Most non-randomised controlled trials are unable to establish clear causal relationships in chronic obstructive pulmonary disease (COPD) due to the presence of confounding factors. This review summarises the evidence that the Mendelian randomisation method can be a powerful tool for performing causal inferences in COPD.

A non-systematic search of English-language scientific literature was performed on PubMed using the following keywords: 'Mendelian randomisation', 'COPD', 'lung function', and 'GWAS'. No date restrictions were applied. The types of articles selected included randomised controlled trials, cohort studies, observational studies, and reviews.

Mendelian randomisation is becoming an increasingly popular method for identifying the risk factors of COPD. Recent Mendelian randomisation studies have revealed some risk factors for COPD, such as club cell secretory protein-16, impaired kidney function, air pollutants, asthma, and depression. In addition, Mendelian randomisation results suggest that genetically predicted factors such as PM2.5, inflammatory cytokines, growth differentiation factor 15, docosahexaenoic acid, and testosterone may have causal relationships with lung function.

Mendelian randomisation is a robust method for performing causal inferences in COPD research as it reduces the impact of confounding factors.

Numerous studies have accelerated the knowledge expansion on the role of gut microbiota in inflammatory bowel disease (IBD). However, the precise mechanisms behind host-microbe cross-talk remain largely undefined, due to the complexity of the human intestinal ecosystem and multiple external factors. In this review, we introduce the interactome concept to systematically summarize how intestinal dysbiosis is involved in IBD pathogenesis in terms of microbial composition, functionality, genomic structure, transcriptional activity, and downstream proteins and metabolites. Meanwhile, this review also aims to present an updated overview of the relevant mechanisms, high-throughput multi-omics methodologies, different types of multi-omics cohort resources, and computational methods used to understand host-microbiota interactions in the context of IBD. Finally, we discuss the challenges pertaining to the integration of multi-omics data in order to reveal host-microbiota cross-talk and offer insights into relevant future research directions.

The Taiwan Strait is located between Fujian and Taiwan Provinces, China, and is susceptible to various currents and human activities, which result in frequent algal blooms. Surface water samples were collected in the Taiwan Strait in the spring of 2022, and phytoplankton community structure was assessed by both microscopic and metabarcoding analyses. Diatoms dominated in phytoplankton community based on microscopy, while dinoflagellates dominated in the eukaryotic phytoplankton based on metabarcoding. Water temperature was the main environmental factor affecting the phytoplankton community structure. A total of 133 algal bloom (AB) species, including 32 harmful algal bloom (HAB) species, were detected via the combination of both methods. Scrippsiella acuminata and Noctiluca scintillans were the dominant AB species. N. scintillans occurs abundantly in shallow coastal waters with low temperatures, low salinities and high nutrient levels. The distribution of AB species in our study is consistent with historical algal blooms in the Taiwan Strait.

Runs of homozygosity (ROHs) are widely observed across the genomes of various species and have been reported to be associated with many traits and common diseases, as well as rare recessive diseases, in human populations. Although single nucleotide polymorphism (SNP) array data have been used in previous studies on ROHs, recent advances in whole-genome sequencing (WGS) technologies and the development of nationwide cohorts/biobanks are making high-density genomic data increasingly available, and it is consequently becoming more feasible to detect ROHs at higher resolution. In the study, we searched for ROHs in two high-coverage WGS datasets from 3552 Japanese individuals and 192 three-generation families (consisting of 1120 family members) in prospective genomic cohorts. The results showed that a considerable number of ROHs, especially short ones that may have remained undetected in conventionally used SNP-array data, can be detected in the WGS data. By filtering out sequencing errors and leveraging pedigree information, longer ROHs are more likely to be detected in WGS data than in SNP-array data. Additionally, we identified gene families within ROH islands that are associated with enriched pathways related to sensory perception of taste and odors, suggesting potential signatures of selection in these key genomic regions.

Tumour protein p53 (TP53) is a tumour suppressor gene that is frequently mutated in cancers. Traditional TP53 detection methods, such as polymerase chain reactions, are time-consuming and demand skilled laboratory personnel. As an alternative, in the current study, we have demonstrated a high resistivity silicon (HR-Si) based conductometric biosensor designed for the rapid and specific identification of TP53 point mutations directly at the point-of-need. This biosensor accurately detected R248Q and R248W point mutant single strand DNA (ssDNA) as models, in real-time. Both R248Q and R248W mutant ssDNA exhibited a limit of detection (LOD) of 0.5 ng/mL in human plasma. The selectivity studies revealed that both R248Q and R248W mutant ssDNA can be detected 10 × lower molar content against their wild-type ssDNA. Validation of the sensor using clinical samples harbouring known TP53 mutations demonstrated a sensitivity of 100%, a specificity of 100%, and a LOD of 2.5 ng/mL. This precision biosensing platform at the point-of-need has the potential to revolutionise cancer diagnostics.

