biology (Clifton, N.J.) · 2026Article
- Comparative metagenomics using pan-metagenomic graphs.bioRxiv : the preprint server for biology · 2025Article
- Evaluation of metagenome binning: advances and challenges.Briefings in bioinformatics · 2025Article
- Host species and geographic location shape microbial diversity and functional potential in the conifer needle microbiome.Microbiome · 2025Article
- Enhancing genome recovery across metagenomic samples using MAGmax.Bioinformatics (Oxford, England) · 2025Article
- Highly-accurate prediction of colorectal cancer through low abundance uncultivated genomes recovered using metagenomic co-assembly and binning approach.BMC cancer · 2025Article
- A Graph Contrastive Learning Method for Enhancing Genome Recovery in Complex Microbial Communities.Entropy (Basel, Switzerland) · 2025Article
- De novo peptide databases enable protein-based stable isotope probing of microbial communities with up to species-level resolution.Environmental microbiome · 2025Article
- The impact of pooling on the observed microbiome profile of preweaned piglet feces.FEMS microbiology ecology · 2025Article
- A metagenomic perspective on the microbial prokaryotic genome census.Science advances · 2025Article
- Article
- Missing microbial eukaryotes and misleading meta-omic conclusions.Nature communications · 2024Article
- Genome-centric metagenomics provides insights into the core microbial community and functional profiles of biofloc aquaculture.mSystems · 2024Article
- From soil to sequence: filling the critical gap in genome-resolved metagenomics is essential to the future of soil microbial ecology.Environmental microbiome · 2024Article
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Authors and funding
2 authors at 1 institution in 1 country.
Funding
No grant is acknowledged in the PubMed record.
Abstract
backgroundFor many environments, biome-specific microbial gene catalogues are being recovered using shotgun metagenomics followed by assembly and gene calling on the assembled contigs. The assembly is typically conducted either by individually assembling each sample or by co-assembling reads from all the samples. The co-assembly approach can potentially recover genes that display too low abundance to be assembled from individual samples. On the other hand, combining samples increases the risk of mixing data from closely related strains, which can hamper the assembly process. In this respect, assembly on individual samples followed by clustering of (near) identical genes is preferable. Thus, both approaches have potential pros and cons, but it remains to be evaluated which assembly strategy is most effective. Here, we have evaluated three assembly strategies for generating gene catalogues from metagenomes using a dataset of 124 samples from the Baltic Sea: (1) assembly on individual samples followed by clustering of the resulting genes, (2) co-assembly on all samples, and (3) mix assembly, combining individual and co-assembly.
resultsThe mix-assembly approach resulted in a more extensive nonredundant gene set than the other approaches and with more genes predicted to be complete and that could be functionally annotated. The mix assembly consists of 67 million genes (Baltic Sea gene set, BAGS) that have been functionally and taxonomically annotated. The majority of the BAGS genes are dissimilar (< 95% amino acid identity) to the Tara Oceans gene dataset, and hence, BAGS represents a valuable resource for brackish water research.
conclusionThe mix-assembly approach represents a feasible approach to increase the information obtained from metagenomic samples. Video abstract.
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