Integrative classification of human coding and non-coding genes based on RNA metabolism profiles

Abstract

The pervasive transcription of the human genome results in a heterogeneous mix of coding and long non-coding RNAs (lncRNAs). Only a small fraction of lncRNAs possess demonstrated regulatory functions, making it difficult to distinguish functional lncRNAs from non-functional transcriptional byproducts. This has resulted in numerous competing classifications of human lncRNA that are complicated by a steady increase in the number of annotated lncRNAs.\n\nTo address these challenges, we quantitatively examined transcription, splicing, degradation, localization and translation for coding and non-coding human genes. Annotated lncRNAs had lower synthesis and higher degradation rates than mRNAs, and we discovered mechanistic differences explaining the slower splicing of lncRNAs. We grouped genes into classes with similar RNA metabolism profiles. These classes contained both mRNAs and lncRNAs to varying degrees; they exhibited distinct relationships between steps of RNA metabolism, evolutionary patterns, and sensitivity to cellular RNA regulatory pathways. Our classification provides a behaviorally-coherent alternative to genomic context-driven annotations of lncRNAs.\n\nHighlightsO_LIHigh-resolution 4SU pulse labeling of RNA allows for quantifying synthesis, processing and decay rates across thousands of coding and non-coding transcripts.\nC_LIO_LISynthesis and processing rates of lncRNAs are lower than mRNAs, while degradation rates were substantially higher\nC_LIO_LIDifferences in the splicing efficiency between slow/lncRNA and fast/mRNA introns are explained by GC-content, splicing regulatory elements and unphosphorylated RNA poll II.\nC_LIO_LIA new annotation-agnostic classification of RNAs reveals seven clusters of lncRNAs and mRNAs with unique metabolism patterns that provides behaviorally coherent subsets of lncRNAs.\nC_LIO_LIClasses are distinguished by evolutionary patterns and sensitivity to cellular RNA regulatory pathways.\nC_LI