MiBiOmics: an interactive web application for multi-omics data exploration and integration

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.

[1]  Nathalie Villa-Vialaneix,et al.  Unsupervised multiple kernel learning for heterogeneous data integration , 2017, bioRxiv.

[2]  O. Paliy,et al.  Application of multivariate statistical techniques in microbial ecology , 2016, Molecular ecology.

[3]  M. Inouye,et al.  Mergeomics: multidimensional data integration to identify pathogenic perturbations to biological systems , 2016, BMC Genomics.

[4]  Kim-Anh Lê Cao,et al.  mixOmics: An R package for ‘omics feature selection and multiple data integration , 2017, bioRxiv.

[5]  Luis Pedro Coelho,et al.  Plankton networks driving carbon export in the oligotrophic ocean , 2015, Nature.

[6]  Madhav Thambisetty,et al.  A Multi-network Approach Identifies Protein-Specific Co-expression in Asymptomatic and Symptomatic Alzheimer's Disease. , 2017, Cell systems.

[7]  David Sankoff,et al.  Locating rearrangement events in a phylogeny based on highly fragmented assemblies , 2016, BMC Genomics.

[8]  Enrico Petretto,et al.  Multi-tissue Analysis of Co-expression Networks by Higher-Order Generalized Singular Value Decomposition Identifies Functionally Coherent Transcriptional Modules , 2014, PLoS genetics.

[9]  Ron Wehrens,et al.  The pls Package: Principal Component and Partial Least Squares Regression in R , 2007 .

[10]  Kim-Anh Lê Cao,et al.  DIABLO: an integrative approach for identifying key molecular drivers from multi-omics assays , 2019, Bioinform..

[11]  A. Heintz‐Buschart,et al.  Integrated multi-omics of the human gut microbiome in a case study of familial type 1 diabetes , 2016, Nature Microbiology.

[12]  Aedín C. Culhane,et al.  A multivariate approach to the integration of multi-omics datasets , 2014, BMC Bioinformatics.

[13]  Steven J. M. Jones,et al.  Comprehensive molecular portraits of human breast tumours , 2013 .

[14]  A. Lusis,et al.  Considerations for the design of omics studies , 2017 .

[15]  L. Tran,et al.  Integrated Systems Approach Identifies Genetic Nodes and Networks in Late-Onset Alzheimer’s Disease , 2013, Cell.

[16]  Steven J. M. Jones,et al.  Comprehensive molecular portraits of human breast tumors , 2012, Nature.

[17]  Matthias Becker,et al.  Shiny-Seq: advanced guided transcriptome analysis , 2019, BMC Research Notes.

[18]  Sun Kim,et al.  MONGKIE: an integrated tool for network analysis and visualization for multi-omics data , 2016, Biology Direct.

[19]  Alioune Ngom,et al.  A review on machine learning principles for multi-view biological data integration , 2016, Briefings Bioinform..

[20]  Aleix Prat Aparicio Comprehensive molecular portraits of human breast tumours , 2012 .

[21]  Steve Horvath,et al.  WGCNA: an R package for weighted correlation network analysis , 2008, BMC Bioinformatics.

[22]  Luis Pedro Coelho,et al.  Structure and function of the global ocean microbiome , 2015, Science.

[23]  Hugo Y. K. Lam,et al.  Personal Omics Profiling Reveals Dynamic Molecular and Medical Phenotypes , 2012, Cell.

[24]  P. Dixon VEGAN, a package of R functions for community ecology , 2003 .

[25]  Neil D. Rawlings,et al.  New mini- zincin structures provide a minimal scaffold for members of this metallopeptidase superfamily , 2014, BMC Bioinformatics.

[26]  David S. Wishart,et al.  MetaboAnalyst 4.0: towards more transparent and integrative metabolomics analysis , 2018, Nucleic Acids Res..

[27]  Bernardo J. Clavijo,et al.  Rapid transcriptional plasticity of duplicated gene clusters enables a clonally reproducing aphid to colonise diverse plant species , 2017, Genome Biology.