DSSR is cited in a Nature paper on RIC-seq for profiling RNA–RNA interactions

I recently read the paper RIC-seq for global in situ profiling of RNA–RNA spatial interactions published in Nature by the Yuanchao Xue team from the Chinese Academy of Sciences. The abstract is as below:

Highly structured RNA molecules usually interact with each other, and associate with various RNA-binding proteins, to regulate critical biological processes. However, RNA structures and interactions in intact cells remain largely unknown. Here, by coupling proximity ligation mediated by RNA-binding proteins with deep sequencing, we report an RNA in situ conformation sequencing (RIC-seq) technology for the global profiling of intra- and intermolecular RNA–RNA interactions. This technique not only recapitulates known RNA secondary structures and tertiary interactions, but also facilitates the generation of three-dimensional (3D) interaction maps of RNA in human cells. Using these maps, we identify noncoding RNA targets globally, and discern RNA topological domains and trans-interacting hubs. We reveal that the functional connectivity of enhancers and promoters can be assigned using their pairwise-interacting RNAs. Furthermore, we show that CCAT1-5L—a super-enhancer hub RNA—interacts with the RNA-binding protein hnRNPK, as well as RNA derived from the MYC promoter and enhancer, to boost MYC transcription by modulating chromatin looping. Our study demonstrates the power and applicability of RIC-seq in discovering the 3D structures, interactions and regulatory roles of RNA.

The Methods part contains the following section, where DSSR is cited along with several other software tools:

Structural analysis of 28S rRNA. The RIC-seq reads aligned to 45S pre-rRNA (NR_046235.3) were collected and used to construct the interaction matrix shown in Fig. 1h. A Knight–Ruiz normalization al- gorithm, widely used in the normalization of Hi-C contact matrices51, was applied to eliminate sequencing bias along rRNA. For building the physical interaction map of 28S rRNA, the cryo-EM model of human 80S ribosome (RCSB Protein Data Bank (PDB) ID 4V6X) was down- loaded, and the spatial distances between every 5-nt bin in 28S rRNA were calculated using the mean spatial coordinates of carbon atoms in each 5-nt bin. Watson–Crick and non-Watson–Crick base pairs were identified using the DSSR software52. The 3D structure of the ribosome was visualized by the PyMOL system (Educational version, https:// pymol.org/2/). For the missing structures in 28S rRNA, we combined intramolecular RNA–RNA interactions detected by RIC-seq with the RNAstructure algorithm53 to deduce their 2D structures.

There are several other well-known programs for identifying and annotating RNA base pairs, including RNAView, FR3D, and MC-Annotate. One may wonder why DSSR is used here. In addition to asking the authors, interested viewers could simply test for themselves: try the different tools on PDB entry 4V6X and see what happens.

It is worth mentioning that a new DSSR-related paper “DSSR-enabled innovative schematics of 3D nucleic acid structures with PyMOL” has recently been accepted by publication in Nucleic Acids Research. I will shortly write another post on this topic when this paper is officially published online. To see DSSR-PyMOL schematics in action, please visit http://skmatic.x3dna.org. Here is the abstract of the new DSSR-PyMOL article:

Sophisticated analysis and simplified visualization are crucial for understanding complicated structures of biomacromolecules. DSSR (Dissecting the Spatial Structure of RNA) is an integrated computational tool that has streamlined the analysis and annotation of 3D nucleic acid structures. The program creates schematic block representations in diverse styles that can be seamlessly integrated into PyMOL and complement its other popular visualization options. In addition to portraying individual base blocks, DSSR can draw Watson-Crick pairs as long blocks and highlight the minor-groove edges. Notably, DSSR can dramatically simplify the depiction of G-quadruplexes by automatically detecting G-tetrads and treating them as large square blocks. The DSSR-enabled innovative schematics with PyMOL are aesthetically pleasing and highly informative: the base identity, pairing geometry, stacking interactions, double-helical stems, and G-quadruplexes are immediately obvious. These features can be accessed via four interfaces: the command-line interface, the DSSR plugin for PyMOL, the web application, and the web application programming interface. The supplemental PDF serves as a practical guide, with complete and reproducible examples. Thus, even beginners or occasional users can get started quickly, especially via the web application at http://skmatic.x3dna.org.





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