Rice Expression Database (RED) 2.0: an updated rice database integrating diverse RNA expression profiles based on the telomere-to-telomere reference genome

Clicks: 17
ID: 337394
2026
Article Quality & Performance Metrics
Overall Quality
Not rated
Combines reader engagement with the AI quality analysis. This article has not been analysed, so there is no overall score — reader engagement is measured and shown alongside.
AI Quality Assessment
Not analyzed
Readership in this journal
Emerging

Ranked #1,064 of 1,295 articles by views in Nucleic Acids Research

Most read Least read

Bar heights use a square-root scale. Only the 120 most-read articles are drawn; the journal has 1,295 in total.

Mint this article as an NFT
Not yet minted

Create a permanent, verifiable on-chain record of this article on the Scimatic Network. The NFT is held in your Journament account, and you can withdraw it to your own wallet at any time.

5 SUSD one-off · no wallet required
Abstract
Rice Expression Database (RED) is a rice gene expression database derived from large-scale RNA-seq data across different tissues and experimental conditions. Here, we present RED 2.0 (https://ngdc.cncb.ac.cn/red) with significant updates and enhancements. Specifically, RED 2.0 contains 357 projects covering 6471 bulk RNA-seq experiments across 18 tissues and 78 single-cell RNA-seq experiments comprising 984 495 cells. All projects are further manually curated and grouped into diverse biological contexts, including abiotic stress, biotic stress, hormone, development, genotype, and anatomy. Notably, based on the telomere-to-telomere assembly of the Nipponbare genome, RED 2.0 incorporates comprehensive expression profiles for 57 359 protein-coding genes (67 818 transcripts), 16 522 long non-coding RNA genes (47 396 transcripts), and 79 007 circular RNAs. In addition, it is equipped with online tools for differential expression analysis, functional enrichment analysis, co-expression network construction, and genome browser. Collectively, RED 2.0 provides a comprehensive expression landscape for both coding and non-coding genes across diverse conditions, serving as an important resource for rice functional genomics and molecular breeding.
Reference Key
openalex_W7223935987 Use this key to autocite in the manuscript while using SciMatic Manuscript Manager or Thesis Manager
Authors
Journal Nucleic Acids Research
Year 2026
DOI
10.1093/nar/gkag1064
URL
Keywords Keywords not found

Citations

No citations found. To add a citation, contact the admin at info@scimatic.org

No comments yet. Be the first to comment on this article.