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.

Recent advances in the investigation of fusion RNAs and their role in molecular pathology of cancer.

Gene fusions have had a significant role in the development of various types of cancer, oftentimes involved in oncogenic activities through dysregulation of gene expression or signalling pathways. Some cancer-associated chromosomal translocations can undergo backsplicing, resulting in fusion-circular RNAs, a more stable isoform immune to RNase degradation. This stability makes fusion circular RNAs a promising diagnostic biomarker for cancer. While the detection of linear fusion RNAs and their function in certain cancers have been described in literature, fusion circular RNAs lag behind due to their low abundance in cancer cells. This review highlights current literature on the role of linear and circular fusion transcripts in cancer, tools currently available for detecting of these chimeric RNAs and their function and how they play a role in tumorigenesis.

A unique circular RNA expression pattern in the peripheral blood of myalgic encephalomyelitis/chronic fatigue syndrome patients.

Myalgic encephalomyelitis/chronic fatigue syndrome (ME/CFS) is a debilitating disease with obscure aetiology. The underdiagnosis rate of ME/CFS is high due to the lack of diagnostic criteria based on objective markers. In recent years, circRNAs have emerged as potential genetic biomarkers for neurological diseases, including Parkinson's disease and Alzheimer's disease, making them likely to have the same prospect of being biomarkers in ME/CFS. However, despite the extensive amount of research that has been performed on the transcriptomes of ME/CFS patients, all of them are solely focused on linear RNAs, and the profiling of circRNAs in ME/CFS has been completely omitted. In this study, we investigated the expression profiles of circRNAs, comparing ME/CFS patients and controls before and after two sessions of cardiopulmonary exercise longitudinally. In patients with ME/CFS, the number of detected circRNAs was higher compared to healthy controls, indicating potential differences in circRNA expression associated with the disease. Additionally, healthy controls showed an increase in the number of circRNAs following exercise testing, while no similar pattern was evident in ME/CFS patients, further highlighting physiological differences between the two groups. A lack of correlation was observed between differentially expressed circRNAs and their corresponding coding genes in terms of expression and function, suggesting the potential of circRNAs as independent biomarkers in ME/CFS. Specifically, 14 circRNAs were highly expressed in ME/CFS patients but absent in controls throughout the exercise study, indicating a unique molecular signature specific to ME/CFS patients and providing potential diagnostic biomarkers for the disease. Significant enrichment of protein and gene regulative pathways were detected in relation to five of these 14 circRNAs based on their predicted miRNA target genes. Overall, this is the first study to describe the circRNA expression profile in peripheral blood of ME/CFS patients, providing valuable insights into the molecular mechanisms underlying the disease.

RNA polyadenylation patterns in the human transcriptome.

The eukaryotic transcriptome undergoes various post-transcriptional modifications which assists gene expression. Polyadenylation is a molecular process occurring at the 3'-end of the RNA molecule which involves the poly(A) polymerase attaching adenine monophosphate molecules in a chain-like fashion to assemble a poly(A) tail. Multiple RNA isoforms are produced with differing 3'-UTR and exonic compositions through alternative polyadenylation (APA) which enhances the diversification of alternatively spliced mRNA transcripts. To study polyadenylation patterns, novel methods have been developed using short-read and long-read sequencing technologies to analyse the 3'-ends of the transcript. Recent studies have identified unique polyadenylation patterns in different cellular functions, including oncogenic activity, which could prove valuable in the understanding of medical genetics, particularly in the discovery of biomarkers in diseased states. We present a review of current literature reporting on polyadenylation and the biological relevance in the mammalian transcriptome, with a focus on the human transcriptome. Additionally, we have explored the various methods available to detect polyadenylation patterns using second and third generation sequencing technologies.