While the size of chromosomes can be measured under a microscope, obtaining the exact size of a genome remains a challenge. Biochemical methods and k-mer distribution-based approaches allow only estimations. An alternative approach to estimate the genome size based on high contiguity assemblies and read mappings is presented here. Analyses of Arabidopsis thaliana and Beta vulgaris data sets are presented to show the impact of different parameters. Oryza sativa, Brachypodium distachyon, Solanum lycopersicum, Vitis vinifera, and Zea mays were also analyzed to demonstrate the broad applicability of this approach. Further, MGSE was also used to analyze Escherichia coli, Saccharomyces cerevisiae, and Caenorhabditis elegans datasets to show its utility beyond plants. Mapping-based Genome Size Estimation (MGSE) and additional scripts are available on GitHub: https://github.com/bpucker/MGSE . MGSE predicts genome sizes based on short reads or long reads requiring a minimal coverage of 5-fold.

Synthetic genetic materials, particularly those in genetically modified organisms (GMOs) deployed into complex environments, necessitate robust postmarket surveillance for continuous monitoring of both the materials and their applications throughout their lifecycle. Here, we introduce novel-coded genomic material for a blind mutation test that evaluates mutagenesis in synthetic genomic sequences without requiring direct sequence comparison. This test utilizes a Genome-Digest, which is embedded within essential genes, establishing mathematical correlation between the nucleotide sequence and codon order. This novel design allows for independent assessment of mutations by decoding the nucleotide sequence, thereby eliminating the need for reference sequences or extensive bioinformatic analysis. Furthermore, the test has the capability to analyze mixed genomic materials from a single sample and can be extended to the pooled testing of multiple samples as well. Building on this framework, we propose the 'Genome-ShockWatch' methodology. In proof-of-concept trials, it successfully detected mutations that exceeded a predefined threshold in long-read sequencing data from a yogurt sample containing Genome-Digest encoded Nissle 1917 E. coli cells and naturally occurring probiotic bacteria. Consequently, the Genome-Digest system provides a robust foundation for the routine surveillance and management of GMOs and related synthetic products, ensuring their safety and efficacy in diverse environmental contexts.

The resources required to study gene function are limited, especially when considering the number of genes in the human genome and the complexity of their function. Therefore, genes are prioritized for experimental studies based on many different considerations, including, but not limited to, perceived biomedical importance, such as disease-associated genes, or the understanding of biological processes, such as cell signalling pathways. At the same time, most genes are not studied or are under-characterized, which hampers our understanding of their function and potential effects on human health and wellness. Understanding function annotation disparity is a necessary first step toward understanding how much functional knowledge is gained from the human genome, and toward guidelines for better targeting future studies of the genes in the human genome effectively. Here, we present a comprehensive longitudinal analysis of the human proteome utilizing data analysis tools from economics and information theory. Specifically, we view the human proteome as a population of proteins within a knowledge economy: we treat the quantified knowledge of the protein's function as the analogue of wealth and examine the distribution of information in a population of proteins in the proteome in the same manner distribution of wealth is studied in societies. Our results show a highly skewed distribution of information about human proteins over the last decade, in which the inequality in the annotations given to the proteins remains high. Additionally, we examine the correlation between the knowledge about protein function as captured in databases and the interest in proteins as reflected by mentions in the scientific literature. We show a large gap between knowledge and interest and dissect the factors leading to this gap. In conclusion, our study shows that research efforts should be redirected to less studied proteins to mitigate the disparity among human proteins both in databases and literature.

