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Database (Oxford). 2019 Jan 1;2019. doi: 10.1093/database/bay145.

Restructured GEO: restructuring Gene Expression Omnibus metadata for genome dynamics analysis.

Author information

1
School of Biomedical Informatics, University of Texas Health Science Center at Houston (UTHealth), Houston, Texas, USA.
2
Universidad Antonio Nariño, Bogotá, Colombia.
3
Department of Biostatistics & Data Science, School of Public Health, University of Texas Health Science Center at Houston (UTHealth), Houston, Texas, USA.
4
Department of Biostatistics and Computational Biology, School of Medicine and Dentistry, University of Rochester, Rochester, New York, USA.
5
TechWave International. Inc., Houston, Texas, USA.

Abstract

Motivation:

Gene Expression Omnibus (GEO) and other publicly available data store their metadata in the format of unstructured English text, which is very difficult for automated reuse.

Results:

We employed text mining techniques to analyze the metadata of GEO and developed Restructured GEO database (ReGEO). ReGEO reorganizes and categorizes GEO series and makes them searchable by two new attributes extracted automatically from each series' metadata. These attributes are the number of time points tested in the experiment and the disease being investigated. ReGEO also makes series searchable by other attributes available in GEO, such as platform organism, experiment type, associated PubMed ID as well as general keywords in the study's description. Our approach greatly expands the usability of GEO data, demonstrating a credible approach to improve the utility of vast amount of publicly available data in the era of Big Data research.

PMID:
30649296
PMCID:
PMC6333964
DOI:
10.1093/database/bay145
[Indexed for MEDLINE]
Free PMC Article

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