Genome Sequencing Technologies Research Articles

Coding genomes with gapped pattern graph convolutional network.

Genome sequencing technologies reveal a huge amount of genomic sequences. Neural network-based methods can be prime candidates for retrieving insights from these sequences because of their applicability to large and diverse datasets. However, the highly variable lengths of genome sequences severely impair the presentation of sequences as input to the neural network. Genetic variations further complicate tasks that involve sequence comparison or alignment. Inspired by the theory and applications of spaced seeds, we propose a graph representation of genome sequences called gapped pattern graph. These graphs can be transformed through a Graph Convolutional Network to form lower-dimensional embeddings for downstream tasks. On the basis of the gapped pattern graphs, we implemented a neural network model and demonstrated its performance on diverse tasks involving microbe and mammalian genome data. Our method consistently outperformed all the other state-of-the-art methods across various metrics on all tasks, especially for the sequences with limited homology to the training data. In addition, our model was able to identify distinct gapped pattern signatures from the sequences. The framework is available at https://github.com/deepomicslab/GCNFrame. Supplementary data are available at Bioinformatics online.

CITEViz: interactively classify cell populations in CITE-Seq via a flow cytometry-like gating workflow using R-Shiny

BackgroundThe rapid advancement of new genomic sequencing technology has enabled the development of multi-omic single-cell sequencing assays. These assays profile multiple modalities in the same cell and can often yield new insights not revealed with a single modality. For example, Cellular Indexing of Transcriptomes and Epitopes by Sequencing (CITE-Seq) simultaneously profiles the RNA transcriptome and the surface protein expression. The surface protein markers in CITE-Seq can be used to identify cell populations similar to the iterative filtration process in flow cytometry, also called “gating”, and is an essential step for downstream analyses and data interpretation. While several packages allow users to interactively gate cells, they often do not process multi-omic sequencing datasets and may require writing redundant code to specify gate boundaries. To streamline the gating process, we developed CITEViz which allows users to interactively gate cells in Seurat-processed CITE-Seq data. CITEViz can also visualize basic quality control (QC) metrics allowing for a rapid and holistic evaluation of CITE-Seq data.ResultsWe applied CITEViz to a peripheral blood mononuclear cell CITE-Seq dataset and gated for several major blood cell populations (CD14 monocytes, CD4 T cells, CD8 T cells, NK cells, B cells, and platelets) using canonical surface protein markers. The visualization features of CITEViz were used to investigate cellular heterogeneity in CD14 and CD16-expressing monocytes and to detect differential numbers of detected antibodies per patient donor. These results highlight the utility of CITEViz to enable the robust classification of single cell populations.ConclusionsCITEViz is an R-Shiny app that standardizes the gating workflow in CITE-Seq data for efficient classification of cell populations. Its secondary function is to generate basic feature plots and QC figures specific to multi-omic data. The user interface and internal workflow of CITEViz uniquely work together to produce an organized workflow and sensible data structures for easy data retrieval. This package leverages the strengths of biologists and computational scientists to assess and analyze multi-omic single-cell datasets. In conclusion, CITEViz streamlines the flow cytometry gating workflow in CITE-Seq data to help facilitate novel hypothesis generation.