For steady transgenic expression, lentiviral vector-mediated gene delivery is a commonly used technique. One question that needs to be explored is how external lentiviral vectors and overexpressed genes perturb cellular homeostasis, potentially altering transcriptional networks. In this study, two Human Embryonic Kidney 293T (HEK293T)-derived cell lines were established via lentiviral transduction, one overexpressing green fluorescent protein (GFP) and the other co-overexpressing GFP and ADORA3 following puromycin selection to ensure stable genomic integration. Genes with differentially transcript utilization (gDTUs) and differentially expressed genes (DEGs) across cell lines were identified after short-read and long-read RNA-seq. Only 31 genes were discovered to have changed in expression when GFP was expressed, although hundreds of genes showed variations in transcript use. In contrast, even when co-overexpression of GFP and ADORA3 alters the expression of more than 1000 genes, there are still less than 1000 gDTUs. Moreover, DEGs linked to ADORA3 overexpression play a major role in RNA splicing, whereas gDTUs are highly linked to a number of malignancies and the molecular mechanisms that underlie them. For the analysis of gene expression data from stable cell lines derived from HEK293T, our findings provide important insights into changes in gene expression and alternative splicing.

As gene editing technologies enter the clinic, state-of-the-art characterization methods have been developed in parallel to assess the components of these paradigm-shifting medicines. One such component, the guide RNA (gRNA) element of CRISPR-based drugs, is a large synthetic heavily modified oligonucleotide that programs for the desired gene edit. Conventional oligonucleotide sequencing technologies can inform gRNA composition, but these methods may not completely capture the chemical modifications that are introduced during synthesis. Circumventing these challenges, mass spectrometry has demonstrated use in oligonucleotide analyses and has been combined here with ion mobility to deepen its characterization power. The use of ion mobility enabled us to perform gas-phase separation of the fragment ions produced by top-down mass spectrometry, yielding a significant increase in fragment identifications for a highly modified 100-mer gRNA by uncovering high-confidence assignments for heavily modified regions and for the important spacer region. Furthermore, the high-confidence fragment assignments empowered simultaneous de novo sequencing and chemical modification localization for the 5'-end spacer region as well as for 15 nucleotides on the heavily modified 3'-end. Overall, a total sequence coverage of 95% was achieved for the heavily modified 100-mer, ushering near complete sequence and chemical modification confirmation by top-down mass spectrometry.

Nearly 10,000 plant species are represented by RNA-seq datasets in the NCBI sequence read archive, which are difficult to search in unassembled format due to database size. Here, we optimize RNA-seq assembly to transform most of this public RNA-seq data to a searchable database for biosynthetic gene discovery. We test our transcriptome mining pipeline towards the diversification of moroidins, which are plant ribosomally-synthesized and posttranslationally-modified peptides (RiPPs) biosynthesized from copper-dependent peptide cyclases. Moroidins are bicyclic compounds with a conserved stephanotic acid scaffold, which becomes cytotoxic to non-small cell lung adenocarcinoma cells with an additional C-terminal macrocycle. We discover moroidin analogs with second ring structures diversified at the crosslink and the non-crosslinked residues including a moroidin analog from water chickweed, which exhibits higher cytotoxicity against lung adenocarcinoma cells than moroidin. Our study expands stephanotic acid-type peptides to grasses, Lowiaceae, mints, pinks, and spurges while demonstrating that large-scale transcriptome mining can broaden the medicinal chemistry toolbox for chemical and biological exploration of eukaryotic RiPP lead structures.

To explore the application value of metagenomic next-generation sequencing (mNGS) and targeted next-generation sequencing (tNGS) in the diagnosis of pulmonary tuberculosis (PTB) in bronchoalveolar lavage fluid (BALF).

Data from 202 patients with suspected PTB at Wuhan Central Hospital (Jan 2022 - Jan 2024) were retrospectively analyzed. BALF samples were collected and examined using mNGS and tNGS, comparing their sensitivity to traditional methods like acid-fast staining, TB culture, and TB-DNA. Mixed microbial species were identified from the BALF using mNGS and tNGS, and the pros and cons of tNGS were evaluated against mNGS.

Of the 202 patients evaluated, 94 were diagnosed with PTB. The BALF mNGS and tNGS exhibited a sensitivity of 77.66% and a specificity of 100%, with positive and negative predictive values of 100% and 83.72%, respectively, outperforming conventional diagnostic methods. It was possible to compare the AUC values of the ROC curves of the BALF mNGS and tNGS with the corresponding values of the other three assay methods (0.89 vs 0.56, p < 0.05), MTB culture (0.89 vs 0.71, p < 0.05), and TB-DNA (0.89 vs 0.68, p < 0.05). Additionally, these techniques identified mixed microbial species in 52.13% of the BALF samples. Although both mNGS and tNGS demonstrated similar diagnostic rates, tNGS proved to be faster, more cost-effective, and incorporated a tuberculosis-specific wall-breaking technology, thereby suggesting greater clinical utility.

