Abstract
The deployment of various high-throughput instruments in molecular biology has produced a large of volume of heterogeneous experimental omics data . It is of paramount importance to collect and organize different types of those data in a unified manner so as to disseminate those valuable data to the researchers of interest. One of the essential components in such data organization and dissemination is to maintain appropriate metadata which help understand collected data. The metadata should be defined sufficient enough to provide necessary information for the researchers who want to use them. This paper presents a metadata modeling method tailored to experimental omics data which are generated for specific biological specimens. The method first identifies the candidate keywords by analyzing the literature and then they are compared with items used in other biological metadata. The candidate keywords are examined and organized into categories so that the organized keywords provide a unified view of metadata for heterogeneous collection of experimental omics data. The metadata modeling results for experimental omics data of human tissues are expressed in database schema. The proposed method has been successfully applied to metadata modeling for experimental omics data in the Biobank of Korea Centers of Disease Control and Prevention.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
References
The European Bioinformatics Institute, https://www.ebi.ac.uk/
MIGen, http://www.mibbi.org/inde x.php/ projects/MIGen/
Minimum Information about a Phylogenetic Analysis, http://www.mibbi.org/index.php/projects/MIAPA
Snyder M, Mias G, Stanberry L, Kolker E: Metadata Checklist for the Integrated Personal Omics Study: Proteomics and Metabolomics Experiments: Vol 4, 202–206, Big Data, (2013)
National Center for Biotechnology Information, http://www.ncbi.nlm.nih.gov
The Protein Information Resource, http://pir.georgetown.edu/
The Protein Data Bank archive, https://www.wwpdb.org/
Eugene Kolker, Roger Higdon, Winston Haynes, Dean Welch, William Broomall, Doron Lancet, Larissa Stanberry, and Natali Kolke: MOPED: Model Organism Protein Expression Database: Vol. 40, Nucleic Acids Research, (2012)
Gene Expression Omnibus, https://www.ncbi.nlm.nih.gov/geo/
Sequence Read Archive Information, https://www.ncbi.nlm.nih.gov/sra
Biobank Information Management System, http://is.cdc.go.kr/
The Cancer Genome Atlas (TCGA), https://cancergenome.nih.gov/
The database of Genotypes and Phenotypes, https://www.ncbi.nlm.nih.gov/gap
Database of Genomic Variants archive, https://www.ebi.ac.uk/dgva
T. J. Pugh, A. D. Delaney, N. Farnoud1, S. Flibotte, M. Griffith, H. I. Li1, H. Qian, P. Farinha, R. D. Gascoyne and M. A. Marra: Impact of whole genome amplification on analysis of copy number variants: Vol. 36, No. 13, Nucleic Acids Research (2008)
Ingeborg B, Anette SB Wolff, Kristian L, Per M Knappskog and Eystein S Husebye : Genome-wide copy number variation (CNV) in patients with autoimmune Addison’s disease: BioMed Central, (2011)
Shanker Swaminathan, Matthew J. Huentelman, Jason J. Corneveaux, Amanda J. Myers, Kelley M. Faber, Tatiana Foroud1, Richard Mayeux, Li Shen, Sungeun Kim, Mari Turk, John Hardy, Eric M. Reiman, Andrew J. Saykin: Analysis of copy number variation in Alzheimer’s disease: the NIALOAD/ NCRAD Family Study: Vol. 9(7):801-14, pubMed, (2012)
Aparna Prasad, Daniele Merico, Bhooma Thiruvahindrapuram, John Wei, Anath C. Lionel, Daisuke Sato, Jessica Rickaby, Chao Lu, Peter Szatmari, Wendy Roberts, Bridget A. Fernandez, Christian R. Marshall, Eli Hatchwell, Peggy S. Eis, and Stephen W. Scherer: A Discovery Resource of Rare Copy Number Variations in Individuals with Autism Spectrum Disorder: Vol. 2 (12), G3.Genes|Genomes|Genetics, (2012)
X Zheng, FY Demirci, MM Barmada, GA Richardson, OL Lopez, RA Sweet, MI Kamboh and E Feingold: Genome-wide copy-number variation study of psychosis in Alzheimer’s disease: Transl Psychiatry, (2015)
Acknowledgments
This work was supported by the Research Program funded by the Korea Centers for Disease Control and Prevention (Grant No.: 11-1352173-000267-01) and by Next-Generation Information Computing Development Program through the National Research Foundation (NRF) of Korea (Grant No.: NRF-2017M3C4A7069432), by the MSIT (Ministry of Science and ICT), Korea.
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2019 Springer Nature Singapore Pte Ltd.
About this paper
Cite this paper
Hwang, K.S., Lee, K.M. (2019). Keyword-Based Metadata Modeling for Experimental Omics Data Dissemination. In: Lee, W., Leung, C. (eds) Big Data Applications and Services 2017. BIGDAS 2017. Advances in Intelligent Systems and Computing, vol 770. Springer, Singapore. https://doi.org/10.1007/978-981-13-0695-2_15
Download citation
DOI: https://doi.org/10.1007/978-981-13-0695-2_15
Published:
Publisher Name: Springer, Singapore
Print ISBN: 978-981-13-0694-5
Online ISBN: 978-981-13-0695-2
eBook Packages: Intelligent Technologies and RoboticsIntelligent Technologies and Robotics (R0)