The large-scale heterogeneity of genetic diseases necessitated the deeper examination of nucleotide sequence alterations enhancing the discovery of new targeted drug attack points. The appearance of new sequencing techniques was essential to get more interpretable genomic data. In contrast to the previous short-reads, longer lengths can provide a better insight into the potential health threatening genetic abnormalities. Long-reads offer more accurate variant identification and genome assembly methods, indicating advances in nucleotide deflect-related studies. In this review, we introduce the historical background of sequencing technologies and show their benefits and limits, as well. Furthermore, we highlight the differences between short- and long-read approaches, including their unique advances and difficulties in methodologies and evaluation. Additionally, we provide a detailed description of the corresponding bioinformatics and the current applications.

Purpose: Homocysteine (HCY) metabolism is regulated by the methionine cycle, which is essential for DNA methylation and is associated with the folate cycle. This study examines the alterations in DNA methylation signature including epigenetic age changes, measure cell-free DNA (cfDNA), and HCY concentrations, and identifies genetic markers that may influence homocysteine response following folic acid (FA) supplementation in individuals with hyperhomocysteinemia (HHC). Methods: Blood samples were obtained from 43 HHC patients undergoing FA supplementation. We quantified FA and HCY levels, separated plasma and white blood cell fractions, and evaluated global DNA methylation using LINE-1 bisulfite pyrosequencing. Biological age was determined using Illumina BeadArray technology, and whole-exome sequencing was performed to investigate the patients’ genetic backgrounds. Results: Following FA supplementation, cfDNA levels significantly decreased and correlated positively with HCY (r = 0.2375). Elevated average LINE-1 methylation of cfDNA and PBMC-origin DNA was observed, with mean relative changes of 1.9% for both sample types. Regarding HCY levels, we categorized patients based on their response to FA supplementation. FA responders showed decreased HCY from 15.7 ± 5.5 to 11 ± 2.9 µmol/L, while in FA non-responders, an opposite trend was detected. The average biological age was reduced by 2.6 years, with a notable reduction observed in 80% of non-responders and 48% of responders. Sequencing identified mutations in several genes related to the one-carbon cycle, including MTRR, CHAT, and MTHFD1, with strong correlations to the non-responder phenotypes found in genes like PRMT3, TYMS, DNMT3A, and HIF3A. Conclusions: FA supplementation influences the HCY level, as well as affects the cfDNA amount and the DNA methylation pattern. However, genetic factors may play a crucial role in mediating individual responses to folate intake, emphasizing the need for personalized approaches in managing hyperhomocysteinemia.

The rapid advancement of genomic technologies has significantly transformed biomedical research and clinical applications, particularly in oncology. Identifying patient-specific genetic mutations has become a crucial tool for early cancer detection and personalized treatment strategies. Detecting tumors at the earliest possible stage provides critical insights beyond traditional tissue analysis. This paper presents a novel cyber-physical system that combines high-resolution tissue scanning, laser microdissection, next-generation sequencing, and genomic analysis to offer a comprehensive solution for early cancer detection. We describe the methodologies for scanning tissue samples, image processing of the morphology of single cells, quantifying morphometric parameters, and generating and analyzing real-time genomic metadata. Additionally, the intelligent system integrates data from open-access genomic databases for gene-specific molecular pathways and drug targets. The developed platform also includes powerful visualization tools, such as colon-specific gene filtering and heatmap generation, to provide detailed insights into genomic heterogeneity and tumor foci. The integration and visualization of multimodal single-cell genomic metadata alongside tissue morphology and morphometry offer a promising approach to precision oncology.

The progression of colorectal cancer is strongly influenced by environmental and genetic conditions. One of the key factors is tumor heterogeneity which is extensively studied by cfDNA and bulk sequencing methods; however, we lack knowledge regarding its effects at the single-cell level. Motivated by this, we aimed to employ an end-to-end single-cell sequencing workflow from tissue-derived sample isolation to exome sequencing. Our main goal was to investigate the heterogeneity patterns by laser microdissecting samples from different locations of a tissue slide. Moreover, by studying healthy colon control, tumor-associated normal, and colorectal cancer tissues, we explored tissue-specific heterogeneity motifs. For completeness, we also compared the performance of the whole-exome bulk, cfDNA, and single-cell sequencing methods based on variation at the level of a single nucleotide.

Background/Objectives: LINE-1 (L1) retrotransposons comprise approximately 17% of the human genome. Most are truncated, rearranged, or mutated molecular fossils, and only a small, variably estimated subset of L1 loci retains retrotransposition competence. L1 hypomethylation and retrotransposition have been associated with colorectal cancer (CRC), but comprehensive characterization of individual L1 insertions and their methylation remains challenging because of their repetitive sequence and length. In particular, conventional short-read sequencing provides limited resolution of individual L1 loci. Methods: We used Oxford Nanopore long-read whole-genome sequencing (WGS) to characterize L1 insertion-burden, insertion structure, and methylation in 21 CRC tissue samples, 22 normal adjacent tissue samples (NAT), 13 CRC-negative control tissues (NEG), and single samples of the CRC-derived cell lines SW480 and HT29. We focused on L1HS, the human-specific LINE-1 subfamily, and combined structural insertion detection with native DNA methylation profiling of individual full-length L1HS loci. Results: We identified 5,163 L1HS insertions meeting stringent predefined criteria, including 1,030 full-length insertions. L1HS insertion-burden showed a significant increase from NEG to NAT and CRC tissue groups (Jonckheere–Terpstra, P = 0.0338), while CRC tissues exhibited significantly lower mean L1HS methylation (Kruskal–Wallis \( P = 1.7 \times 10^{-5} \)) and substantial inter-sample and locus-level heterogeneity. Global mean L1HS methylation showed a weak but significant correlation with the number of full-length L1HS insertions, whereas the minimum methylation observed among L1HS loci within each sample showed a stronger correlation with L1HS insertion-burden. Conclusions: These findings demonstrate the utility of long-read sequencing for integrated structural and epigenetic characterization of individual L1HS loci in CRC and provide a framework for future investigation of LINE-1 features in larger, molecularly and clinically characterized cohorts.

Transposable elements (TEs) constitute nearly half of the human genome and shape chromatin organization, gene regulation and genome evolution. We have large gaps in understanding their influence to physiology and pathology for now. In humans, the most active elements—LINE-1 (L1), Alu, and SVA retain some copies with the ability to evade epigenetic repression and mobilize via target-primed reverse transcription (TPRT), whereas copies become inactive through fragmentation, mutation, or nesting, a process where a TE segments integrates into another TE segment. TE activity contributes to genomic instability and has been implicated in aging, cancer, neurological disorders, chromatin organization, and epigenetic regulation. Studying TEs is challenging due to their repetitive and polymorphic nature. Recent advances in sequencing technologies, including short- and long-read platforms, combined with specialized bioinformatic pipelines, now allow more comprehensive characterization of TE insertions, deletions, expression, and epigenetic status. Computational approaches vary in sensitivity, specificity, and resource requirements, and their performance is influenced by sequencing modality, coverage, and the reference genome used. Assembly-based and read-based methods, as well as tools integrating methylation or single-cell data, provide complementary insights into TE biology. Here we review the biology of active human TE. Survey state of the art short and long-read pipelines for TE analysis. And highlight their applications in studies of aging cancer and other complex diseases. We also provide practical guidance for selecting appropriate sequencing strategies and tools for TE-focused projects, and discuss emerging approaches and open questions in the field.