RNA-Chrom: a manually curated analytical database of RNA-chromatin interactome

BaRDIC: robust peak calling for RNA-DNA interaction data

Chromatin-associated non-coding RNAs play important roles in various cellular processes by targeting genomic loci. Two types of genome-wide NGS experiments exist to detect such targets: 'one-to-al', which focuses on targets of a single RNA, and 'all-to-al', which captures targets of all RNAs in a sample. As with many NGS experiments, they are prone to biases and noise, so it becomes essential to detect 'peaks'-specific interactions of an RNA with genomic targets. Here, we present BaRDIC-Binomial RNA-DNA Interaction Caller-a tailored method to detect peaks in both types of RNA-DNA interaction data. BaRDIC is the first tool to simultaneously take into account the two most prominent biases in the data: chromatin heterogeneity and distance-dependent decay of interaction frequency. Since RNAs differ in their interaction preferences, BaRDIC adapts peak sizes according to the abundances and contact patterns of individual RNAs. These features enable BaRDIC to make more robust predictions than currently applied peak-calling algorithms and better handle the characteristic sparsity of all-to-all data. The BaRDIC package is freely available at https://github.com/dmitrymyl/BaRDIC.

Functional identification of cis-regulatory long noncoding RNAs at controlled false discovery rates

A key attribute of some long noncoding RNAs (lncRNAs) is their ability to regulate expression of neighbouring genes in cis. However, such 'cis-lncRNAs' are presently defined using ad hoc criteria that, we show, are prone to false-positive predictions. The resulting lack of cis-lncRNA catalogues hinders our understanding of their extent, characteristics and mechanisms. Here, we introduce TransCistor, a framework for defining and identifying cis-lncRNAs based on enrichment of targets amongst proximal genes. TransCistor's simple and conservative statistical models are compatible with functionally defined target gene maps generated by existing and future technologies. Using transcriptome-wide perturbation experiments for 268 human and 134 mouse lncRNAs, we provide the first large-scale survey of cis-lncRNAs. Known cis-lncRNAs are correctly identified, including XIST, LINC00240 and UMLILO, and predictions are consistent across analysis methods, perturbation types and independent experiments. We detect cis-activity in a minority of lncRNAs, primarily involving activators over repressors. Cis-lncRNAs are detected by both RNA interference and antisense oligonucleotide perturbations. Mechanistically, cis-lncRNA transcripts are observed to physically associate with their target genes and are weakly enriched with enhancer elements. In summary, TransCistor establishes a quantitative foundation for cis-lncRNAs, opening a path to elucidating their molecular mechanisms and biological significance.

Exploring the landscape of tools and resources for the analysis of long non-coding RNAs

In recent years, research on long non-coding RNAs (lncRNAs) has gained considerable attention due to the increasing number of newly identified transcripts. Several characteristics make their functional evaluation challenging, which called for the urgent need to combine molecular biology with other disciplines, including bioinformatics. Indeed, the recent development of computational pipelines and resources has greatly facilitated both the discovery and the mechanisms of action of lncRNAs. In this review, we present a curated collection of the most recent computational resources, which have been categorized into distinct groups: databases and annotation, identification and classification, interaction prediction, and structure prediction. As the repertoire of lncRNAs and their analysis tools continues to expand over the years, standardizing the computational pipelines and improving the existing annotation of lncRNAs will be crucial to facilitate functional genomics studies.