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Moles, L.

Publications and source records attributed to Moles, L..

2 recordsLinked to original sources

MBIOME: A comprehensive, reproducible, and open-source workflow for amplicon-based microbiome data analysis.

Microbiome analysis has become a pivotal tool in understanding the role of microbiota in human health and disease. However, the lack of standardized workflows, together with the limitations of proprietary software solutions, hampers reproducibility and flexibility. Here, we present Mbiome, an open-source, user-friendly and automated workflow designed to streamline amplicon-based microbiome analysis. Built upon QIIME2, Mbiome supports both bacterial (16S rRNA) and fungal (ITS) profiling, and is compatible with raw fastq files generated by Ion Torrent (IT) and Illumina (IL) sequencing platforms. The workflow guides users through an interactive setup process via a simple configuration file, enabling researchers with minimal bioinformatics experience to perform comprehensive analyses without writing code. Once configured, Mbiome automates major steps including quality control, taxonomic assignment, and {beta}-diversity analyses, functional predictions (via q2-metnet), and customizable visualizations and statistical analyses. Mbiome has been validated using real-world datasets from multiple sclerosis research projects, performing a comparison between different microbiome analysis approaches, including 16S hypervariable region reconstruction, amplicon-based strategies, and cross-platform sequencing (IT and IL), as well as against results obtained with Ion Reporter (IR) commercial software. This evaluation demonstrated its versatility and effectiveness across different sequencing platforms. Moreover, Mbiome provided improved flexibility, transparency, and taxonomic resolution compared to IR. By combining accessibility, reproducibility, and cross-platform compatibility, Mbiome lowers the barrier to microbiome data analysis and facilitates high-quality, standardized workflows in both research and applied settings. Mbiome is publicly available at https://github.com/MGorostidi/mbiome.

bioinformatics↗

A proposed workflow to analyze bacterial transcripts in RNAseq from blood extracellular vesicles of people with Multiple Sclerosis

MotivationThe taxonomical characterisation of bacterial species derived from genetic material blood, including reads derived from bacterial extracellular vesicles (bEVs) poses certain challenges, such as the proper discrimination of "true" reads from contaminating reads. This is a common issue in taxa profiling and can lead to the false discovery of species that are present in the sample. To avoid such biases a careful approximation to taxa profiling is necessary. ResultsIn this work we propose a workflow to analyze the presence of bacterial transcripts as indicative of putative bEVs circulating in the blood of people with MS (pwMS). The workflow includes several reference mapping steps against the host genome and a consensus selection of genera based on different taxa profilers. The consensus selection is performed with a flagging system that removes species with low abundance or with high variation across profilers. Additionally, the inclusion of biological samples from known cultured species as well as the generation of artificial reads constitute two key aspects of this workflow. AvailabilityThe workflow is available at the following repository: https://github.com/NanoNeuro/EV_taxprofiling. Contactdavidangel.otaeguibichot@osakidetza.eus

bioinformatics↗