BALF mNGS and tNGS technologies quickly and accurately detect PTB patients with greater sensitivity and specificity than traditional MTB methods. While both mNGS and tNGS demonstrate enhanced capacity for polymicrobial detection, the clinical significance of co-detected microorganisms requires integration with clinical context to differentiate colonization from active infection. Compared to mNGS, tNGS provides distinct advantages in clinical utility.

Hidden messages in DNA molecules by employing chemical modifications has been suggested for private data storage and transmission at high information density. However, rapidly decoding these "molecular keys" with corresponding basecallers remains challenging. We present DeepSME, a nanopore sequencing and deep-learning based framework towards single-molecule encryption, demonstrated by using 5-hydroxymethylcytosine (5hmC) substitution for individual nucleotide recognition rather than sequential interactions. This non-natural, motif-insensitive methylation disrupts ion current, resulting in a readout failure of 67.2%-100%, concealing the privacy within the DNAs. We further develop an alignment-free DeepSME basecaller as a key to reconstitute the digital information. Our three-stage training pipeline, expands k-mer size from 46 to 49, achieving over 92% precision and recall from scratch. DeepSME deciphers fully 5hmC concealed text and image within 16× coverage depth with an F1-score of 86.4%, surpassing all the state-of-the-art basecallers. Demonstrated on edge computing devices, DeepSME holds supreme potential for DNA-based private communications and broader bioengineering and medical applications.

This study achieved cancer type and survival time prediction by transforming transcriptomic features into feature maps and employing deep learning models. Using transcriptomic data from 27 cancer types and survival data from 10 types in the TCGA database, a pan-cancer transcriptomic feature map was constructed through data cleaning, feature extraction, and visualization. Using Inception network and gated convolutional modules yielded a pan-cancer classification accuracy of 91.8 %. Additionally, by extracting 31 differential genes from different cancer feature maps, an interaction network diagram was drawn, identifying two key genes, ANXA5 and ACTB. These genes are potential biomarkers related to cancer progression, angiogenesis, metastasis, and treatment resistance. Survival prediction analysis on 10 cancer types, combined with feature maps and data amplification, cancer survival prediction accuracy reached from 0.75 to 0.91. This transcriptomic feature map provides a novel approach for cancer omics analysis, to facilitate personalized treatments and reflecting individual differences.

Lymphovascular invasion (LVI) is a crucial step in metastasis and is closely associated with poor prognosis in patients with breast cancer. However, its clinical and molecular characteristics remain insufficiently defined. We aimed to identify molecular targets for LVI-positive (LVI+) breast cancer and predict patient prognosis via the analysis of genomic variations using targeted sequencing.

We established a large-scale targeted sequencing cohort of 4,079 breast cancer samples, which included 3,159 early-stage and locally advanced patients with available LVI statuses. Comparisons of somatic mutation frequencies and germline pathogenic/likely pathogenic (P/LP) mutation frequencies, mutational signature analyses, and mutual exclusivity and co-occurrence analyses were performed to identify key genomic features involved in LVI+ patients. Additionally, Kaplan-Meier survival analysis was conducted to further explore the prognostic value of co-mutations in LVI+ cases.

We observed that LVI+ patients with the hormone receptor-positive/human epidermal growth factor receptor 2-negative (HR+/HER2-) and triple-negative breast cancer (TNBC) subtypes exhibited worse disease-free survival. Notably, HR+/HER2- and HER2+ breast cancer patients with LVI displayed distinct genomic features compared with LVI- tumors. Specifically, LVI+ HR+/HER2- tumors exhibited greater frequencies of somatic mutations in TP53 and ESR1, germline BRCA2 P/LP variations, and an enrichment of clock-like single-base substitution (SBS)1 mutational signatures. In contrast, LVI+ HER2+ tumors demonstrated a higher incidence of somatic PIK3CA mutations and increased activity of the apolipoprotein B mRNA editing enzyme catalytic polypeptide (APOBEC)-associated SBS2 signature. Furthermore, we revealed that the co-mutation of TP53 and NF1 could serve as a potential prognostic marker for LVI+ HR+/HER2- patients.

Our findings provide a comprehensive overview of the genomic characteristics of LVI in breast cancer, thereby offering insights that may help in refining precision treatment strategies for LVI+ breast cancer patients.

