RT Journal Article
SR Electronic
T1 MiBiOmics: An interactive web application for multi-omics data exploration and integration
JF bioRxiv
FD Cold Spring Harbor Laboratory
SP 2020.04.24.031773
DO 10.1101/2020.04.24.031773
A1 Zoppi, Johanna
A1 Guillaume, Jean-François
A1 Neunlist, Michel
A1 Chaffron, Samuel
YR 2020
UL http://biorxiv.org/content/early/2020/04/25/2020.04.24.031773.abstract
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.