GenArchBench: A genomics benchmark suite for arm HPC processors

Arm usage has substantially grown in the High-Performance Computing (HPC) community. Japanese supercomputer Fugaku, powered by Arm-based A64FX processors, held the top position on the Top500 list between June 2020 and June 2022, currently sitting in the fourth position. The recently released 7th generation of Amazon EC2 instances for compute-intensive workloads (C7 g) is also powered by Arm Graviton3 processors. Projects like European Mont-Blanc and U.S. DOE/NNSA Astra are further examples of Arm irruption in HPC. In parallel, over the last decade, the rapid improvement of genomic sequencing technologies and the exponential growth of sequencing data has placed a significant bottleneck on the computational side. While most genomics applications have been thoroughly tested and optimized for x86 systems, just a few are prepared to perform efficiently on Arm machines. Moreover, these applications do not exploit the newly introduced Scalable Vector Extensions (SVE).This paper presents GenArchBench, the first genome analysis benchmark suite targeting Arm architectures. We have selected computationally demanding kernels from the most widely used tools in genome data analysis and ported them to Arm-based A64FX and Graviton3 processors. Overall, the GenArch benchmark suite comprises 13 multi-core kernels from critical stages of widely-used genome analysis pipelines, including base-calling, read mapping, variant calling, and genome assembly. Our benchmark suite includes different input data sets per kernel (small and large), each with a corresponding regression test to verify the correctness of each execution automatically. Moreover, the porting features the usage of the novel Arm SVE instructions, algorithmic and code optimizations, and the exploitation of Arm-optimized libraries. We present the optimizations implemented in each kernel and a detailed performance evaluation and comparison of their performance on four different HPC machines (i.e., A64FX, Graviton3, Intel Xeon Skylake Platinum, and AMD EPYC Rome). Overall, the experimental evaluation shows that Graviton3 outperforms other machines on average. Moreover, we observed that the performance of the A64FX is significantly constrained by its small memory hierarchy and latencies. Additionally, as proof of concept, we study the performance of a production-ready tool that exploits two of the ported and optimized genomic kernels.

Unlocking Secrets: Bioinformatics' Impact on Forensic Bio-Examinations

Bioinformatics (BI) is a diverse and interdisciplinary domain that studies biological data through computational techniques. Its rapid technological progress has led to a significant increase in biological data production, sparking a notable expansion in BI research and application. In recent years, the convergence between BI and forensic science has become increasingly evident, with the growing use of BI tools and techniques in forensic investigations. BI is not only extensively used in biomedical research to analyse biological data and forecast gene regulation but also in the development of drugs targeting disorders linked to epigenetic alterations. It merges principles from various fields like biology, computer science, and statistics to process extensive omics data, create databases, predict structures, and model metabolic processes. The impact of BI on biomedical research is substantial, aiding in data processing, prediction, and visualisation. Moreover, BI is increasingly important in analysing data generated through Sanger-based electrophoresis methods and massively parallel sequencing in forensic DNA analysis, demonstrating its practical application in the field. Therefore, BI plays a critical role in forensic identification, searching databases side by side, forensic DNA phenotyping and biographic ancestry, forensic genetic genealogy, and forensic epigenomics. An overview of different bioinformatic methodologies employed in forensic applications, exploring approaches for managing data and investigating trend analysis strategies within forensic contexts, is discussed. Additionally, it investigates data management strategies. The primary objective was to explore how BI facilitates forensic BI's use, integration, and impact. Utilising a systematic literature review and content analysis, the study examined peer-reviewed articles, conference proceedings, and reports from critical databases like Google Scholar and Scopus, focusing on publications from 2010 to 2024. Key findings highlight the significant advancements in genomic sequencing technologies and their application in personalised therapeutics. Forensic scientists can improve forensic exams' accuracy, reliability, and efficiency by using BI tools, metrics, and efficient data management procedures.

Assembly collapsing versus heterozygosity oversizing: detection of homokaryotic and heterokaryotic Laccaria trichodermophora strains by hybrid genome assembly.

Genome assembly and annotation using short-paired reads is challenging for eukaryotic organisms due to their large size, variable ploidy and large number of repetitive elements. However, the use of single-molecule long reads improves assembly quality (completeness and contiguity), but haplotype duplications still pose assembly challenges. To address the effect of read length on genome assembly quality, gene prediction and annotation, we compared genome assemblers and sequencing technologies with four strains of the ectomycorrhizal fungus Laccaria trichodermophora. By analysing the predicted repertoire of carbohydrate enzymes, we investigated the effects of assembly quality on functional inferences. Libraries were generated using three different sequencing platforms (Illumina Next-Seq, Mi-Seq and PacBio Sequel), and genomes were assembled using single and hybrid assemblies/libraries. Long reads or hybrid assemby resolved the collapsing of repeated regions, but the nuclear heterozygous versions remained unresolved. In dikaryotic fungi, each cell includes two nuclei and each nucleus has differences not only in allelic gene version but also in gene composition and synteny. These heterokaryotic cells produce fragmentation and size overestimation of the genome assembly of each nucleus. Hybrid assembly revealed a wider functional diversity of genomes. Here, several predicted oxidizing activities on glycosyl residues of oligosaccharides and several chitooligosaccharide acetylase activities would have passed unnoticed in short-read assemblies. Also, the size and fragmentation of the genome assembly, in combination with heterozygosity analysis, allowed us to distinguish homokaryotic and heterokaryotic strains isolated from L. trichodermophora fruit bodies.

