Integrative classification of human coding and noncoding genes through RNA metabolism profiles

Area of Science:

• Molecular Biology
• Genomics
• RNA Biology

Background:

• The human genome produces numerous long noncoding RNAs (lncRNAs), but distinguishing functional ones from transcriptional noise is challenging.
• Existing lncRNA classifications are complex and struggle with the growing number of annotated lncRNAs.

Purpose of the Study:

• To develop a novel classification system for human genes based on quantitative RNA metabolism profiles.
• To differentiate functional lncRNAs from non-functional transcriptional byproducts.

Main Methods:

• Quantitative analysis of transcription, splicing, degradation, localization, and translation for coding and noncoding human genes.
• Clustering genes into classes with similar RNA metabolism profiles, including both messenger RNAs (mRNAs) and lncRNAs.

Main Results:

• Annotated lncRNAs exhibit lower synthesis and higher degradation rates compared to mRNAs.
• Distinct mechanistic differences were identified contributing to slower lncRNA splicing.
• Gene classes showed unique RNA metabolism, evolutionary patterns, and sensitivity to cellular RNA-regulatory pathways.

Conclusions:

• A new classification based on RNA metabolism profiles effectively groups genes, including both mRNAs and lncRNAs.
• This RNA metabolism-based classification provides a valuable alternative to existing genomic context-driven lncRNA annotations.