CoNCRAtlas
CoNCRAtlas provides a high-confidence catalog of long non-coding RNAs (lncRNAs) and microRNAs (miRNAs) in Gossypium hirsutum and Gossypium barbadense to support transcriptome-based functional and evolutionary studies.
Key Features:
- High-confidence lncRNA and miRNA collection: A curated set of lncRNAs and miRNAs identified from large-scale RNA sequencing and small RNA sequencing datasets.
- Data sources: Derived explicitly from RNA sequencing (RNA-seq) and small RNA sequencing (small RNA-seq) datasets.
- Expression patterns: Extensive expression pattern data obtained from transcriptome analyses across thousands of samples.
- Multi-omics annotations: Integration of multi-omics annotations to enrich functional context.
- Contextual cataloging: Systematic identification and annotation across tissues, developmental stages, and biological conditions.
- Species focus: Specific coverage of Gossypium hirsutum and Gossypium barbadense.
Scientific Applications:
- Evolutionary studies: Enable comparative and evolutionary analyses of ncRNAs between cotton species.
- Functional characterization: Support functional studies of lncRNAs and miRNAs via expression patterns across tissues and conditions.
- Breeding and trait research: Inform breeding-program–relevant investigations by linking ncRNA expression to developmental stages and biological contexts.
Methodology:
lncRNAs and miRNAs were identified from large-scale RNA sequencing (RNA-seq) and small RNA sequencing (small RNA-seq) datasets, with expression profiles obtained from transcriptome analyses across thousands of samples and annotations integrated from multi-omics data.
Topics
Details
- Tool Type:
- web application
- Operating Systems:
- Mac, Linux, Windows
- Programming Languages:
- Shell
- Added:
- 3/6/2024
- Last Updated:
- 11/24/2024
Operations
Publications
Singh A, AT V, Gupta K, Sharma S, Kumar S. Long non-coding RNA and microRNA landscape of two major domesticated cotton species. Computational and Structural Biotechnology Journal. 2023;21:3032-3044. doi:10.1016/j.csbj.2023.05.011. PMID:37266406. PMCID:PMC10229759.