The Impact of Innovative Research Methods for Enhancing Agricultural Plants for Sustainable Development in The Future

In the pursuit of sustainable agricultural practices, the role of innovative research methods in enhancing agricultural plants is paramount. This abstract delineates the profound impact of such methodologies on sustainable development in the foreseeable future. Cutting-edge techniques such as genomic sequencing, molecular markers, and genome editing technologies revolutionize plant breeding by identifying and manipulating genes responsible for desirable traits, including disease resistance and drought tolerance. Omics technologies, encompassing transcriptomics, proteomics, and metabolomics, provide comprehensive insights into gene expression patterns and metabolic pathways, thereby facilitating targeted breeding strategies aimed at enhancing plant resilience and productivity. High-throughput phenotyping platforms enable rapid and accurate characterization of plant traits, empowering breeders to select superior varieties with enhanced performance under diverse environmental conditions. Concurrently, bioinformatics tools and data analytics play a pivotal role in deciphering complex genomic datasets, aiding in the identification of key genes and regulatory networks governing important agronomic traits. Biotechnology and synthetic biology approaches offer novel avenues for genetic manipulation, allowing for the design and engineering of plants with optimized traits, such as improved nutritional content and stress tolerance. Additionally, the integration of climate modeling with systems biology provides valuable insights into the interaction between genotype and environment, facilitating the development of climate-resilient crop varieties tailored to specific agroecosystems. Through the synergistic application of these innovative research methods, agricultural scientists and breeders can accelerate the pace of crop improvement while simultaneously promoting sustainability by fostering resilience, resource efficiency, and environmental stewardship. This transformative paradigm in agricultural research promises to address the complex challenges of food security, climate change, and environmental degradation, thereby laying the foundation for a more sustainable and resilient agricultural future.

How Do I Report Genes in a Paper?

Genetic testing, including whole genome, whole exome, and other next-generation sequencing technologies, has evolved vastly in the past decade. With this, the number of identified genes and genetic variants is constantly increasing. Although a variety of databases and online tools exist that summarize, categorize, and classify genes, a clear guideline of which information is needed when reporting a gene and what to do when identifying a new gene is lacking. This includes the correct nomenclature, descriptive information about genetic loci and genetic variation, aliases, and correlated phenotypes. This tutorial is meant to serve as an introduction to reporting genes in a paper and provides an overview of available databases and tools to obtain all necessary information on the genes of interest.

Implementation of a high-throughput whole genome sequencing approach with the goal of maximizing efficiency and cost effectiveness to improve public health.

Public Health Laboratories that implement whole genome sequencing (WGS) technologies may struggle to find the balance between sample volume and cost effectiveness. We present a method that allows for sequencing of a variety of bacterial isolates in a cost-effective manner. This report provides specific strategies to implement high-volume WGS, including an innovative, low-cost solution utilizing a novel quarter volume sequencing library preparation. The methods described support the use of high-throughput DNA extraction and WGS within budgetary constraints, strengthening public health responses to outbreaks and disease surveillance.

Emerging roles and mechanisms of lncRNAs in fruit and vegetables

Abstract With the development of genome sequencing technologies, many long non-coding RNAs (lncRNAs) have been identified in fruit and vegetables. LncRNAs are primarily transcribed and spliced by RNA polymerase II (Pol II) or plant-specific Pol IV/V, and exhibit limited evolutionary conservation. LncRNAs intricately regulate various aspects of fruit and vegetables, including pigment accumulation, reproductive tissue development, fruit ripening, and responses to biotic and abiotic stresses, through diverse mechanisms such as gene expression modulation, interaction with hormones and transcription factors, microRNAs regulation, and involvement in alternative splicing. This review presents a comprehensive overview of lncRNAs classification, basic characteristics, and most importantly, recent advances in understanding their functions and regulatory mechanisms.

