The advent of next-generation sequencing (NGS) has brought about a paradigm shift in genomics research, offering unparalleled capabilities for analyzing DNA and RNA molecules in a high-throughput and cost-effective manner. This transformative technology has swiftly propelled genomics advancements across diverse domains. NGS allows for the rapid sequencing of millions of DNA fragments simultaneously, providing comprehensive insights into genome structure, genetic variations, gene expression profiles, and epigenetic modifications. The versatility of NGS platforms has expanded the scope of genomics research, facilitating studies on rare genetic diseases, cancer genomics, microbiome analysis, infectious diseases, and population genetics. Moreover, NGS has enabled the development of targeted therapies, precision medicine approaches, and improved diagnostic methods. This review provides an insightful overview of the current trends and recent advancements in NGS technology, highlighting its potential impact on diverse areas of genomic research. Moreover, the review delves into the challenges encountered and future directions of NGS technology, including endeavors to enhance the accuracy and sensitivity of sequencing data, the development of novel algorithms for data analysis, and the pursuit of more efficient, scalable, and cost-effective solutions that lie ahead.

We are very thankful to the commentator for pointing out the issues in the review article by Satam et al

Prostate cancer (PCa) is one of the leading causes of cancer death in men. While prostate-specific antigen (PSA) testing is widely used for screening, its diagnostic accuracy is limited, often failing to distinguish between benign and malignant prostate conditions, underscoring the need for novel biomarkers with improved diagnostic performance. This study aimed to identify and validate a panel of urinary RNA biomarkers with improved diagnostic accuracy for PCa. RNA-sequencing analysis of exfoliated cells in urine specimens identified 50 candidate RNAs. After initial qPCR testing in pooled urine, three biomarkers (TTC3, H4C5, EPCAM) with optimal specificity and sensitivity were selected as a biomarker panel. Diagnostic performance was evaluated in a case-control study divided into development (n = 243 participants) and validation (n = 646 participants) datasets. Biomarker expression was confirmed in tissue specimens, and the oncogenic function of TTC3 was assessed in vitro and in vivo. The three-biomarker urine panel robustly identified PCa with an area under the curve (AUC) of 0.96 (95% CI: 0.94-0.98) compared to 0.83 (95% CI: 0.77-0.88) for urinary prostate cancer antigen 3 (PCA3) RNA in the development dataset and 0.92 (95% CI: 0.89-0.94) compared to 0.76 (95% CI: 0.72-0.80) for PCA3 in the validation dataset. Urine biomarkers were nearly eliminated post-prostatectomy and were confirmed to originate from prostate tissue at both the RNA and protein levels. The panel maintained high diagnostic accuracy of PSA-negative PCa cases and distinguished PCa from benign prostate conditions (BPH, prostatitis). Functional studies demonstrated that TTC3 depletion significantly suppressed in vitro and in vivo tumour growth. This urine-based biomarker panel offers a promising sensitive and specific noninvasive diagnostic for PCa with the potential to form the basis for laboratory-developed and in vitro diagnostic assays. This study was supported by the International Prostate Cancer Foundation, JHU SKCCC (grant number P30CA006973), and Bankhead-Coley Cancer Research Program (grant number 24B16) to R. J. Perera and by the Maryland Innovation Initiative Grant to C. P. Pavlovich and R. J. Perera.

The field of epitranscriptomics is undergoing a technology-driven revolution. During past decades, RNA modifications like N6-methyladenosine (m⁶A), pseudouridine (ψ), and 5-methylcytosine (m⁵C) became acknowledged for playing critical roles in cellular processes. Direct RNA sequencing by Oxford Nanopore Technologies (ONT) enabled the detection of modifications in native RNA, by detecting noncanonical RNA nucleosides properties in raw data. Consequently, the field’s cutting edge has a heavy component in computer science, opening new avenues of cooperation across the community, as exchanging data is as impactful as exchanging samples. Therefore, we seize the occasion to bring scientists together within the RNA Modification and Processing (RMaP) challenge to advance solutions for RNA modification detection and discuss ideas, problems and approaches. We show several computational methods to detect the most researched mRNA modifications (m⁶A, ψ, and m⁵C). Results demonstrate that a low prediction error and a high prediction accuracy can be achieved on these modifications across different approaches and algorithms. The RMaP challenge marks a substantial step towards improving algorithms’ comparability, reliability, and consistency in RNA modification prediction. It points out the deficits in this young field that need to be addressed in further challenges.