About this Research Topic
Circular RNAs (circRNAs) are sparking interest in several branches of biology and biomedicine. CircRNAs are structurally stable and evolutionary conserved and can regulate cellular processes by different mechanisms, including the encoding of unique peptides and the interaction with miRNAs and proteins, mainly with a decoying activity. CircRNAs are particularly attractive for biomedical and cancer research since their expression is dysregulated in diseases, also in conjunction with chromosomal rearrangements. Besides, they have been linked to tumor development, can be involved in immune response and virus-host interactions and could be valuable agents for developing diagnostics and therapeutics tools.
Genome-wide study of circRNAs commonly employs high-throughput sequencing followed by the application of advanced computational techniques to explore circRNA features. Nevertheless, circRNA bioinformatics is a relatively novel field that still lacks powerful integrated methods to characterize circRNAs structure and function. Among open problems are accurate circRNA detection and estimation of expression levels, prediction of the circRNA role in cell behavior and impact on the phenotype.
Our aim is to create a collection of articles describing cutting-edge bioinformatics approaches solving the problems posed by circRNA investigations. The areas to be covered include but are not limited to novel computational tools of bioinformatics and systems biology, machine learning approaches, statistical models, databases, and multi-omics data integration methods for circRNA studies. Reviews of research done in the mentioned above areas will be welcome as well.
Genome-wide study of circRNAs commonly employs high-throughput sequencing followed by the application of advanced computational techniques to explore circRNA features. Nevertheless, circRNA bioinformatics is a relatively novel field that still lacks powerful integrated methods to characterize circRNAs structure and function. Among open problems are accurate circRNA detection and estimation of expression levels, prediction of the circRNA role in cell behavior and impact on the phenotype.
Our aim is to create a collection of articles describing cutting-edge bioinformatics approaches solving the problems posed by circRNA investigations. The areas to be covered include but are not limited to novel computational tools of bioinformatics and systems biology, machine learning approaches, statistical models, databases, and multi-omics data integration methods for circRNA studies. Reviews of research done in the mentioned above areas will be welcome as well.
Keywords: circular RNA, computational biology, RNA-seq, function prediction, regulatory networks
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