Antimicrobial resistance survey and whole-genome analysis of nosocomial P. Aeruginosa isolated from eastern Province of China in 2016–2021

BackgroundPseudomonas aeruginosa is a major Gram-negative pathogen that can exacerbate lung infections in the patients with cystic fibrosis, which can ultimately lead to death.MethodsFrom 2016 to 2021, 103 strains of P. aeruginosa were isolated from hospitals and 20 antibiotics were used for antimicrobial susceptibility determination. Using next-generation genome sequencing technology, these strains were sequenced and analyzed in terms of serotypes, ST types, and resistance genes for epidemiological investigation.ResultsThe age distribution of patients ranged from 10 days to 94 years with a median age of 69 years old. The strains were mainly isolated from sputum (72 strains, 69.9%) and blood (14 strains, 13.6%). The size of these genomes ranged from 6.2 Mb to 7.4 Mb, with a mean value of 6.5 Mb. In addition to eight antibiotics that show inherent resistance to P. aeruginosa, the sensitivity rates for colistin, amikacin, gentamicin, ceftazidime, piperacillin, piperacillin-tazobactam, ciprofloxacin, meropenem, aztreonam, imipenem, cefepime and levofloxacin were 100%, 95.15%, 86.41%, 72.82%, 71.84%, 69.90%, 55.34%, 52.43%, 50.49%, 50.49%, 49.51% and 47.57% respectively, and the carriage rate of MDR strains was 30.69% (31/101). Whole-genome analysis showed that a total of 50 ST types were identified, with ST244 (5/103) and ST1076 (4/103) having a more pronounced distribution advantage. Serotype predictions showed that O6 accounted for 29.13% (30/103), O11 for 23.30% (24/103), O2 for 18.45% (19/103), and O1 for 11.65% (12/103) of the highest proportions. Notably, we found a significantly higher proportion of ExoU in P. aeruginosa strains of serotype O11 than in other cytotoxic exoenzyme positive strains. In addition to this, a total of 47 crpP genes that mediate resistance to fluoroquinolones antibiotics were found distributed on 43 P. aeruginosa strains, and 10 new variants of CrpP were identified, named 1.33, 1.34, 1.35, 1.36, 1.37, 1.38, 1.39, 1.40, 1.41 and 7.1.ConclusionsWe investigated the antibiotic susceptibility of clinical isolates of P. aeruginosa and genomically enriched the diversity of P. aeruginosa for its prophylactic and therapeutic value.

A Review: Multi-Omics Approach to Studying the Association between Ionizing Radiation Effects on Biological Aging.

Multi-omics studies have emerged as powerful tools for tailoring individualized responses to various conditions, capitalizing on genome sequencing technologies' increasing affordability and efficiency. This paper delves into the potential of multi-omics in deepening our understanding of biological age, examining the techniques available in light of evolving technology and computational models. The primary objective is to review the relationship between ionizing radiation and biological age, exploring a wide array of functional, physiological, and psychological parameters. This comprehensive review draws upon an extensive range of sources, including peer-reviewed journal articles, government documents, and reputable websites. The literature review spans from fundamental insights into radiation effects to the latest developments in aging research. Ionizing radiation exerts its influence through direct mechanisms, notably single- and double-strand DNA breaks and cross links, along with other critical cellular events. The cumulative impact of DNA damage forms the foundation for the intricate process of natural aging, intersecting with numerous diseases and pivotal biomarkers. Furthermore, there is a resurgence of interest in ionizing radiation research from various organizations and countries, reinvigorating its importance as a key contributor to the study of biological age. Biological age serves as a vital reference point for the monitoring and mitigation of the effects of various stressors, including ionizing radiation. Ionizing radiation emerges as a potent candidate for modeling the separation of biological age from chronological age, offering a promising avenue for tailoring protocols across diverse fields, including the rigorous demands of space exploration.

Use of a Candida albicans SC5314 PacBio HiFi reads dataset to close gaps in the reference genome assembly, reveal a subtelomeric gene family, and produce accurate phased allelic sequences.

