Biology subjects

Moyne, O.

Publications and source records attributed to Moyne, O..

4 recordsLinked to original sources

bioRxiv2026

A Multi-Omics Processing Pipeline (MOPP) for Extracting Taxonomic and Functional Insights from Metaribosome Profiling (metaRibo-Seq) data

Metaribosome profiling (metaRibo-Seq) enables genome-wide measurement of translation across complex microbial communities by sequencing ribosome-protected mRNA fragments, but the short length of these footprints creates substantial nonspecific mapping against large reference genome collections, leading to spurious taxonomic and functional assignments. Here we present MOPP (Multi-Omics Processing Pipeline), a modular reference-based workflow that denoises metaRibo-Seq data by leveraging matched metagenomic coverage breadth to identify genomes likely to be truly present in a sample before aligning metatranslatomic and optional metatranscriptomic reads. MOPP generates taxon-by-gene count tables across genomic, transcriptional and translational layers, enabling integrated downstream analyses of microbial function. We evaluated MOPP using a defined 79-member synthetic human gut community profiled by metagenomics and metaRibo-Seq. Coverage breadth filtering markedly improved detection accuracy relative to a standard baseline workflow, with performance remaining robust across a broad intermediate threshold range and peaking at 92-95% coverage breadth. At a 92% threshold, MOPP reduced the number of distinct detected operational genomic units by 99.4% while retaining 87.8% of aligned metaRibo-Seq reads on average, and increased the F1 score from 0.02 to 0.61. Residual false positives were predominantly attributable to genomes with extremely high nucleotide similarity to true community members, whereas false negatives were enriched among low-abundance taxa, indicating that remaining errors are driven primarily by biological similarity and detection limits rather than widespread nonspecific mapping. Together, these results establish MOPP as a high-throughput workflow for robust processing of metaRibo-Seq in the context of matched metagenomics and position it as a scalable framework for integrated taxonomic and functional analysis of microbial communities across genomic, transcriptional and translational layers.

bioinformatics↗

bioRxiv2023

Guild and Niche Determination Enable Targeted Alteration of the Microbiome

Microbiome science has greatly contributed to our understanding of microbial life and its essential roles for the environment and human health1-5. However, the nature of microbial interactions and how microbial communities respond to perturbations remains poorly understood, resulting in an often descriptive and correlation-based approach to microbiome research6-8. Achieving causal and predictive microbiome science would require direct functional measurements in complex communities to better understand the metabolic role of each member and its interactions with others. In this study we present a new approach that integrates transcription and translation measurements to predict competition and substrate preferences within microbial communities, consequently enabling the selective manipulation of the microbiome. By performing metatranscriptomic (metaRNA-Seq) and metatranslatomic (metaRibo-Seq) analysis in complex samples, we classified microbes into functional groups (i.e. guilds) and demonstrated that members of the same guild are competitors. Furthermore, we predicted preferred substrates based on importer proteins, which specifically benefited selected microbes in the community (i.e. their niche) and simultaneously impaired their competitors. We demonstrated the scalability of microbial guild and niche determination to natural samples and its ability to successfully manipulate microorganisms in complex microbiomes. Thus, the approach enhances the design of pre- and probiotic interventions to selectively alter members within microbial communities, advances our understanding of microbial interactions, and paves the way for establishing causality in microbiome science.

microbiology↗

bioRxiv2020

Nucleoid openness profiling links bacterial genome structure to phenotype

Gene expression requires specific structural alternations in the nucleoid structure to enable the access of the transcription machinery into the genomic DNA. In prokaryotes, DNA binding proteins, including nucleoid-associated proteins (NAPs) and transcription factors (TFs), drive the change in structure and gene expression. Currently, studies of global NAP and TF binding are often hindered by the lack of appropriate epigenomic tools. Here, we present POP-seq, a method that provides in vivo genome-wide openness profiles of the bacterial nucleoid. We demonstrate that POP-seq can be used to map the global in vivo protein-DNA binding events. Our results highlight a negative correlation between genome openness, compaction and transcription, suggesting that regions that are not accessible to Tn5 transposase are either too compacted or occupied by RNA polymerase. Importantly, we also show that the least open regions are enriched in housekeeping genes, while the most open regions are significantly enriched in genes important for fast adaptation to changing environment. Finally, we demonstrated that the genome openness profile is growth condition specific. Together, those results suggest a model where one can distinguish two types of epigenetic control: one stable, long-term silencing of highly compacted regions, and a second, highly responsive regulation through the dynamic competition between NAPs and RNA polymerase binding. Overall, POP-seq captures structural changes in the prokaryotic chromatin and provides condition-specific maps of global protein-DNA binding events, thus linking overall transcriptional and epigenetic regulation directly to phenotype.

microbiology↗

bioRxiv2019

Feature-based Molecular Networking in the GNPS Analysis Environment

Molecular networking has become a key method used to visualize and annotate the chemical space in non-targeted mass spectrometry-based experiments. However, distinguishing isomeric compounds and quantitative interpretation are currently limited. Therefore, we created Feature-based Molecular Networking (FBMN) as a new analysis method in the Global Natural Products Social Molecular Networking (GNPS) infrastructure. FBMN leverages feature detection and alignment tools to enhance quantitative analyses and isomer distinction, including from ion-mobility spectrometry experiments, in molecular networks.

bioinformatics↗