NONCODE v7.0: updated lncRNA resource integrating scRNA-seq data encompassing immune baseline, development, and...

Area of Science:

• Genomics and Bioinformatics
• Molecular Biology
• Computational Biology

Background:

• Single-cell RNA sequencing (scRNA-seq) advances gene expression heterogeneity studies.
• Existing databases lack comprehensive long noncoding RNA (lncRNA) data across diverse biological contexts.
• Systematic analysis of lncRNA expression requires integrated single-cell annotations.

Purpose of the Study:

• To update the NONCODE database with scRNA-seq data for systematic lncRNA expression analysis.
• To create a foundational resource for investigating lncRNA functions in different cell types and disease states.
• To provide enhanced functional modules for data retrieval, visualization, and comparative analysis.

Main Methods:

• Integration of 2061 human scRNA-seq samples from 229 datasets into NONCODE v7.0.
• Standardized quality control and processing of approximately 14 million single cells.
• Annotation of 94,843 lncRNAs, covering 98.5% of those in NONCODE v6.0.
• Development of single-cell-tailored functional modules for data analysis and visualization.

Main Results:

• NONCODE v7.0 incorporates extensive scRNA-seq data across physiological and pathological conditions.
• The database annotates a vast number of lncRNAs within a large, high-quality single-cell population.
• Enhanced analytical modules enable identification of cell-type-specific differentially expressed lncRNAs.

Conclusions:

• NONCODE v7.0 serves as a crucial resource for single-cell lncRNA research.
• The integrated data and analytical tools facilitate the study of lncRNA roles in health and disease.
• This platform supports downstream functional investigations of lncRNAs at the single-cell level.