Long-read sequencing (LRS) was introduced as the third generation of next-generation sequencing technologies with a high accuracy rate in genomic variant identification for some of its platforms. Due to the structural complexity of many pharmacogenes, the presence of rare variants, and the limitations of genotyping and short-read sequencing approaches in detecting pharmacovariants, LRS methods are likely to become increasingly utilized in the near future. In this review, we aim to provide a comprehensive discussion of current and future applications of long-read genotyping methods by introducing the opportunities and advantages as well as the challenges and disadvantages of state-of-the-art LRS platforms for the implementation of pharmacogenomic tests in clinical and research settings. New approaches to data processing, as well as the challenges and pitfalls of performing such tests in daily practice, will be explored in detail. We provide references to resources for those who are interested or intend to employ LRS in pharmacogenomics screening, both in clinical and research settings.

Glioma, particularly glioblastoma (GBM), remains a highly aggressive and challenging tumour, characterised by poor prognosis and limited therapeutic options. LSM2, an RNA-binding protein, has been implicated in tumour progression, yet its role in glioma remains underexplored. This study aims to investigate the expression, prognostic significance, and molecular mechanisms of LSM2 in glioma, focusing on its impact on RNA splicing regulation.

Clinical and transcriptomic data from 163 GBM and 518 lower-grade glioma (LGG) cases from The Cancer Genome Atlas (TCGA) were analysed to assess LSM2 expression and its prognostic value. RNA sequencing was performed on LSM2 knockdown in T98G glioblastoma cells to identify differentially expressed genes (DEGs) and alternative splicing events (ASEs). Bioinformatics tools were employed to perform functional enrichment analyses and construct protein-protein interaction (PPI) networks.

LSM2 expression was significantly elevated in gliomas, particularly in GBM and in tumours with 1p/19q non-deletion or IDH1 mutation (p < 0.001). High LSM2 expression was correlated with shorter overall survival (HR = 1.7, p = 0.01). Knockdown of LSM2 in T98G cells identified 728 upregulated and 1,720 downregulated genes, alongside 1,949 splicing alterations, which primarily affected pathways related to RNA metabolism, DNA damage response, and cell cycle regulation. Key hub genes such as TLN1, FN1, and IRF7 were associated with glioma progression and poor prognosis.

Our findings demonstrate that LSM2 plays a critical role in glioma progression through the regulation of RNA splicing dynamics. Elevated LSM2 expression serves as a prognostic biomarker and offers promising potential as a therapeutic target in glioma.

Current microbial sequencing relies on short-read platforms like Illumina and DNBSEQ, which are cost-effective and accurate but often produce fragmented draft genomes. Here, we used CycloneSEQ for long-read sequencing of ATCC BAA-835, producing long-reads with an average length of 11.6 kbp and an average quality score of 14.4. Hybrid assembly with short-reads data resulted in an error rate of only 0.04 mismatches and 0.08 indels per 100 kbp compared to the reference genome. This method, validated across nine species, successfully assembled complete circular genomes. Hybrid assembly significantly enhances genome completeness by using long-reads to fill gaps and accurately assembling multi-copy rRNA genes, unlike short-reads alone. Data subsampling showed that combining over 500 Mbp of short-read data with 100 Mbp of long-read data yields high-quality circular assemblies. CycloneSEQ long-reads improves the assembly of circular complete genomes from mixed microbial communities; however, its base quality needs improving. Integrating DNBSEQ short-reads improved accuracy, resulting in complete and accurate assemblies.

The management of breast cancer, one of the most common and heterogeneous malignancies, has transformed with the advent of precision medicine. This review explores current developments in genetic profiling, molecular diagnostics, and targeted therapies that have revolutionized breast cancer treatment. Key innovations, such as cyclin-dependent kinases 4/6 (CDK4/6) inhibitors, antibody-drug conjugates (ADCs), and immune checkpoint inhibitors (ICIs), have improved outcomes for hormone receptor-positive (HR+), HER2-positive (HER2+), and triple-negative breast cancer (TNBC) subtypes remarkably. Additionally, emerging treatments, such as PI3K inhibitors, poly (ADP-ribose) polymerase (PARP) inhibitors, and mRNA-based therapies, offer new avenues for targeting specific genetic mutations and improving treatment response, particularly in difficult-to-treat breast cancer subtypes. The integration of liquid biopsy technologies provides a non-invasive approach for real-time monitoring of tumor evolution and treatment response, thus enabling dynamic adjustments to therapy. Molecular imaging and artificial intelligence (AI) are increasingly crucial in enhancing diagnostic precision, personalizing treatment plans, and predicting therapeutic outcomes. As precision medicine continues to evolve, it has the potential to significantly improve survival rates, decrease recurrence, and enhance quality of life for patients with breast cancer. By combining cutting-edge diagnostics, personalized therapies, and emerging treatments, precision medicine can transform breast cancer care by offering more effective, individualized, and less invasive treatment options.

