INetModels 2.0: An interactive visualization and database of multi-omics data
Journal article, 2021
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.
Author
Muhammad Arif
Royal Institute of Technology (KTH)
C. Zhang
Royal Institute of Technology (KTH)
Zhengzhou University
Xiangyu Li
Royal Institute of Technology (KTH)
Cem Güngör
Bash Biotech Inc.
Buǧra Çakmak
Bash Biotech Inc.
Metin Arslantürk
Bash Biotech Inc.
Abdellah Tebani
University of Rouen
CHU Rouen Normandie
Berkay Özcan
Bash Biotech Inc.
Oǧuzhan Subaş
Bash Biotech Inc.
Wenyu Zhou
Stanford University
B. D. Piening
Providence Cancer Center
Hasan Turkez
Atatürk University
L. Fagerberg
Royal Institute of Technology (KTH)
Nathan D. Price
Institute of Molecular Systems Biology
Leroy Hood
Institute of Molecular Systems Biology
Michael P. Snyder
Stanford University
Jens B Nielsen
Chalmers, Biology and Biological Engineering, Systems and Synthetic Biology
Mathias Uhlen
Royal Institute of Technology (KTH)
Adil Mardinoglu
Royal Institute of Technology (KTH)
King's College London
Nucleic Acids Research
0305-1048 (ISSN) 1362-4962 (eISSN)
Vol. 49 W1 W271-W276Areas of Advance
Health Engineering
Subject Categories
Bioinformatics (Computational Biology)
Human Computer Interaction
Bioinformatics and Systems Biology
DOI
10.1093/nar/gkab254
PubMed
33849075