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.

Links