The advent of novel therapeutic agents has advanced biomarker characterization in non-small-cell lung cancer (NSCLC), driving increased adoption of next-generation sequencing (NGS) technologies for molecular testing. However, comprehensive data addressing the clinical utility of different NGS platforms for NSCLC remains limited.

This retrospective study analyzed real-world data from 478 Taiwanese NSCLC patients over five years, using the Oncomine Focus Assay (OFA) to assess genetic alterations. The evaluation focused on assay accuracy, limit of detection (LoD), sequencing performance, and the genetic landscape of NSCLC.

The OFA achieved an NGS success rate of 80.5% (385/478), with tumor cell percentage, specimen source and FFPE block age identified as key factors affecting success. Quality metrics demonstrated robust sequencing performance, including 97.0 ± 9.6% on-target alignment, 94.7 ± 6.4% uniformity, and ≥ 500 × coverage for 98.0 ± 6.6% of amplicons. Among the 385 patients analyzed, 86.8% (334/385) were found to harbor pathogenic or likely pathogenic variants, of which 78.4% (262/334) were SNVs/Indels, 41.6% (139/334) were CNVs, 2.7% (9/334) were exon skipping alterations, and 10.2% (34/334) were gene fusions. Actionable driver mutations included EGFR mutations (46.2%, 178/385), KRAS mutations (9.4%, 36/385), ERBB2 mutations (6.8%, 26/385), ALK fusions (4.4%, 17/385), MET exon 14 skipping (2.3%, 9/385), BRAF mutations (2.3%, 9/385), ROS1 and RET fusions (1.8%, 7/385 each), and NTRK1 fusions (0.5%, 2/385). Notably, KRAS G12 C mutation was detected in 2.8% (11/385) of cases.

This study demonstrates the robust performance of the OFA in identifying clinically relevant genetic alterations in NSCLC. The findings support its clinical utility in precision oncology and provide valuable insights into the genetic landscape of Asian NSCLC, enhancing personalized treatment strategies for lung cancer patients.

Reference genome assemblies are the basis for comprehensive genomic analyses and comparisons. Due to declining sequencing costs and growing computational power, genome projects are now feasible in smaller labs. De novo genome sequencing for non-model or emerging model organisms requires knowledge about genome size and techniques for extracting high molecular weight DNA. Next to quality, the amount of DNA obtained from single individuals is crucial, especially, when dealing with small organisms. While long-read sequencing technologies are the methods of choice for creating high quality genome assemblies, pure short-read assemblies might bear most of the coding parts of a genome but are usually much more fragmented and do not well resolve repeat elements or structural variants. Several genome initiatives produce more and more non-model organism genomes and provide rules for standards in genome sequencing and assembly. However, sometimes the organism of choice is not part of such an initiative or does not meet its standards. Therefore, if the scientific question can be answered with a genome of low contiguity in intergenic parts, missing the high standards of chromosome scale assembly should not prevent publication. This review describes how to set up an animal genome sequencing project in the lab, how to estimate costs and resources, and how to deal with suboptimal conditions. Thus, we aim to suggest optimal strategies for genome sequencing that fulfil the needs according to specific research questions, e.g. "How are species related to each other based on whole genomes?" (phylogenomics), "How do genomes of populations within a species differ?" (population genomics), "Are differences between populations relevant for conservation?" (conservation genomics), "Which selection pressure is acting on certain genes?" (identification of genes under selection), "Did repeats expand or contract recently?" (repeat dynamics).

Metagenomic next-generation sequencing (mNGS) can potentially detect various pathogenic microorganisms without bias to improve the diagnostic rate of fever of unknown origin (FUO), but there are no effective methods to predict mNGS-positive results. This study aimed to develop an interpretable machine learning algorithm for the effective prediction of mNGS results in patients with FUO.

A clinical dataset from a large medical institution was used to develop and compare the performance of several predictive models, namely eXtreme Gradient Boosting (XGBoost), Light Gradient-Boosting Machine (LightGBM), and Random Forest, and the Shapley additive explanation (SHAP) method was employed to interpret and analyze the results.