Candida albicans SC5314 is the most-often used strain for molecular manipulation of the species. The SC5314 reference genome sequence is the result of considerable effort from many scientists and has advanced research into fungal biology and pathogenesis. Although the resource is highly developed and presented in a phased diploid format, the sequence includes gaps and does not extend to the telomeres on its eight chromosome pairs. Accurate SC5314 genome assembly is complicated by the presence of extensive repeated sequences and considerable allelic length variation at some loci. Advances in genome sequencing technology provide the tools to obtain highly accurate long-read data that span even the most-difficult-to-assemble genome regions. Here, we describe derivation of a PacBio HiFi data set and creation of a collapsed haploid telomere-to-telomere assembly of the SC5314 genome (ASM3268872v1) that revealed previously unknown features of the strain. ASM3268872v1 subtelomeric distances were up to 19 kb larger than in the reference genome and revealed a family of highly conserved DNA helicase-encoding genes at 10 of the 16 chromosome ends. We also describe alignments of individual HiFi reads to deduce accurate diploid sequences for the most notoriously difficult-to-assemble C. albicans genes: the agglutinin-like sequence (ALS) gene family. We provide a tutorial that demonstrates how the HiFi reads can be visualized to explore any region of interest. Availability of the HiFi reads data set and the ASM3268872v1 comparative guide assembly will streamline research efforts because accurate diploid sequences can be derived using simple in silico methods rather than time-consuming laboratory-bench approaches.

Bioinformatics and personalized medicine in the U.S.: A comprehensive review: Scrutinizing the advancements in genomics and their potential to revolutionize healthcare delivery

This study provides a comprehensive review of the role of bioinformatics in personalized medicine, particularly focusing on its integration with genomics and the consequent revolution in healthcare delivery. The primary objective was to explore how bioinformatics facilitates the transition from traditional healthcare practices to more personalized, predictive, and patient-centered medical treatments. Utilizing a systematic literature review and content analysis, the study examined peer-reviewed articles, conference proceedings, and reports from key databases like PubMed, Web of Science, and Scopus, focusing on publications from 2010 to 2023. Key findings highlight the significant advancements in genomic sequencing technologies and their application in personalized therapeutics. The integration of bioinformatics tools in healthcare systems has shown a substantial impact on patient care, emphasizing the importance of ethical and economic considerations, data privacy, and security. The study also revealed the crucial role of collaborations and partnerships within the industry and healthcare systems for the advancement of personalized medicine. The future landscape of genomic medicine presents both opportunities and challenges, particularly in disease prediction, prevention, and treatment. The study concludes with strategic recommendations for healthcare practitioners and policymakers, emphasizing the need for continuous education, robust policy frameworks, and equitable access to personalized medicine. Future research directions include the development of advanced bioinformatics tools, exploration of ethical implications, and understanding barriers to clinical integration. This study underscores the transformative potential of bioinformatics in revolutionizing healthcare delivery and patient care.

Leveraging machine learning for taxonomic classification of emerging astroviruses.

Astroviruses are a family of genetically diverse viruses associated with disease in humans and birds with significant health effects and economic burdens. Astrovirus taxonomic classification includes two genera, Avastrovirus and Mamastrovirus. However, with next-generation sequencing, broader interspecies transmission has been observed necessitating a reexamination of the current host-based taxonomic classification approach. In this study, a novel taxonomic classification method is presented for emergent and as yet unclassified astroviruses, based on whole genome sequence k-mer composition in addition to host information. An optional component responsible for identifying recombinant sequences was added to the method's pipeline, to counteract the impact of genetic recombination on viral classification. The proposed three-pronged classification method consists of a supervised machine learning method, an unsupervised machine learning method, and the consideration of host species. Using this three-pronged approach, we propose genus labels for 191 as yet unclassified astrovirus genomes. Genus labels are also suggested for an additional eight as yet unclassified astrovirus genomes for which incompatibility was observed with the host species, suggesting cross-species infection. Lastly, our machine learning-based approach augmented by a principal component analysis (PCA) analysis provides evidence supporting the hypothesis of the existence of human astrovirus (HAstV) subgenus of the genus Mamastrovirus, and a goose astrovirus (GoAstV) subgenus of the genus Avastrovirus. Overall, this multipronged machine learning approach provides a fast, reliable, and scalable prediction method of taxonomic labels, able to keep pace with emerging viruses and the exponential increase in the output of modern genome sequencing technologies.

