Every year there is more and more evidence that non-coding RNAs play an important role in biological processes affecting various levels of organization of living systems: from the cellular (regulation of gene expression, remodeling and maintenance of chromatin structure, co-transcriptional suppression of transposons, splicing, post-transcriptional RNA modifications, etc.) to cell populations and even organismal ones (development, aging, cancer, cardiovascular and many other diseases). The development and creation of mutually complementary databases that will aggregate, unify and structure different types of data can help to reach the system level of studying non-coding RNAs. Here we present the RNA-Chrom manually curated analytical database, which contains the coordinates of billions of contacts of thousands of human and mouse RNAs with chromatin. Through the user-friendly web interface (https://rnachrom2.bioinf.fbb.msu.ru/), two approaches to the analysis of the RNA–chromatin interactome were implemented. Firstly, to find out whether the RNA of interest to a user contacts with chromatin, and if so, with which genes or DNA loci? Secondly, to find out which RNAs are in contact with the DNA locus of interest to a user (and probably participate in its regulation), and if there are such, what is the nature of their interaction? For a more detailed study of contact maps and their comparison with other data, the web interface allows a user to view them in the UCSC Genome Browser. Database URLhttps://genome.ucsc.edu/

Еще в начале 60-х годов прошлого века заметили, что с хроматином ассоциировано большое количество различных РНК. Что это за РНК и где на хроматине они локализованы? Когда и в каких процессах эти РНК выполняют свою нормальную или патогенную функцию? В данном обзоре мы описываем современные подходы, позволяющие в какой-то мере ответить на эти вопросы. Мы разбираем экспериментальные методы, позволяющие получить полный РНК-хроматиновый интерактом клетки или полногеномные карты взаимодействий отдельных РНК с хроматином, а также методы обработки экспериментальных данных. Мы акцентируем внимание на некодирующих РНК, которые функционируют в тесном контакте с хроматином и ассоциированными с хроматином белковыми комплексами. Разнообразие биологических примеров, полученных с помощью этих методов, не оставляет сомнений в том, что за счет тонкой настройки архитектуры хроматина, приводящей к изменению уровня экспрессии генов, взаимодействующие с хроматином РНК играют важную роль в работе клеточных систем.

Non-coding RNAs play an essential role in a wide variety of biological processes. We have previously developed RNA-Chrom, an analytical database containing uniformly processed RNA-chromatin interactions data. Here, we analyzed the consistency of these data with each other and performed a comparative analysis of human and mouse datasets from “all-to-all” experiments (interactome of all possible RNAs in a cell) and “one-to-all” experiments (individual RNA interactome). We analyzed the dependence of RNA cis-contacts density as a function of distance to the RNA source gene and found that in all experiments, density of cis-contacts decreases with distance. We tested whether the most contacting RNAs of one “all-to-all” experiment (“RNA-leaders”) are the same for the other “all-to-all” experiments. Our analysis shows that “all-to-all” experiments are not complete and require substantially deeper sequencing, and conclusions can only be drawn for highly contacting RNAs. We noted that for “all-to-all” experiments, sequencing depth and the type of cell treatment give a significant impact on the contact map, while the cell line does not have as much of an impact.

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-all”, which focuses on targets of a single RNA, and “all-to-all”, 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. BaRDIC package is freely available at https://github.com/dmitrymyl/BaRDIC .

Two types of experiments are used to study RNA-chromatin interactions: the interactome search for individual RNAs (“one-to-all” or OTA) and genome-wide contact mapping for all RNAs (“all-to-all” or ATA). Comparative analysis of ATA and OTA data revealed fundamental differences in resolution, completeness, and specificity. OTA data exhibit high resolution (~1000 bp) and reproducibility (>90%), serving as a “gold standard”. ATA data, however, have lower resolution (~5000 bp), and their reproducibility (<10%) is critically dependent on the protocol, with two-step fixation using disuccinimidyl glutarate and formaldehyde (GRID-seq) showing a clear advantage over formaldehyde alone. The introduced “chromatin potential” metric and BaRDIC peak filtering effectively isolate the specific signal. This study proposes a strategy for reliable interactome analysis: combining RNA selection based on chromatin potential with the use of concordant contacts from peaks.

Long non-coding RNAs (lncRNAs) significantly contribute to genome structure and regulation. Many lncRNAs are known to interact with chromatin and in this way to affect gene expression patterns through epigenetic regulation. Still, experimental protocols for lncRNA-chromatin interactions do not provide any insight into the mechanisms of lncRNA-based genome-wide regulation. Here we present an integration of HiMoRNA – a resource containing correlated lncRNA-epigenetic changes in specific genomic locations genome-wide, – and RNA-Chrom, a resource featuring uniformly processed experimental data on RNA-chromatin interactions. Our integration approach allows generating interpretable and experimentally supported hypotheses on the mechanisms of lncRNA epigenetic regulation of gene expression. For this integration we have tailored the interface of HiMoRNA such that for many lncRNAs experimentally detected RNA-chromatin contacts are available from RNA-Chrom for browsing, analysis and downloading. HiMoRNA peaks supported by RNA-Chrom contacts can be explained by external experimental data. We believe that the integration of HiMoRNA and RNA-Chrom is a convenient and valuable approach that can provide experimental and mechanistic insights and greatly facilitate functional annotation of lncRNAs.