2022 Fiscal Year Annual Research Report
Comprehensive optimization of cell type-specific gene co-expression networks and construction of a cell type-specific co-expression database
| Project/Area Number |
20K06609
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| Research Institution | Kyoto University |
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Principal Investigator |
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| Project Period (FY) |
2020-04-01 – 2023-03-31
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| Keywords | gene expression / gene co-expression / data normalization / database / batch effects / RNA-seq / network analysis |
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| Outline of Annual Research Achievements |
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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| Remarks |
We are preparing a gene co-expression database using our data, but the public version has not yet been completed, and the URL has not yet been decided. We hope to make it public as soon as possible.
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