PT  - JOURNAL ARTICLE
AU  - Zoppi, Johanna
AU  - Guillaume, Jean-François
AU  - Neunlist, Michel
AU  - Chaffron, Samuel
TI  - MiBiOmics: An interactive web application for multi-omics data exploration and integration
AID  - 10.1101/2020.04.24.031773
DP  - 2020 Jan 01
TA  - bioRxiv
PG  - 2020.04.24.031773
4099  - http://biorxiv.org/content/early/2020/04/25/2020.04.24.031773.short
4100  - http://biorxiv.org/content/early/2020/04/25/2020.04.24.031773.full
AB  - Background Multi-omics experimental approaches are becoming common practice in biological and medical sciences underlying the need to design new integrative techniques and applications to enable the holistic characterization of biological systems. The integrative analysis of heterogeneous datasets generally allows us to acquire additional insights and generate novel hypotheses about a given biological system. However, it can often become challenging given the large size of omics datasets and the diversity of existing techniques. Moreover, visualization tools for interpretation are usually non-accessible to biologists without programming skills.Results Here, we present MiBiOmics, a web-based and standalone application that facilitates multi-omics data visualization, exploration, integration, and analysis by providing easy access to dedicated and interactive protocols. It implements advanced ordination techniques and the inference of omics-based (multi-layer) networks to mine complex biological systems, and identify robust biomarkers linked to specific contextual parameters or biological states.Conclusions Through an intuitive and interactive interface, MiBiOmics provides easy-access to ordination techniques and to a network-based approach for integrative multi-omics analyses. MiBiOmics is currently available as a Shiny app at https://shiny-bird.univ-nantes.fr/app/Mibiomics and as a standalone application at https://gitlab.univ-nantes.fr/combi-ls2n/mibiomics.Competing Interest StatementThe authors have declared no competing interest.ASVAmplicon Sequence Variant.AUCArea Under the Curve.DIABLOData Integration Analysis for Biomarker discovery using Latent variable approaches for ‘Omics studies.PCAPrincipal Component Analysis.PCoAPrincipal COordinates Analysis.OPLSOrthogonal Partial Least Square.OPLS-DAOrthogonal Partial Least Square Discriminant Analysis.OTUOperational Taxonomic Unit.VIPVariable Importance Projection.WGCNAWeighted Gene Correlation Network Analysis.