Chromatin-enriched RNAs mark active and repressive cis-regulation: An analysis of nuclear RNA-seq.
PLoS Comput Biol
; 16(2): e1007119, 2020 02.
Article
in En
| MEDLINE
| ID: mdl-32040509
Long noncoding RNAs (lncRNAs) localize in the cell nucleus and influence gene expression through a variety of molecular mechanisms. Chromatin-enriched RNAs (cheRNAs) are a unique class of lncRNAs that are tightly bound to chromatin and putatively function to locally cis-activate gene transcription. CheRNAs can be identified by biochemical fractionation of nuclear RNA followed by RNA sequencing, but until now, a rigorous analytic pipeline for nuclear RNA-seq has been lacking. In this study, we survey four computational strategies for nuclear RNA-seq data analysis and develop a new pipeline, Tuxedo-ch, which outperforms other approaches. Tuxedo-ch assembles a more complete transcriptome and identifies cheRNA with higher accuracy than other approaches. We used Tuxedo-ch to analyze benchmark datasets of K562 cells and further characterize the genomic features of intergenic cheRNA (icheRNA) and their similarity to enhancer RNAs (eRNAs). We quantify the transcriptional correlation of icheRNA and adjacent genes and show that icheRNA is more positively associated with neighboring gene expression than eRNA or cap analysis of gene expression (CAGE) signals. We also explore two novel genomic associations of cheRNA, which indicate that cheRNAs may function to promote or repress gene expression in a context-dependent manner. IcheRNA loci with significant levels of H3K9me3 modifications are associated with active enhancers, consistent with the hypothesis that enhancers are derived from ancient mobile elements. In contrast, antisense cheRNA (as-cheRNA) may play a role in local gene repression, possibly through local RNA:DNA:DNA triple-helix formation.
Full text:
1
Database:
MEDLINE
Main subject:
RNA
/
Chromatin
/
Cell Nucleus
/
Gene Expression Regulation
/
Sequence Analysis, RNA
Limits:
Animals
/
Humans
Language:
En
Journal:
PLoS Comput Biol
Journal subject:
BIOLOGIA
/
INFORMATICA MEDICA
Year:
2020
Type:
Article
Affiliation country:
United States