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

Pervasive transcription of the human genome results in a heterogeneous mix of coding RNAs and long noncoding RNAs (IncRNAs). Only a small fraction of IncRNAs have demonstrated regulatory functions, thus making functional IncRNAs difficult to distinguish from nonfunctional transcriptional byproducts. This difficulty has resulted in numerous competing human IncRNA classifications that are complicated by a steady increase in the number of annotated IncRNAs. To address these challenges, we quantitatively examined transcription, splicing, degradation, localization and translation for coding and noncoding human genes. We observed that annotated IncRNAs had lower synthesis and higher degradation rates than mRNAs and discovered mechanistic differences explaining slower IncRNA splicing. We grouped genes into classes with similar RNA metabolism profiles, containing both mRNAs and IncRNAs to varying extents. These classes exhibited distinct RNA metabolism, different evolutionary patterns and differential sensitivity to cellular RNA-regulatory pathways. Our classification provides an alternative to genomic context-driven annotations of lncRNAs.