A fresh perspective on infrared spectroscopy as a prescreening method for molecular and stable isotopes analyses on ancient human bones

Following the development of modern genome sequencing technologies, the investigation of museum osteological finds is increasingly informative and popular. Viable protocols to help preserve these collections from exceedingly invasive analyses, would allow greater access to the specimens for scientific research. The main aim of this work is to survey skeletal tissues, specifically petrous bones and roots of teeth, using infrared spectroscopy as a prescreening method to assess the bone quality for molecular analyses. This approach could overcome the major problem of identifying useful genetic material in archaeological bone collections without resorting to demanding, time consuming and expensive laboratory studies. A minimally invasive sampling of archaeological bones was developed and bone structural and compositional changes were examined, linking isotopic and genetic data to infrared spectra. The predictive model based on Infrared parameters is effective in determining the occurrence of ancient DNA (aDNA); however, the quality/quantity of aDNA cannot be determined because of the influence of environmental and local factors experienced by the examined bones during the burial period.

Joint inference of clonal structure using single-cell genome and transcriptome sequencing data.

Latest advancements in the high-throughput single-cell genome (scDNA) and transcriptome (scRNA) sequencing technologies enabled cell-resolved investigation of tissue clones. However, it remains challenging to cluster and couple single cells for heterogeneous scRNA and scDNA data generated from the same specimen. In this study, we present a computational framework called CCNMF, which employs a novel Coupled-Clone Non-negative Matrix Factorization technique to jointly infer clonal structure for matched scDNA and scRNA data. CCNMF couples multi-omics single cells by linking copy number and gene expression profiles through their general concordance. It successfully resolved the underlying coexisting clones with high correlations between the clonal genome and transcriptome from the same specimen. We validated that CCNMF can achieve high accuracy and robustness using both simulated benchmarks and real-world applications, including an ovarian cancer cell lines mixture, a gastric cancer cell line, and a primary gastric cancer. In summary, CCNMF provides a powerful tool for integrating multi-omics single-cell data, enabling simultaneous resolution of genomic and transcriptomic clonal architecture. This computational framework facilitates the understanding of how cellular gene expression changes in conjunction with clonal genome alternations, shedding light on the cellular genomic difference of subclones that contributes to tumor evolution.

Genetic predisposition to differentiated thyroid cancer in the Polish population.

Genome sequencing technologies reveal molecular mechanisms of differentiated thyroid cancer (DTC). Unlike somatic mutation analysis from thyroidectomy samples, germline mutations showing genetic susceptibility to DTC are less understood. The study aimed to assess the prevalence of germline mutations predisposing to DTC in a cohort of Polish individuals based on their whole genome sequencing data. We analyzed sequencing data from 1076 unrelated individuals totaling over 1018 billion read pairs and yielding an average 35.26 × read depth per genome, released openly for academic and clinical research as the Thousand Polish Genomes database (https://1000polishgenomes.com). The list of genes chosen for further analysis was based on the review of previous studies. The cohort contained 104 variants located within the coding and noncoding DNA sequences of 90 genes selected by ClinVar classification as pathogenic and potentially pathogenic. The frequency of variants in the Polish cohort was compared with the frequency estimated for the non‑Finnish European population obtained from the gnomAD database (gnomad.broadinstitute.org). Significant differences in variant frequency were found for the APC, ARSB, ATM, BRCA1, CHEK2, DICER1, GPD1L, INSR, KCNJ10, MYH9, PALB2, PLCB1, PLEKHG5, PTEN, RET, SEC23B, SERPINA1, SLC26A4, SMAD3, STK11, TERT, TOE1, and WRN genes. Even though the Polish population is genetically similar to the other European populations, there are significant differences in variant frequencies contributing to the disease development and progression, such as those in the RET, CHEK2, BRCA1, SLC26A4, or TERT genes. Further studies are needed to identify genomic variants associated directly with DTC.

