RNA, the key molecule in the central dogma, is essential for gene expression. The life cycle of an RNA involves transcription, modification, processing, localisation, translation, and degradation. Every step of these biological processes is crucial for regulating gene expression. With significant advances in next-generation sequencing technologies, large volumes of data were generated in measuring these RNA biological processes over tens of thousands of RNAs across different cell types and under different conditions. In addition, diverse technologies have been developed to reveal other properties of RNAs aside from sequence content, such as RNA editing, RNA modification and RNA structure. Measurements of these diverse properties at the genome-wide scale facilitate understanding the complexity of transcriptome architectures. Notably, these technologies have been widely applied across diverse species and natural variants for exploring genetic diversity at the RNA level.
These massive data resources have provided excellent opportunities for the applications of deep learning algorithms in exploring the general rules of RNA features and their functions in gene regulation. These applications have not only transformed the scope and depth of deciphering RNA-based molecular mechanisms but also promoted RNA-based molecular design and editing. Deep learning has opened a new era for studying RNA biology.
In this exciting era, on behalf of the editorial team, we invite authors to consider submitting manuscripts that may contribute to deep learning applications in RNA biology in an Article Collection about “Deep learning in RNA biology.” The journal will publish original research articles and reviews. All manuscripts will be reviewed by qualified reviewers, and accepted articles will be published online after proofreading and formatting.
The potential topics considered within this Article Collection include, but are not limited to:
1. Utilizing deep learning approaches for transcriptome data analysis.
2. Incorporating deep learning approaches for predicting RNA modifications.
3. Implementing deep learning approaches for RNA structure prediction.
4. Applying deep learning approaches in the design of RNA molecules.
5. Leveraging deep learning approaches for predicting RNA functionalities in diverse biological processes.
6. Using deep learning approaches for identifying RNA switches.
7. Implementing deep learning approaches for RNA architecture annotation.
8. Using deep learning approaches for predicting RNA-protein interactions.
Dr. Ding is a Group Leader at the John Innes Centre. She is one of the pioneers in the research field of RNA structure. Her group has developed new high-throughput methodologies for studying in vivo RNA structure and generated several databases. Her group has revealed functional roles of RNA structure features in regulating post-transcriptional gene expression such as RNA processing, translation and RNA degradation.
Prof. Zhang is one of the pioneers in the research field of the RNA structural systems biology. His group has combined research techniques such as structural biology, genomics, machine learning, and big data analysis to study major and cutting-edge biological problems. His group uses computing, especially machine learning, as our core tool and explore our conjecture through a unique experimental platform. In particular, the laboratory is also interested in developing and creating new computing and experimental techniques.
Dr. Wolfinger is a theoretical chemist in structural and computational RNA biology. His research comprises algorithmic bioinformatics and computational genomics. In particular, his group has been developing extensive computational methods for predicting RNA 2D and 3D structures, studying RNA-protein interactions, exploiting co-transcriptional RNA folding dynamics, and designing riboswitches for synthetic biology applications.
Disclosure statement: The Guest Advisors have no conflicts to disclose related to this work.
All manuscripts submitted to this Article Collection will undergo a full peer-review; the Guest Advisor for this collection will not be handling the manuscripts (unless they are an Editorial Board member). Please review the journal scope and author submission instructions prior to submitting a manuscript.
The deadline for submitting manuscripts is 31st July 2023.
