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.

Enhancing novel isoform discovery: leveraging nanopore long-read sequencing and machine learning approaches.

Long-read sequencing technologies can capture entire RNA transcripts in a single sequencing read, reducing the ambiguity in constructing and quantifying transcript models in comparison to more common and earlier methods, such as short-read sequencing. Recent improvements in the accuracy of long-read sequencing technologies have expanded the scope for novel splice isoform detection and have also enabled a far more accurate reconstruction of complex splicing patterns and transcriptomes. Additionally, the incorporation and advancements of machine learning and deep learning algorithms in bioinformatic software have significantly improved the reliability of long-read sequencing transcriptomic studies. However, there is a lack of consensus on what bioinformatic tools and pipelines produce the most precise and consistent results. Thus, this review aims to discuss and compare the performance of available methods for novel isoform discovery with long-read sequencing technologies, with 25 tools being presented. Furthermore, this review intends to demonstrate the need for developing standard analytical pipelines, tools, and transcript model conventions for novel isoform discovery and transcriptomic studies.

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.

Investigation of the Circular Transcriptome in Alzheimer's Disease Brain.

Circular RNAs (circRNAs) are a subclass of non-coding RNAs which have demonstrated potential as biomarkers for Alzheimer's disease (AD). In this study, we conducted a comprehensive exploration of the circRNA transcriptome within AD brain tissues. Specifically, we assessed circRNA expression patterns in the dorsolateral prefrontal cortex collected from nine AD-afflicted individuals and eight healthy controls. Utilising two circRNA detection tools, CIRI2 and CIRCexplorer2, we detected thousands of circRNAs and performed a differential expression analysis. CircRNAs which exhibited statistically significantly differential expression were identified as AD-specific differentially expressed circRNAs. Notably, our investigation revealed 120 circRNAs with significant upregulation and 1325 circRNAs displaying significant downregulation in AD brains when compared to healthy brain tissue. Additionally, we explored the expression profiles of the linear RNA counterparts corresponding to differentially expressed circRNAs in AD-afflicted brains and discovered that the linear RNA counterparts exhibited no significant changes in the levels of expression. We used CRAFT tool to predict that circUBE4B had potential to target miRNA named as hsa-miR-325-5p, ultimately regulated CD44 gene. This study provides a comprehensive overview of differentially expressed circRNAs in the context of AD brains, underscoring their potential as molecular biomarkers for AD. These findings significantly enhance our comprehension of AD's underlying pathophysiological mechanisms, offering promising avenues for future diagnostic and therapeutic developments.

Bile acids mediate fructose-associated liver tumour growth in mice.

High fructose diets are associated with an increased risk of liver cancer. Previous studies in mice suggest increased lipogenesis is a key mechanism linking high fructose diets to liver tumour growth. However, these studies administered fructose to mice at supraphysiological levels. The aim of this study was to determine whether liver tumour growth and lipogenesis were altered in mice fed fructose at physiological levels. To test this, we injected male C57BL/6 mice with the liver carcinogen diethylnitrosamine and then fed them diets without fructose or fructose ranging from 10 to 20 % total calories. Results showed mice fed diets with ≥15 % fructose had significantly increased liver tumour numbers (2-4-fold) and total tumour burden (∼7-fold) vs mice fed no-fructose diets. However, fructose-associated tumour burden was not associated with lipogenesis. Conversely, unbiased metabolomic analyses revealed bile acids were elevated in the sera of mice fed a 15 % fructose diet vs mice fed a no-fructose diet. Using a syngeneic ectopic liver tumour model, we show that ursodeoxycholic acid, which decreases systemic bile acids, significantly reduced liver tumour growth in mice fed the 15 % fructose diet but not mice fed a no-fructose diet. These results point to a novel role for systemic bile acids in mediating liver tumour growth associated with a high fructose diet. Overall, our study shows fructose intake at or above normal human consumption (≥15 %) is associated with increased liver tumour numbers and growth and that modulating systemic bile acids inhibits fructose-associated liver tumour growth in mice.