RNA G-quadruplex (rG4) is a vital RNA tertiary structure motif that involves the base pairs on both Hoogsteen and Watson-Crick faces of guanines. rG4 is of great importance in the post-transcriptional regulation of gene expression. Experimental technologies have advanced to identify in vitro and in vivo rG4s across diverse transcriptomes. Building on these recent advances, here we present G4Atlas, the first transcriptome-wide G-quadruplex database, in which we have collated, classified, and visualized transcriptome rG4 experimental data, generated from rG4-seq, chemical profiling and ligand-binding methods. Our comprehensive database includes transcriptome-wide rG4s generated from 82 experimental treatments and 238 samples across ten species. In addition, we have also included RNA secondary structure prediction information across both experimentally identified and unidentified rG4s to enable users to display any potential competitive folding between rG4 and RNA secondary structures. As such, G4Atlas will enable users to explore the general functions of rG4s in diverse biological processes. In addition, G4Atlas lays the foundation for further data-driven deep learning algorithms to examine rG4 structural features.

Deep learning, or artificial neural networks, is a type of machine learning algorithm that can decipher underlying relationships from large volumes of data and has been successfully applied to solve structural biology questions, such as RNA structure. RNA can fold into complex RNA structures by forming hydrogen bonds, thereby playing an essential role in biological processes. While experimental effort has enabled resolving RNA structure at the genome-wide scale, deep learning has been more recently introduced for studying RNA structure and its functionality. Here, we discuss successful applications of deep learning to solve RNA problems, including predictions of RNA structures, non-canonical G-quadruplex, RNA-protein interactions and RNA switches. Following these cases, we give a general guide to deep learning for solving RNA structure problems.

Life science has been in pursuit of precise and efficient genome editing in living cells since the very beginning of the first restriction cloning attempt. The introduction of RNA-guided CRISPR-associated (Cas) nucleases contributed to this ultimate goal through their ability to deliver a double-strand break (DSB) to a precise target location in various species, obsoleting the preceding editing tools, such as zinc-finger nucleases (ZFNs) and transcription activator-like effector nucleases (TALENs). The derivative technology, base editing, combines the catalytically inactivated Cas nuclease and nucleotide deaminase and mediates the genetic modifications at single-nucleotide precision without introducing a DSB. Moreover, the cytosine base editors (CBEs) are able to transform multiple codons into stop codons, rapidly inactivating a gene of interest and enabling loss-of-function study in some recombination-deficient species. Here, we present the CRISPR-CBEI tool kit to assist the design of sgRNAs for CBE-mediated gene inactivation.