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Open AccessArticle

Meta-Analysis of Hypoxic Transcriptomes from Public Databases

by Hidemasa Bono 1,* and Kiichi Hirota 2,*
1
Database Center for Life Science (DBCLS), Joint Support-Center for Data Science Research, Research Organization of Information and Systems, 1111 Yata, Mishima, Shizuoka 411-8540, Japan
2
Department of Human Stress Response Science, Institute of Biomedical Science, Kansai Medical University, Hirakata 573-1010, Japan
*
Authors to whom correspondence should be addressed.
Biomedicines 2020, 8(1), 10; https://doi.org/10.3390/biomedicines8010010
Received: 30 November 2019 / Revised: 26 December 2019 / Accepted: 8 January 2020 / Published: 9 January 2020
(This article belongs to the Special Issue Hypoxia-Inducible Factors: Regulation and Therapeutic Potential)
Hypoxia is the insufficiency of oxygen in the cell, and hypoxia-inducible factors (HIFs) are central regulators of oxygen homeostasis. In order to obtain functional insights into the hypoxic response in a data-driven way, we attempted a meta-analysis of the RNA-seq data from the hypoxic transcriptomes archived in public databases. In view of methodological variability of archived data in the databases, we first manually curated RNA-seq data from appropriate pairs of transcriptomes before and after hypoxic stress. These included 128 human and 52 murine transcriptome pairs. We classified the results of experiments for each gene into three categories: upregulated, downregulated, and unchanged. Hypoxic transcriptomes were then compared between humans and mice to identify common hypoxia-responsive genes. In addition, meta-analyzed hypoxic transcriptome data were integrated with public ChIP-seq data on the known human HIFs, HIF-1 and HIF-2, to provide insights into hypoxia-responsive pathways involving direct transcription factor binding. This study provides a useful resource for hypoxia research. It also demonstrates the potential of a meta-analysis approach to public gene expression databases for selecting candidate genes from gene expression profiles generated under various experimental conditions. View Full-Text
Keywords: hypoxia; transcriptome; RNA-seq; ChIP-seq; public database; meta-analysis hypoxia; transcriptome; RNA-seq; ChIP-seq; public database; meta-analysis
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Bono, H.; Hirota, K. Meta-Analysis of Hypoxic Transcriptomes from Public Databases. Biomedicines 2020, 8, 10.

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