Systematic identification of cis-interacting lncRNAs and their targets

Abstract

Transcription of the human genome yields mostly long non-coding RNAs (lncRNAs). Systematic functional annotation of lncRNAs is challenging due to their low expression level, cell type-specific occurrence, poor sequence conservation between orthologs, and lack of information about RNA domains. Currently, 95% of human lncRNAs have no functional characterization. Using chromatin conformation and Cap Analysis of Gene Expression (CAGE) data in 18 human cell types, we systematically located genomic regions in spatial proximity to lncRNA genes and identified functional clusters of interacting protein-coding genes, lncRNAs and enhancers. Using these clusters we provide a cell type-specific functional annotation for 7,651 out of 14,198 (53.88%) lncRNAs. LncRNAs tend to have specialized roles in the cell type in which it is first expressed, and to incorporate more general functions as its expression is acquired by multiple cell types during evolution. By analyzing RNA-binding protein and RNA-chromatin interaction data in the context of the spatial genomic interaction map, we explored mechanisms by which these lncRNAs can act.

Antisense oligonucleotide-mediated perturbation of long non-coding RNA reveals functional features in stem cells and across cell types

“…From the RADICL-seq results, 115 lncRNAs showed significant contacts (FDR < 0.05) and 2059 lncRNA-protein-coding gene pairs were identified. Consistent with the previous studies, 25, 26 the significant pairs of DNA-DNA and RNA-DNA interactions are largely overlapped ( Fig. S5a ).…”

“…8 The analytical pipeline has been described. 26 Briefly, after the binomial test at 10 kb resolution for intra-chromosomal interactions, respectively, interacting regions with FDR < 0.05 were considered significant. The bin regions were intersected with the whole gene regions (gene region + 800 bp upstream the transcription start site) of the lncRNA targets, the whole gene regions of mRNAs and the promoter regions of mRNAs (800 bp upstream and 200 bp downstream of the transcription start site of all transcripts) defined in FANTOM CAT.…”

“…The TAD regions in the whole genome of iPS cells were predicted by the Hi-C data at 50 kb resolution, as described previously. 26 Inter-chromosomal Hi-C interactions was identified at 50 kb resolution and mapped to genes the same way as intra-chromosomal interactions. The inter-chromosomal Hi-C interaction was only used to filter RADICL inter-chromosomal signal.…”

HiCUP-Plus: a fast open-source pipeline for accurately processing large scale Hi-C sequence data

“…Due to its popularity in the field of regulatory genomics, Hi-C has been made commercially available (Arima, 2022; Dovetail Genomics, 2021; Qiagen, 2022). Large-scale functional genomics projects such as FANTOM6 (Ramilowski et al, 2020; Agrawal et al, 2021) have applied Hi-C to map cell type-specific domains. While originally developed with “6-cutter” restriction-enzymes, it is now recommended to use “4-cutter” restriction enzymes or DNase for higher resolution (Belaghzal et al, 2018; Cameron et al, 2020; Ma et al, 2018; Ramani et al, 2016).…”

linc2function: A deep learning model to identify and assign function to long noncoding RNA (lncRNA)

“…These RNAs are involved in various cellular biological processes, and they do that in conjunction with other biomolecules through complex pathways [4]. A large number of lncRNAs are being discovered but about 95% of lncRNAs still lack functional annotation [5]. We reviewed existing lncRNA annotation methods and databases, some are sourced reliably while others are crowdsourced, and discovered that there is very little consensus between them [6].…”