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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 incoherent annotations of human lncRNA. To 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. We discovered mechanistic differences explaining the slower splicing of lncRNAs. We grouped genes into coherent classes by performing annotation-agnostic unsupervised classification of 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 functionally coherent alternative to genomic context-driven annotations of lncRNAs.

ORGANISM(S): Homo sapiens

PROVIDER: GSE84722 | GEO | 2016/11/04

SECONDARY ACCESSION(S): PRJNA336176

REPOSITORIES: GEO

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Project description:Background: Long non-coding RNAs (lncRNAs) are increasingly implicated as gene regulators and may ultimately be more numerous than protein-coding genes in the human genome. Despite large numbers of reported lncRNAs, reference annotations are likely incomplete due to their lower and tighter tissue-specific expression compared to mRNAs. An unexplored factor potentially confounding lncRNA identification is inter-individual expression variability. Here, we characterize lncRNA natural expression variability in human primary granulocytes. Results: We annotate granulocyte lncRNAs and mRNAs in RNA-seq data from ten healthy individuals, identifying multiple lncRNAs absent from reference annotations, and use this to investigate three known features (higher tissue-specificity, lower expression, and reduced splicing efficiency) of lncRNAs relative to mRNAs. Expression variability was examined in seven individuals sampled three times at one or more than one month intervals. We show that lncRNAs display significantly more inter-individual expression variability compared to mRNAs. We confirm this finding in 2 independent human datasets by analyzing multiple tissues from the GTEx project and lymphoblastoid cell lines from the GEUVADIS project. Using the latter dataset we also show that including more human donors into the transcriptome annotation pipeline allows identification of an increasing number of lncRNAs, but minimally affects mRNA gene number. Conclusions: A comprehensive annotation of lncRNAs is known to require an approach that is sensitive to low and tight tissue-specific expression. Here we show that increased inter-individual expression variability is an additional general lncRNA feature to consider when creating a comprehensive annotation of human lncRNAs or proposing their use as prognostic or disease markers. We used PolyA+ RNA-seq data from human primary granulocytes of 10 healthy individuals to de novo annotate lncRNAs and mRNAs in this cell type and ribosomal depleted (total) RNA-seq data from seven of these individuals sampled three times to analyze lncRNA amd mRNA expression variability

Dataset Information

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

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