The mNGS-positive rate among 284 patients with FUO reached 64.1%. Overall, the LightGBM-based model exhibited the best comprehensive predictive performance, with areas under the curve of 0.84 and 0.93 for the training and validation sets, respectively. Using the SHAP method, the five most important factors for predicting mNGS-positive results were albumin, procalcitonin, blood culture, disease type, and sample type.

The validated LightGBM-based predictive model could have practical clinical value in enhancing the application of mNGS in the etiological diagnosis of FUO, representing a powerful tool to optimize the timing of mNGS.

Oncogenic gene fusions occur across a broad range of cancers and are a defining feature of some cancer types. Cancers driven by gene fusion products tend to respond well to targeted therapies, where available; thus, detection of potentially targetable oncogenic fusions is necessary to select optimal treatment. Detection methods include non-sequencing methods, such as fluorescence in situ hybridization and immunohistochemistry, and sequencing methods, such as DNA- and RNA-based next-generation sequencing (NGS). While NGS is an efficient way to analyze multiple genes of interest at once, economic and technical factors may preclude its use in routine care globally, despite several guideline recommendations. The aim of this review is to present a summary of oncogenic gene fusions, with a focus on fusions that affect tyrosine kinase signaling, and to highlight the importance of testing for oncogenic fusions. We present an overview of the identification of oncogenic gene fusions and therapies approved for the treatment of cancers harboring gene fusions, and summarize data regarding treating fusion-positive cancers with no current targeted therapies and clinical studies of fusion-positive cancers. Although treatment options may be limited for patients with rare alterations, healthcare professionals should identify patients most likely to benefit from oncogenic gene fusion testing and initiate the appropriate targeted therapy to achieve optimal treatment outcomes.

Simultaneous identification of natural and chemically modified DNA nucleotides at molecular resolution remains a pivotal challenge in genomic science. Despite significant advances in current sequencing technologies, the ability to identify subtle changes in natural and chemically modified nucleotides is hindered by structural and configurational complexity. Given the critical role of nucleobase modifications in data storage and personalized medicine, we propose a computational approach using a graphene nanopore coupled with machine learning (ML) to simultaneously recognize both natural and chemically modified nucleotides, exploring a wide range of modifications in the nucleobase, sugar, and phosphate moieties while investigating quantum transport mechanisms to uncover distinct molecular signatures and detailed electronic and orbital insights of the nucleotides. Integrating with the best-fitted model, the graphene nanopore achieves a good classification accuracy of up to 96% for each natural, chemically modified, purine, and pyrimidine nucleotide. Our approach offers a rapid and precise solution for real-time DNA sequencing by decoding natural and chemically modified nucleotides on a single platform.

The fields of allergy and immunology are increasingly recognizing the transformative potential of artificial intelligence (AI). Its adoption is reshaping research directions, clinical practices, and healthcare systems. However, a systematic overview identifying current statuses, emerging trends, and future research hotspots is lacking.

This study applied bibliometric analysis methods to systematically evaluate the global research landscape of AI applications in allergy and immunology. Data from 3,883 articles published by 21,552 authors across 1,247 journals were collected and analyzed to identify leading contributors, prevalent research themes, and collaboration patterns.

Analysis revealed that the USA and China are currently leading in research output and scientific impact in this domain. AI methodologies, especially machine learning (ML) and deep learning (DL), are predominantly applied in drug discovery and development, disease classification and prediction, immune response modeling, clinical decision support, diagnostics, healthcare system digitalization, and medical education. Emerging trends indicate significant movement toward personalized medical systems integration.

The findings demonstrate the dynamic evolution of AI in allergy and immunology, highlighting the broadening scope from basic diagnostics to comprehensive personalized healthcare systems. Despite advancements, critical challenges persist, including technological limitations, ethical concerns, and regulatory frameworks that could potentially hinder further implementation and integration.

AI holds considerable promise for advancing allergy and immunology globally by enhancing healthcare precision, efficiency, and accessibility. Addressing existing technological, ethical, and regulatory challenges will be crucial to fully realizing its potential, ultimately improving global health outcomes and patient well-being.

