Compilation of resources on subcellular localization of lncRNA Provisionally Accepted

Shubham Choudhury¹ Anand S. Rathore¹ Gajendra P. Raghava^1*

• ¹Indraprastha Institute of Information Technology Delhi, India

Long non-coding RNAs (lncRNAs) play a vital role in biological processes, and their dysfunctions lead to a wide range of diseases. Due to advancements in sequencing technology, more than 20,000 lncRNA transcripts have been identified in humans, almost equivalent to coding transcripts. One crucial aspect in annotating lncRNA function is predicting their subcellular localization, which often determines their functional roles within cells. This review aims to cover the experimental techniques, databases, and in-silico tools developed for identifying subcellular localization. Firstly, we discuss the experimental methods employed to determine the subcellular localization of lncRNAs. These techniques provide valuable insights into the precise cellular compartments where lncRNAs reside. Secondly, we explore the available computational resources and databases contributing to our understanding of lncRNAs, including information on their subcellular localization. These computational methods utilize algorithms and machine learning approaches to predict lncRNA subcellular locations using sequence and structural features. Lastly, we discuss the limitations of existing methodologies, future challenges, and potential applications of subcellular localization prediction for lncRNAs. We highlight the need for further advancements in computational methods and experimental validation to enhance the accuracy and reliability of subcellular localization predictions. To support the scientific community, we have developed a platform called LncInfo, which offers comprehensive information on lncRNAs, including their subcellular localization. This platform aims to consolidate and provide accessible resources to researchers studying lncRNAs and their functional roles (http://webs.iiitd.edu.in/raghava/lncinfo).