TY - JOUR
T1 - A Multiomics Graph Database System for Biological Data Integration and Cancer Informatics
AU - Thapa, Ishwor
AU - Ali, Hesham
N1 - Funding Information:
This work was supported by the Nebraska Research Initiative (NRI) System Science Grant.
Publisher Copyright:
© Copyright 2020, Mary Ann Liebert, Inc., publishers 2020.
PY - 2021/2
Y1 - 2021/2
N2 - 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.
AB - 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.
KW - cancer informatics
KW - data integration
KW - graph database
KW - multiomics
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U2 - 10.1089/cmb.2020.0231
DO - 10.1089/cmb.2020.0231
M3 - Article
C2 - 32783648
AN - SCOPUS:85100603295
VL - 28
SP - 209
EP - 219
JO - Journal of Computational Biology
JF - Journal of Computational Biology
SN - 1066-5277
IS - 2
ER -