Although next-generation sequencing (NGS) technology revolutionized sequencing, offering a tremendous sequencing capacity with groundbreaking depth and accuracy, it continues to demonstrate serious limitations. In the early 2010s, the introduction of a novel set of sequencing methodologies, presented by two platforms, Pacific Biosciences (PacBio) and Oxford Nanopore Sequencing (ONT), gave birth to third-generation sequencing (TGS). The innovative long-read technologies turn genome sequencing into an ease-of-handle procedure by greatly reducing the average time of library construction workflows and simplifying the process of de novo genome assembly due to the generation of long reads. Long sequencing reads produced by both TGS methodologies have already facilitated the decipherment of transcriptional profiling since they enable the identification of full-length transcripts without the need for assembly or the use of sophisticated bioinformatics tools. Long-read technologies have also provided new insights into the field of epitranscriptomics, by allowing the direct detection of RNA modifications on native RNA molecules. This review highlights the advantageous features of the newly introduced TGS technologies, discusses their limitations and provides an in-depth comparison regarding their scientific background and available protocols as well as their potential utility in research and clinical applications.

The integration of artificial intelligence (AI) into next-generation sequencing (NGS) has revolutionized genomics, offering unprecedented advancements in data analysis, accuracy, and scalability. This review explores the synergistic relationship between AI and NGS, highlighting its transformative impact across genomic research and clinical applications. AI-driven tools, including machine learning and deep learning, enhance every aspect of NGS workflows—from experimental design and wet-lab automation to bioinformatics analysis of the generated raw data. Key applications of AI integration in NGS include variant calling, epigenomic profiling, transcriptomics, and single-cell sequencing, where AI models such as CNNs, RNNs, and hybrid architectures outperform traditional methods. In cancer research, AI enables precise tumor subtyping, biomarker discovery, and personalized therapy prediction, while in drug discovery, it accelerates target identification and repurposing. Despite these advancements, challenges persist, including data heterogeneity, model interpretability, and ethical concerns. This review also discusses the emerging role of AI in third-generation sequencing (TGS), addressing long-read-specific challenges, like fast and accurate basecalling, as well as epigenetic modification detection. Future directions should focus on implementing federated learning to address data privacy, advancing interpretable AI to improve clinical trust and developing unified frameworks for seamless integration of multi-modal omics data. By fostering interdisciplinary collaboration, AI promises to unlock new frontiers in precision medicine, making genomic insights more actionable and scalable.

In recent years, the advent of new experimental methodologies for studying the high complexity of the human genome and proteome has led to the generation of an increasing amount of digital information, hence bioinformatics, which harnesses computer science, biology, and chemistry, playing a mandatory role for the analysis of the produced datasets. The emerging technology of Artificial Intelligence (AI), including Machine Learning (ML) and Artificial Neural Networks (ANNs), is nowadays at the core of biomedical research and has already paved the way for significant breakthroughs in both biological and medical sciences. AI and computer science have transformed traditional medicine into modern biomedicine, thus promising a new era in systems biology that will enhance drug discovery strategies and facilitate clinical practice. The current review defines the main categories of AI and thoroughly describes the fundamental principles of the widely used ML, ANNs and DL approaches. Furthermore, we aim to underline the determinant role of AI-based methods in various biological research fields, such as proteomics and drug design techniques, and finally, investigate the implication of AI in everyday clinical practice and healthcare systems. Finally, this review also highlights the challenges and future directions of AI in Modern Biomedical study.

In October 2020, the chemistry Nobel Prize was awarded to Emmanuelle Charpentier and Jennifer A. Doudna for the discovery of a new promising genome-editing tool: the genetic scissors of CRISPR-Cas9. The identification of CRISPR arrays and the subsequent identification of cas genes, which together represent an adaptive immunological system that exists not only in bacteria but also in archaea, led to the development of diverse strategies used for precise DNA editing, providing new insights in basic research and in clinical practice. Due to their advantageous features, the CRISPR-Cas systems are already employed in several biological and medical research fields as the most suitable technique for genome engineering. In this review, we aim to describe the CRISPR-Cas systems that have been identified among prokaryotic organisms and engineered for genome manipulation studies. Furthermore, a comprehensive comparison between the innovative CRISPR-Cas methodology and the previously utilized ZFN and TALEN editing nucleases is also discussed. Ultimately, we highlight the contribution of CRISPR-Cas methodology in modern biomedicine and the current plethora of available applications for gene KO, repression and/or overexpression, as well as their potential implementation in therapeutical strategies that aim to improve patients’ quality of life.

Over 10¹⁴ symbiotic microorganisms are present in a healthy human body and are responsible for the synthesis of vital vitamins and amino acids, mediating cellular pathways and supporting immunity. However, the deregulation of microbial dynamics can provoke diverse human diseases such as diabetes, human cancers, cardiovascular diseases, and neurological disorders. The human gastrointestinal tract constitutes a hospitable environment in which a plethora of microbes, including diverse species of archaea, bacteria, fungi, and microeukaryotes as well as viruses, inhabit. In particular, the gut microbiome is the largest microbiome community in the human body and has drawn for decades the attention of scientists for its significance in medical microbiology. Revolutions in sequencing techniques, including 16S rRNA and ITS amplicon sequencing and whole genome sequencing, facilitate the detection of microbiomes and have opened new vistas in the study of human microbiota. Especially, the flourishing fields of metagenomics and metatranscriptomics aim to detect all genomes and transcriptomes that are retrieved from environmental and human samples. The present review highlights the complexity of the gastrointestinal tract microbiome and deciphers its implication not only in cellular homeostasis but also in human diseases. Finally, a thorough description of the widely used microbiome detection methods is discussed.

Colorectal cancer (CRC) is the second most lethal cause of cancer-related deaths in Europe. Fragments of tRNA^Pro are conserved among vertebrates, characterized by pleiotropic regulatory functions and have been found to discriminate colorectal tumors from normal colorectal mucosa. In the current study, we investigated the prognostic utility of 5′-tiRNA-Pro^TGG levels in CRC. For this purpose, total RNA was extracted from 155 malignant colorectal tumors and 74 adjacent non-cancerous tissue specimens, polyadenylated and reverse-transcribed using an oligo-dT adapter as primer. Real-time quantitative PCR (qPCR) was used to assess the levels of 5′-tiRNA-Pro^TGG. Kaplan-Meier survival analysis demonstrated that high 5′-tiRNA-Pro^TGG levels predict both poor disease-free survival (DFS) and overall survival (OS) of CRC patients. Of note, high 5′-tiRNA-Pro^TGG levels retain their unfavorable prognostic value in patients with rectal cancer and/or moderately differentiated CRC (grade II). More importantly, multivariate cox regression analysis highlighted that the overexpression of 5′-tiRNA-Pro^TGG constitutes an adverse prognostic factor predicting short-term relapse of CRC patients independently of the established prognosticators in CRC. Finally, bioinformatics analysis unveiled a potentially critical role of 5′-tiRNA-Pro^TGG regarding the maintenance of cellular homeostasis, signaling, cell communication, and cellular motility.