A Multiomics Graph Database System for Biological Data Integration and Cancer Informatics

Ishwor Thapa, Hesham Ali

Research output: Contribution to journalArticlepeer-review


The multiomics data are heterogeneous and come from different biological levels such as epigenetics, genomics, transcriptomics and proteomics. The development of high-throughput technologies has enabled researchers not only to study all the entities together but also to utilize information from different levels spanning DNA methylation, copy number variation (CNV), mutation, gene expression, and miRNA expression. With the recent advancement in image informatics, the field of radiomics is rapidly emerging. It can be expected that the information from microscopic images of the tissue will soon be part of many multiomics studies. Meanwhile, integration of different kinds of multiomics data to extract relevant biological information is currently a big challenge. This study is our ongoing effort to develop a model that properly integrates multiomics data and allows easy retrieval of information relevant to biological processes. In this article, we have enriched our previous graph database model to store gene expression, miRNA expression, DNA methylation, mutation, CNV, clinical data, including information of the image of tissue slides. To show that the model is working, we used data from the Cancer Genome Atlas for three cancer types.

Original languageEnglish (US)
Pages (from-to)209-219
Number of pages11
JournalJournal of Computational Biology
Issue number2
StatePublished - Feb 2021


  • cancer informatics
  • data integration
  • graph database
  • multiomics

ASJC Scopus subject areas

  • Modeling and Simulation
  • Molecular Biology
  • Genetics
  • Computational Mathematics
  • Computational Theory and Mathematics


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