INetModels 2.0: an interactive visualization and database of multi-omics data.

Muhammad Arif,Metin Arslantürk,Michael Snyder,Brian Piening,Abdellah Tebani,Berkay Özcan,Nathan Price,Mathias Uhlen,Leroy Hood,Buğra Çakmak,Hasan Turkez,Cem Güngör,Oğuzhan Subaş,Xiangyu Li,Adil Mardinoglu,Jens Nielsen,Cheng Zhang,Wenyu Zhou,Linn Fagerberg

doi:10.1093/nar/gkab254

Abstract

It is essential to reveal the associations between various omics data for a comprehensive understanding of the altered biological process in human wellness and disease. To date, very few studies have focused on collecting and exhibiting multi-omics associations in a single database. Here, we present iNetModels, an interactive database and visualization platform of Multi-Omics Biological Networks (MOBNs). This platform describes the associations between the clinical chemistry, anthropometric parameters, plasma proteomics, plasma metabolomics, as well as metagenomics for oral and gut microbiome obtained from the same individuals. Moreover, iNetModels includes tissue- and cancer-specific Gene Co-expression Networks (GCNs) for exploring the connections between the specific genes. This platform allows the user to interactively explore a single feature's association with other omics data and customize its particular context (e.g. male/female specific). The users can also register their data for sharing and visualization of the MOBNs and GCNs. Moreover, iNetModels allows users who do not have a bioinformatics background to facilitate human wellness and disease research. iNetModels can be accessed freely at https://inetmodels.com without any limitation.

Highlights

During the past decade, the development of highthroughput technologies has dramatically decreased the cost of generating large-scale multi-omics datasets [1]
The data in iNetModels are obtained from recent studies, where large-scale MultiOmics Biological Networks (MOBNs) analyses have been performed for individuals with different metabolic conditions
We provided combined metabolic activators (CMA) to 10 subjects involved in the trial and collected plasma samples during the day to generated proteomics and metabolomics data

Summary

Introduction

The development of highthroughput technologies has dramatically decreased the cost of generating large-scale multi-omics datasets [1]. The data in iNetModels are obtained from recent studies, where large-scale MultiOmics Biological Networks (MOBNs) analyses have been performed for individuals with different metabolic conditions. We retrieved data from The GenotypeTissue Expression (GTEx) Project and The Cancer Genome Atlas (TCGA), created normal tissue- and cancer-specific Gene Co-expression Networks (GCNs) and presented the networks in the iNetModels (Figure 1A).

Results

Conclusion