MetaGeneMark-2: Improved Gene Prediction in Metagenomes

Gemayel, Karl; Lomsadze, Alexandre; Borodovsky, Mark

doi:10.1101/2022.07.25.500264

Cited by 12 publications

(5 citation statements)

References 29 publications

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“…Fragments shorter than 500 bp in the contigs were removed from the assembly. MetaGeneMark (v2.10) ( Gemayel et al, 2022 ) was then used to predict genes on contigs, and the information with a length less than 100 nt in the prediction results was filtered out. We used the CD-HIT software (v4.5.8) 2 with the default setting to create a unique initial gene catalog.…”

Section: Methodsmentioning

confidence: 99%

Impact of low-dose ozone nanobubble treatments on antimicrobial resistance genes in pond water

Huang,

Butaye,

et al. 2024

Front. Microbiol.

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Antimicrobial resistance (AMR) poses a significant global health threat as the silent pandemic. Because of the use of antimicrobials in aquaculture systems, fish farms may be potential reservoirs for the dissemination of antimicrobial resistance genes (ARGs). Treatments with disinfectants have been promoted to reduce the use of antibiotics; however, the effect of these types of treatments on AMR or ARGs is not well known. This study aimed to evaluate the effects of low dose ozone treatments (0.15 mg/L) on ARG dynamics in pond water using metagenomic shotgun sequencing analysis. The results suggested that ozone disinfection can increase the relative abundance of acquired ARGs and intrinsic efflux mediated ARGs found in the resistance nodulation cell division (RND) family. Notably, a co-occurrence of efflux and non-efflux ARGs within the same bacterial genera was also observed, with most of these genera dominating the bacterial population following ozone treatments. These findings suggest that ozone treatments may selectively favor the survival of bacterial genera harboring efflux ARGs, which may also have non-efflux ARGs. This study underscores the importance of considering the potential impacts of disinfection practices on AMR gene dissemination particularly in aquaculture settings where disinfectants are frequently used at low levels. Future endeavors should prioritize the evaluation of these strategies, as they may be associated with an increased risk of AMR in aquatic environments.

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Section: Methodsmentioning

confidence: 99%

Impact of low-dose ozone nanobubble treatments on antimicrobial resistance genes in pond water

Huang,

Butaye,

et al. 2024

Front. Microbiol.

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“…Host-filtered metagenomic samples were assembled using MEGAHIT (Li et al, 2015) and open reading frames (ORFs) in contigs were predicted using MetaGeneMark-2 (Gemayel et al, 2022). The predicted genes from each sample were merged and clustered using CD-HIT (Fu et al, 2012) based on the criteria of identity >95% and coverage > 90% to remove redundant genes.…”

Section: Metagenomic Data Analysismentioning

confidence: 99%

A double-blind, randomized, placebo-controlled study assessing the impact of probiotic supplementation on antibiotic induced changes in the gut microbiome

John,

Michael,

Dabcheva

et al. 2024

Front. Microbiomes

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The human gut microbiome, crucial for health, can be disrupted by antibiotic treatment, leading to various health issues and the rise of antimicrobial resistance (AMR). This study investigates the impact of a probiotic on the gut microbiome’s composition and antimicrobial resistance genes (ARGs) content following antibiotic treatment. Conducted as a single-centre, double-blind, randomized, placebo-controlled trial, adults taking oral antibiotics were allocated into a probiotic or placebo group. Evaluations included viable cell enumeration and shotgun metagenomic sequencing for microbiome analysis, along with ARG assessment. The probiotic maintained the numbers of lactobacilli, significantly increased the Bacteroides population and decreased numbers of enterobacteria. The lactobacilli and enterococci numbers decreased in the placebo. The alpha diversity remained stable in the probiotic group throughout the study, but significant reductions were observed in the placebo group post antibiotic treatment. There was significant spatial separation in beta diversities between groups at the end of the study. Compared to baseline levels, there was a significant reduction in the abundance of ARGs in the probiotic group at the end of the study, while ARG abundance in the placebo group was comparable with baseline levels at the end of the study. Co-occurrence network analysis observed consistent betweenness centrality and node degree within group in the probiotic group whereas scores decreased in the placebo group. This study suggests that the probiotic may minimize the disruption of antibiotic treatment on the gut microbiome by preserving microbial diversity and reducing ARG abundance.

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“…To test the performance of gene prediction tools for large-scale metagenomic predictions, 13 different tools (Supplementary Table 1) were evaluated individually as well as in combinations of two or three. Among these, three tools were designed for eukaryotic sequences [30][31][32] , nine focused on prokaryotes [24][25][26][27][33][34][35][36][37][38][39] , and one for viruses 40 . Both Prodigal, and Pyrodigal 39 (v2.1.0), an actively maintained successor to Prodigal 27 (v2.6.3), were included in the comparisons.…”

Section: Selection Of Optimal Gene Prediction Toolsmentioning

confidence: 99%

Lineage-specific microbial protein prediction enables large-scale exploration of protein ecology within the human gut

Schmitz,

Dimonaco,

Clavel

et al. 2024

Preprint

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Microbes use a range of genetic codes and gene structures, yet these are ignored during metagenomic analysis. This causes spurious protein predictions, preventing functional assignment which limits our understanding of ecosystems. To resolve this, we developed a lineage-specific gene prediction approach that uses the correct genetic code based on the taxonomic assignment of genetic fragments, removes partial predictions, and optimises prediction of small proteins. Applied to 9,634 metagenomes and 3,594 genomes from the human gut, this approach increased the landscape of captured expressed microbial proteins by 78.9%, including previously hidden functional groups. Optimised small protein prediction captured 3,772,658 small protein clusters, many with antimicrobial activity. Integration of the protein sequences and sample metadata into a tool, InvestiGUT, enables association of protein prevalence with host parameters. Accurate prediction of proteins is critical for understanding the functionality of microbiomes, hence this work will enhance understanding mechanistic interactions between microbes and hosts.

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MetaGeneMark-2: Improved Gene Prediction in Metagenomes

Cited by 12 publications

References 29 publications

Impact of low-dose ozone nanobubble treatments on antimicrobial resistance genes in pond water

Impact of low-dose ozone nanobubble treatments on antimicrobial resistance genes in pond water

A double-blind, randomized, placebo-controlled study assessing the impact of probiotic supplementation on antibiotic induced changes in the gut microbiome

Lineage-specific microbial protein prediction enables large-scale exploration of protein ecology within the human gut

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