GraphOmics: an interactive platform to explore and integrate multi-omics data

Wandy, J. and Daly, R. (2021) GraphOmics: an interactive platform to explore and integrate multi-omics data. BMC Bioinformatics, 22, 603. (doi: 10.1186/s12859-021-04500-1) (PMID:34922446) (PMCID:PMC8684259)

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Abstract

Background: An increasing number of studies now produce multiple omics measurements that require using sophisticated computational methods for analysis. While each omics data can be examined separately, jointly integrating multiple omics data allows for deeper understanding and insights to be gained from the study. In particular, data integration can be performed horizontally, where biological entities from multiple omics measurements are mapped to common reactions and pathways. However, data integration remains a challenge due to the complexity of the data and the difficulty in interpreting analysis results. Results: Here we present GraphOmics, a user-friendly platform to explore and integrate multiple omics datasets and support hypothesis generation. Users can upload transcriptomics, proteomics and metabolomics data to GraphOmics. Relevant entities are connected based on their biochemical relationships, and mapped to reactions and pathways from Reactome. From the Data Browser in GraphOmics, mapped entities and pathways can be ranked, sorted and filtered according to their statistical significance (p values) and fold changes. Context-sensitive panels provide information on the currently selected entities, while interactive heatmaps and clustering functionalities are also available. As a case study, we demonstrated how GraphOmics was used to interactively explore multi-omics data and support hypothesis generation using two complex datasets from existing Zebrafish regeneration and Covid-19 human studies. Conclusions: GraphOmics is fully open-sourced and freely accessible from https://graphomics.glasgowcompbio.org/. It can be used to integrate multiple omics data horizontally by mapping entities across omics to reactions and pathways. Our demonstration showed that by using interactive explorations from GraphOmics, interesting insights and biological hypotheses could be rapidly revealed.

Item Type:Articles
Status:Published
Refereed:Yes
Glasgow Author(s) Enlighten ID:Daly, Dr Ronan and Wandy, Dr Joe
Authors: Wandy, J., and Daly, R.
College/School:College of Medical Veterinary and Life Sciences > Institute of Cancer Sciences
Journal Name:BMC Bioinformatics
Publisher:BioMed Central
ISSN:1471-2105
ISSN (Online):1471-2105
Copyright Holders:Copyright © 2021 The Authors
First Published:First published in BMC Bioinformatics 22: 603
Publisher Policy:Reproduced under a Creative Commons License
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Project CodeAward NoProject NamePrincipal InvestigatorFunder's NameFunder RefLead Dept
173707Institutional Strategic Support Fund (2016)Anna DominiczakWellcome Trust (WELLCOTR)204820/Z/16/ZInstitute of Cardiovascular & Medical Sciences