Fernández-Edreira, DiegoLiñares Blanco, JoséVázquez del Río, PatriciaFernández-Lozano, Carlos2024-04-252024-04-252024Fernández-Edreira, D., Liñares-Blanco, J., Patricia, V., & Fernandez-Lozano, C. (2024). VIBES: A consensus subtyping of the vaginal microbiota reveals novel classification criteria. Computational and Structural Biotechnology Journal, 23, 148-156. https://doi.org/10.1016/j.csbj.2023.11.0502001-0370http://hdl.handle.net/2183/36357Availability of data and materials The bacterial 16S rRNA gene sequences from all cohorts (SRA022855, SRA051298, PRJNA208535, PRJNA797778, and PRJNA302078) can be accessed via their respective accession numbers on the ENA Browser. Code availability The source code to reproduce all the analysis, along with documentation, is available on GitHub: https://github.com/MALL-Machine-Learning-in-Live-Sciences/BV_Microbiome VIBES can be downloaded and installed directly from: https://github.com/MALL-Machine-Learning-in-Live-Sciences/VIBES[Abstract]: This study aimed to develop a robust classification scheme for stratifying patients based on vaginal microbiome. By employing consensus clustering analysis, we identified four distinct clusters using a cohort that includes individuals diagnosed with Bacterial Vaginosis (BV) as well as control participants, each characterized by unique patterns of microbiome species abundances. Notably, the consistent distribution of these clusters was observed across multiple external cohorts, such as SRA022855, SRA051298, PRJNA208535, PRJNA797778, and PRJNA302078 obtained from public repositories, demonstrating the generalizability of our findings. We further trained an elastic net model to predict these clusters, and its performance was evaluated in various external cohorts. Moreover, we developed VIBES, a user-friendly R package that encapsulates the model for convenient implementation and enables easy predictions on new data. Remarkably, we explored the applicability of this new classification scheme in providing valuable insights into disease progression, treatment response, and potential clinical outcomes in BV patients. Specifically, we demonstrated that the combined output of VIBES and VALENCIA scores could effectively predict the response to metronidazole antibiotic treatment in BV patients. Therefore, this study's outcomes contribute to our understanding of BV heterogeneity and lay the groundwork for personalized approaches to BV management and treatment selection.engAtribución 4.0 Internacionalhttp://creativecommons.org/licenses/by/3.0/es/Bacterial vaginosisMicrobiomeMachine learningConsensus clustering analysisTreatment responsejournal articleopen access10.1016/j.csbj.2023.11.050