Machine learning and related approaches in transcriptomics.

Data acquisition for transcriptomic studies used to be the bottleneck in the transcriptomic analytical pipeline. However, recent developments in transcriptome profiling technologies have increased researchers' ability to obtain data, resulting in a shift in focus to data analysis. Incorporating machine learning to traditional analytical methods allows the possibility of handling larger volumes of complex data more efficiently. Many bioinformaticians, especially those unfamiliar with ML in the study of human transcriptomics and complex biological systems, face a significant barrier stemming from their limited awareness of the current landscape of ML utilisation in this field. To address this gap, this review endeavours to introduce those individuals to the general types of ML, followed by a comprehensive range of more specific techniques, demonstrated through examples of their incorporation into analytical pipelines for human transcriptome investigations. Important computational aspects such as data pre-processing, task formulation, results (performance of ML models), and validation methods are encompassed. In hope of better practical relevance, there is a strong focus on studies published within the last five years, almost exclusively examining human transcriptomes, with outcomes compared with standard non-ML tools.

Protein-coding circular RNAs - mechanism, detection, and their role in cancer and neurodegenerative diseases.

Circular RNAs (circRNAs) are a unique class of non-coding RNAs and were originally thought to have no protein-coding potential due to their lack of a 5' cap and 3' poly(A) tail. However, recent studies have challenged this notion and revealed that some circRNAs have protein-coding potential. They have emerged as a key area of interest in cancer and neurodegeneration research as recent studies have identified several circRNAs that can produce functional proteins with important roles in cancer progression. The protein-coding potential of circRNAs is determined by the presence of an open reading frame (ORF) within the circular structure that can encode a protein. In some cases, the ORF can be translated into a functional protein despite the lack of traditional mRNA features. While the protein-coding potential of most circRNAs remains unclear, several studies have identified specific circRNAs that can produce functional proteins. Understanding the protein-coding potential of circRNAs is important for unravelling their biological functions and potential roles in disease. Our review provides comprehensive coverage of recent advances in the field of circRNA protein-coding capacity and its impact on cancer and neurodegenerative diseases pathogenesis and progression.