COXPRESdb in 2015: coexpression database for animal species by DNA-microarray and RNAseq-based expression data with multiple quality assessment systems.
paper
Cited
Public
- Authors
- Okamura, Yasunobu; Aoki, Yuichi; Obayashi, Takeshi; Tadaka, Shu; Ito, Satoshi; Narise, Takafumi; Kinoshita, Kengo
- Year
- 2015
- Journal
- Nucleic acids research
- PMID
- 25392420
- DOI
- 10.1093/nar/gku1163
- PMCID
- PMC4383961
The COXPRESdb (http://coxpresdb.jp) provides gene coexpression relationships for animal species. Here, we report the updates of the database, mainly focusing on the following two points. For the first point, we added RNAseq-based gene coexpression data for three species (human, mouse and fly), and largely increased the number of microarray experiments to nine species. The increase of the number of expression data with multiple platforms could enhance the reliability of coexpression data. For the second point, we refined the data assessment procedures, for each coexpressed gene list and for the total performance of a platform. The assessment of coexpressed gene list now uses more reasonable P-values derived from platform-specific null distribution. These developments greatly reduced pseudo-predictions for directly associated genes, thus expanding the reliability of coexpression data to design new experiments and to discuss experimental results.
Number of guide genes for each supportability level. Supportability levels are represented as stars, where no star is the lowest and a triple star is the highest. Numbers in the bars indicate the percentage of each supportability level in each platform. Genes without any reference genes in the other platforms are shown as a blank box.
LLM interpretation
This is a stacked horizontal bar chart showing the number of guide genes across various biological platforms, categorized by supportability levels (represented by stars and corresponding p-values). The x-axis represents the number of genes, while the y-axis lists the platforms (e.g., *H. sapiens*, *M. musculus*). The bars are color-coded by supportability, with percentages indicated within each segment, and blank boxes representing genes without orthologs in other platforms.
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