Computational Approaches for Pinpointing the Locations and Properties of Non-coding RNAs

Dear Colleagues,

Non-coding RNAs (ncRNAs) are molecules that play important roles in cellular regulation, and are increasingly being recognised for their potential in understanding and diagnosing disease. The rapid detection of ncRNA transcripts in recent years means that ncRNAs now vastly outnumber that of protein-coding genes. However, there are still key challenges remaining, such as how missing transcripts can be identified, refining current annotations and understanding the specific contexts in which they are expressed. Additionally, there is an urgent need to prioritise which transcripts should be studied further in order to decipher their regulatory roles and impact.

Recent advancements in computer algorithms and machine learning, including deep learning approaches, have a strong, yet to date largely unexplored, potential for boosting our knowledge of ncRNA identification and analysis. Therefore, in this series, we invite manuscripts that explore a broad range of computational algorithms and machine learning techniques in order to gain a more comprehensive understanding of ncRNAs. These manuscripts may potentially incorporate and integrate multiple data sources and types, such as next-generation sequencing data, including both (epi-)transcriptomic and epigenomic data, as well as sequences themselves. Furthermore, they may address a broad range of problems in the field, such as cataloguing and annotating ncRNA transcripts, understanding their properties, including sequence-specific motifs separating different RNA species, analysing their secondary structures and their potential to interact with other biomolecules (e.g., proteins, chromatin and/or other RNAs), and how we can characterise them in the context of specific diseases and conditions.

Contributions to further research in this area has a rich potential to help improve our understanding of biological regulation, which in turn can lead to the development of targeted therapies for a variety of diseases.

Dr. Sarah Rennie
Prof. Dr. Shizuka Uchida
Guest Editors

Manuscript Submission Information

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