| 研究実績の概要 |
In the previous 2 years, we optimized data processing steps for obtaining high-quality RNA-seq data for 68 human and 76 mouse cell types and tissues (Vandenbon, PLoS ONE, 2022). This year, I focused on constructing a cell type-specific gene co-expression database, which can be used for the generation of new hypotheses related to gene regulation.
The data was processed into relational databases that store 1) background information of genes, 2) gene expression data, 3) gene co-expression data, 4) predicted transcription factor binding sites in the promoter regions of genes, and 5) annotation data about the function of genes. Functions have been implemented that will allow users to browse the database, including visualizing the expression patterns of a gene over all tissues, showing gene co-expression networks, and detecting shared functional annotations and DNA motifs in the promoters of genes that have similar patterns of expression. These functions have been implemented using the Flask framework in Python. However, the public version has not yet been completed. I will make it accessible as soon as it is ready.
In addition, we also used the optimized gene expression data generated by this project as a reference dataset for the analysis of gene expression patterns in mouse liver tissue, using single-cell and spatial transcriptomics data (Vandenbon et al., Commun. Biol. 2023). Finally, we used this dataset for updating a method for predicting differentially expressed genes (Vandenbon and Diez, bioRxiv, 2022).
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