Genome-scale metabolic models in translational medicine: the current status and potential of machine learning in improving the effectiveness of the models.

The genome-scale metabolic model (GEM) has emerged as one of the leading modeling approaches for systems-level metabolic studies and has been widely explored for a broad range of organisms and applications. Owing to the development of genome sequencing technologies and available biochemical data, it is possible to reconstruct GEMs for model and non-model microorganisms as well as for multicellular organisms such as humans and animal models. GEMs will evolve in parallel with the availability of biological data, new mathematical modeling techniques and the development of automated GEM reconstruction tools. The use of high-quality, context-specific GEMs, a subset of the original GEM in which inactive reactions are removed while maintaining metabolic functions in the extracted model, for model organisms along with machine learning (ML) techniques could increase their applications and effectiveness in translational research in the near future. Here, we briefly review the current state of GEMs, discuss the potential contributions of ML approaches for more efficient and frequent application of these models in translational research, and explore the extension of GEMs to integrative cellular models.

The Impact of Genomic Sequencing on Veterinary Diagnostics

Genomic sequencing has revolutionized veterinary diagnostics by providing a comprehensive understanding of an animal's genetic makeup and its implications for health and disease. By unraveling genetic variations and disease markers, genomic sequencing enables precise and early diagnoses of inherited diseases, infectious diseases, and cancer in animals. It empowers veterinarians to tailor treatment approaches based on an animal's genomic data, including targeted therapies and personalized medicine. Genomic sequencing also enhances disease surveillance, aids in breeding programs, and improves disease prevention strategies. However, the use of genomic sequencing in veterinary diagnostics raises ethical considerations, such as informed consent, privacy, and data ownership. Challenges include data privacy and security, interpretation of complex genomic data, and ensuring accessibility to genomic sequencing technologies. Emerging trends, including expanded genomic databases, integration of multi-omics approaches, and the implementation of artificial intelligence and machine learning, offer new opportunities for improved diagnoses and treatment outcomes. In conclusion, genomic sequencing has a profound impact on veterinary diagnostics, enabling precise diagnoses, personalized treatment approaches, and advancements in breeding programs and disease prevention.

An update and frequency distribution of Y chromosome haplogroups in modern Japanese males.

Japanese males belong to the Y chromosome C1a1, C2, D1a2a, D1a2a-12f2b, O1b2, O1b2a1a1, O2a2b1, and O2a1b haplogroups. Notably, the regional frequency of each haplogroup is homogeneous. Owing to recent developments in genome sequencing technology, the phylogenetic tree of Y chromosome haplogroups is updated annually. Therefore, in this study, we aimed to provide an update on the Y chromosome haplogroups of modern Japanese males and examine their regional distributions. Using 1,640 samples of Japanese males from seven Japanese cities (Nagasaki, Fukuoka, Tokushima, Osaka, Kanazawa, Kawasaki, and Sapporo), haplogroups C1a1, C2, D1a2a, D1a2a-12f2b, O1b2, and O1b2a1a1 were updated based on the latest phylogenetic tree. Haplogroup C1a1 was mainly classified into C1a1a1a and C1a1a1b subgroups; C1a1a1b was more common in Tokushima and Osaka than in the other regions. Haplogroup C2 was mainly classified into C2a, C2b1a1a, C2b1a1b, C2b1a2, and C2b1b subgroups and exhibited frequency differences in Osaka. Haplogroup D1a2a was classified into D1a2a1c1 and D1a2a2 subgroups, and its frequency varied between Tokushima and Osaka. Haplogroup D1a2a-12f2b was classified into D1a2a1a2b1a1a and D1a2a1a3 subgroups; however, no significant frequency differences were observed. Haplogroup O1b2 was classified into O1b2a1a2a1a, O1b2a1a2a1b, and O1b2a1a3 subgroups, with frequency differences between Nagasaki and Kanazawa. Haplogroup O1b2a1a1 was mainly classified into O1b2a1a1a, O1b2a1a1b, and O1b2a1a1c subgroups; however, no significant frequency differences were observed. Our findings suggest that gene flow in the Kinki region is caused by human migration.