<scp>MuDoGeR</scp>: <scp>Multi‐Domain</scp> Genome recovery from metagenomes made easy

Rocha, Ulisses; Coelho Kasmanas, Jonas; Kallies, René; Saraiva, Joao Pedro; Toscan, Rodolfo Brizola; Štefanič, Polonca; Bicalho, Marcos Fleming; Borim Correa, Felipe; Baştürk, Merve Nida; Fousekis, Efthymios; Viana Barbosa, Luiz Miguel; Plewka, Julia; Probst, Alexander J.; Baldrian, Petr; Stadler, Peter F.; ,

doi:10.1111/1755-0998.13904

Cited by 5 publications

(1 citation statement)

References 96 publications

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“…We used the Multi-Domain Genome Recovery v1.0.1 pipeline to identify viral contigs from the 165 metagenome libraries [34]. Briefly, VirSorter 2 v2.2.4 [35], VirFinder v1.1 [36], and VIBRANT v1.2.1 [37] were used with default settings to identify viral contigs from assemblies that were generated with Spades 3.15.2 [38].…”

Section: Virus Sequence Identificationmentioning

confidence: 99%

Identification of Huge Phages from Wastewater Metagenomes

Kallies,

Hu,

Abdulkadir

et al. 2023

Viruses

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Huge phages have genomes larger than 200 kilobases, which are particularly interesting for their genetic inventory and evolution. We screened 165 wastewater metagenomes for the presence of viral sequences. After identifying over 600 potential huge phage genomes, we reduced the dataset using manual curation by excluding viral contigs that did not contain viral protein-coding genes or consisted of concatemers of several small phage genomes. This dataset showed seven fully annotated huge phage genomes. The phages grouped into distinct phylogenetic clades, likely forming new genera and families. A phylogenomic analysis between our huge phages and phages with smaller genomes, i.e., less than 200 kb, supported the hypothesis that huge phages have undergone convergent evolution. The genomes contained typical phage protein-coding genes, sequential gene cassettes for metabolic pathways, and complete inventories of tRNA genes covering all standard and rare amino acids. Our study showed a pipeline for huge phage analyses that may lead to new enzymes for therapeutic or biotechnological applications.

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Section: Virus Sequence Identificationmentioning

confidence: 99%

Identification of Huge Phages from Wastewater Metagenomes

Kallies,

Hu,

Abdulkadir

et al. 2023

Viruses

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Unveiling the microbial realm with VEBA 2.0: a modular bioinformatics suite for end-to-end genome-resolved prokaryotic, (micro)eukaryotic and viral multi-omics from either short- or long-read sequencing

Espinoza,

Phillips,

Prentice

et al. 2024

Nucleic Acids Research

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The microbiome is a complex community of microorganisms, encompassing prokaryotic (bacterial and archaeal), eukaryotic, and viral entities. This microbial ensemble plays a pivotal role in influencing the health and productivity of diverse ecosystems while shaping the web of life. However, many software suites developed to study microbiomes analyze only the prokaryotic community and provide limited to no support for viruses and microeukaryotes. Previously, we introduced the Viral Eukaryotic Bacterial Archaeal (VEBA) open-source software suite to address this critical gap in microbiome research by extending genome-resolved analysis beyond prokaryotes to encompass the understudied realms of eukaryotes and viruses. Here we present VEBA 2.0 with key updates including a comprehensive clustered microeukaryotic protein database, rapid genome/protein-level clustering, bioprospecting, non-coding/organelle gene modeling, genome-resolved taxonomic/pathway profiling, long-read support, and containerization. We demonstrate VEBA’s versatile application through the analysis of diverse case studies including marine water, Siberian permafrost, and white-tailed deer lung tissues with the latter showcasing how to identify integrated viruses. VEBA represents a crucial advancement in microbiome research, offering a powerful and accessible software suite that bridges the gap between genomics and biotechnological solutions.

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Simulation of 69 microbial communities indicates sequencing depth and false positives are major drivers of bias in Prokaryotic metagenome-assembled genome recovery

Rocha

Kasmanas

Toscan

et al. 2023

Preprint

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We hypothesize that sample evenness, sequencing depth and taxonomic relatedness influence the recovery of metagenome-assembled genomes (MAGs). To test this hypothesis, we assessed MAG recovery in three in silico microbial communities composed of 42 species with the same richness but different sample evenness, sequencing depth and taxonomic distribution profiles using three different pipelines for MAG recovery. The pipeline developed by Parks and colleagues (8K) generated the highest number of MAGs and the lowest number of true positives per community profile. The pipeline by Karst and colleagues (DT) showed the most accurate results (~ 92%), outperforming the 8K and Multi-Metagenome pipeline (MM) developed by Albertsen and collaborators. Sequencing depth influenced the accurate recovery of genomes when using the 8K and MM, even with contrasting patterns: the MM pipeline recovered more MAGs found in the original communities when employing sequencing depths up to 60 million reads, whilst the 8K recovered more true positives in communities sequenced above 60 million reads. DT showed the best species recovery from the same genus, even though close-related species have a low recovery rate in all pipelines. Our results highlight that more bins do not translate to the actual community composition and that sequencing depth plays a role in MAG recovery and increased community resolution. Even low MAG recovery error rates can significantly impact biological inferences. Our data indicates the scientific community should their findings from MAG recovery, especially when asserting novel species or metabolic traits.

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MuDoGeR: Multi‐Domain Genome recovery from metagenomes made easy

Cited by 5 publications

References 96 publications

Identification of Huge Phages from Wastewater Metagenomes

Identification of Huge Phages from Wastewater Metagenomes

Unveiling the microbial realm with VEBA 2.0: a modular bioinformatics suite for end-to-end genome-resolved prokaryotic, (micro)eukaryotic and viral multi-omics from either short- or long-read sequencing

Simulation of 69 microbial communities indicates sequencing depth and false positives are major drivers of bias in Prokaryotic metagenome-assembled genome recovery

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