Functional identification of cis-regulatory long noncoding RNAs at controlled false discovery rates

Area of Science:

• Genomics
• Molecular Biology
• Bioinformatics

Background:

• Long noncoding RNAs (lncRNAs) can regulate neighboring genes in cis, but current identification methods are unreliable.
• A lack of accurate cis-lncRNA catalogues impedes research into their roles and mechanisms.

Purpose of the Study:

• To develop and validate TransCistor, a novel computational framework for robustly defining and identifying cis-lncRNAs.
• To conduct the first large-scale survey of cis-lncRNAs in humans and mice.

Main Methods:

• Developed TransCistor, a framework using statistical models to identify cis-lncRNAs based on target gene enrichment.
• Performed transcriptome-wide perturbation experiments on 268 human and 134 mouse lncRNAs.
• Validated predictions across different analysis methods, perturbation types, and independent experiments.

Main Results:

• TransCistor accurately identified known cis-lncRNAs (e.g., XIST, LINC00240, UMLILO).
• A minority of lncRNAs exhibit cis-activity, with activators being more common than repressors.
• cis-lncRNA transcripts associate with target genes and are weakly enriched with enhancer elements.

Conclusions:

• TransCistor provides a quantitative and reliable foundation for cis-lncRNA identification.
• This framework facilitates further research into the molecular mechanisms and biological significance of cis-lncRNAs.