RNA Biology

From Sequence to Function: Bioinformatics Resources for Non-coding RNAs

Non-coding RNAs are present in all cellular organisms and many viruses. They are integral to a vast array of biological processes, from the decoding of the genome to the arms race between host and pathogen. The classification of ncRNAs is based on size, primary sequence and secondary structure. In many cases, class is a strong predictor of function, making computational tools an important foundation for ncRNA research projects.

Classes of ncRNA, such as rRNAs and tRNAs, have central housekeeping functions, performing molecular roles essential to life. Other ncRNAs, such as miRNAs, regulate post-transcriptional gene expression, which is important in the control of pathways or processes. Despite the link between structure and function, many ncRNAs are poorly characterised and the increasing amount of data from high-throughput sequencing is widening the gap between sequence and functional knowledge. Moreover, certain ncRNA classes, such as circRNAs and lncRNAs, are less well-defined, whilst others, such as snoRNAs, are being shown to possess non-canonical functions. Therefore, accessible databases and resources that integrate sequence and functional information are vital to inform computational prediction and experimental research.

This Article Collection focuses on the data, methodologies and computational resources for collating, finding and interpreting functional information on ncRNAs. This includes ncRNA class-specific and species-specific resources, computational approaches and novel high-throughput datasets. We welcome original research articles, comprehensive reviews and database/resource papers that advance our understanding of ncRNA function through bioinformatics and human disease and model organism studies.

We welcome original research, reviews and methods on:

• Computational methods for ncRNA prediction, classification, structural modelling and functional inference.
• Machine learning and deep learning methods, including RNA foundation models and language model-based approaches.
• Papers developing ‘gold-standard’ benchmarking and evaluation datasets.
• Platforms for the integration of multi-omics data for ncRNA functional annotation.
• Systems and network analysis of ncRNA regulatory networks.
• Methods for mining and interpreting ncRNA data from existing databases, including walk-through methodologies and new database features.
• Integration and assimilation of research data via curation, text-mining or artificial intelligence-based methodologies.
• Comprehensive comparisons of data coverage or the quality of resources.
• Resources and computational approaches for specific ncRNA classes, including snoRNAs, circRNAs, lncRNAs and other emerging RNA families.
• Development of ncRNA databases, web-based tools and visualisation resources.
• New high-throughput ncRNA datasets.
• The collation of disease-associated ncRNAs identified through human disease or model organism research.

keywords:

1. Non-coding RNA
2. Database
3. Bioinformatics
4. Machine learning
5. Functional annotation

Guest Advisors

Dr. Helen Attrill is a senior biocurator at FlyBase and Assistant Research Professor at the Department of Physiology, Development and Neuroscience, University of Cambridge, UK. Helen’s work centres on the annotation of gene function in Drosophila melanogaster, best practices in GO curation and has led initiatives to promote the functional annotation of non-coding RNAs. For more details about Dr. Attrill's work, please visit https://flybase.org/ or https://orcid.org/0000-0003-3212-6364

Dr. Andrew Green is an ARISE Marie Curie Fellow at RNAcentral and EMBL-EBI. Andrew's work focuses on developing and applying ML techniques for biological and scientific data curation, including the creation of LLM-powered curation systems to accelerate the curation of ncRNA functional annotations for GO. Andrew’s recent work explores mechanistic interpretability on biological sequence foundation models to uncover the inner algorithms of models trained on biological sequences. For more details about Dr. Green's work, please visit https://www.ebi.ac.uk/people/person/andrew-green/ or https://orcid.org/0000-0002-8297-0953

Disclosure Statement: Guest Advisors declare no conflict of interest regarding this work.

All manuscripts submitted to this Article Collection will undergo a full peer-review. Please review the journal scope and author submission instructions prior to submitting a manuscript.

To submit your papers to this Article Collection, please:

1. Check "yes" for the question, "Are you submitting your paper for a specific special issue or article collection?"
2. Select the Article Collection ‘From Sequence to Function: Bioinformatics Resources for Non-coding RNAs’ from the drop-down menu under the question, "Special Issue or Article Collection Name."

Please contact Changluan Zhou at [email protected] with any queries and discount codes regarding this Article Collection.