Comprehensive genomic profiling (CGP) refers to the detailed genomic analysis of cancers for oncology patients. With the rapid development of next-generation sequencing (NGS) technologies, CGP has been widely applied to clinical practice and managing oncology patients. CGP can be performed on the tumor DNA and RNA, as well as non-tumor tissues (e.g., blood, pleural effusion, and ascites). In this article, we review the current evidence supporting the use of CGP in the management of oncology patients, both in real-world practice and the bridging to clinical trials. We also discuss the role of the molecular tumor board on the application of CGP in oncology patients. We provide an overview of the current scheme of CGP reimbursement in Taiwan and the precision oncology branch of the National Biobank Consortium of Taiwan. Finally, we discuss about the potential barriers and challenges of applying CGP in managing oncology patients and the future perspectives of CGP in precision oncology.

Hepatitis B virus (HBV) is a global health problem with over 250 million people affected worldwide. Nucleos(t)ide analogues remain the standard of care and suppress production of progeny virions; however, they have limited effect on the viral transcriptome and long-term treatment is associated with off-target toxicities. Promising results are emerging from clinical trials and several drug classes have been evaluated, including capsid assembly modulators and RNA interfering agents. Whilst peripheral biomarkers are used to monitor responses and define treatment endpoints, they fail to reflect the full reservoir of infected hepatocytes. Given these limitations, consideration should be given to the merits of sampling liver tissue, especially in the context of clinical trials. In this review article, we will discuss methods for profiling HBV in liver tissue and their value to the HBV cure programme.

Enhancing athletic performance through the manipulation of nutritional intake has ancient roots, with early guidance from "philosophical giants" like Hippocrates, who describes the balance between diet and exercise. Modern sports nutrition emerged in the 20th century, with research identifying carbohydrate (CHO) intake as beneficial for endurance. Studies like Gordon's in the 1920s linked blood glucose levels to marathon performance, while Cade's research in the 1960s on fluid and electrolyte intake led to the founding of Gatorade and the shift toward drinking during exercise to allegedly prevent dehydration and improve sporting performance. Today, sports nutrition is in a "holding pattern" after significant developments in the 1980s, 1990s, and the 2000s. A new era will involve personalized nutrition, but this development will require a game-changing injection of momentum, recognizing that athletes' responses to nutrition interventions vary widely. New technologies will also need to be developed and perfected, including wearables for real-time biometric monitoring (e.g., heart rate variability, glucose, and sweat composition and rate), which offer potential for tailored nutrition (i.e., diet and hydration) strategies. Applications of genetic and multi-omics technologies (like genomics, transcriptomics, metabolomics, proteomics, and epigenomics) are needed to unlock the potential of personalized sports nutrition by analyzing individual responses to factors such as sleep, nutrition, and exercise. The future lies in fast integration of all available data using next-generation bioinformatics and AI to generate personalized recommendations, with an emphasis on empirical evidence rather than solely commercial interests. As technology matures, sports (and exercise) nutrition will continue refining its practices but will need a paradigm shift to deliver precise interventions that may offer athletes the crucial edge needed to maximize performance while promoting short-term and long-term health.

This dataset provides the complete genome sequence of Psychrobacter sp. KFRI-CH2-11, isolated from Korean fermented anchovy, Myeolchi-jeotgal. Genomic analysis identified genes involved in Vitamin B12 biosynthesis, carbohydrate metabolism, CRISPR-Cas defense systems, and antioxidant activity, underscoring the strain's potential for use in food biotechnology. Additional genes linked to antibiotic resistance and bioremediation suggest adaptability in diverse environments, particularly cold-chain storage in the dairy and meat industry. PathogenFinder analysis confirmed the absence of pathogenicity-associated genes, validating the strain's suitability as a probiotic and biofortifying agent in food products.

Comprehensive genomic profiling (CGP) is gaining ground in modern precision oncology for its ability to potentially analyze multiple tumor alterations and identify actionable ones, guiding targeted anticancer treatments. However, integrating CGP into healthcare systems demands consideration of the available evidence and collaboration between shareholders.

This review explores CGP's cost-effectiveness and feasibility across diverse healthcare settings, based on searches in PubMed, Google Scholar, gray literature, and extensive snowballing. We further aimed to elucidate barriers to routine CGP implementation and discuss potential solutions.

Patients generally express satisfaction with CGP, especially if publicly funded, yet face difficulties in understanding test results, and managing lack of actionable mutations and access to novel treatment avenues. Physicians exhibit confidence in recommending and interpreting CGP for patients with refractory disease and considerable life expectancy and performance status, albeit acknowledging potential treatment delays. Health economic studies support CGP's cost-effectiveness, highlighting increased survival, productivity, reduced medical service utilization, and cost diversion to trial sponsors. Nonetheless, challenges persist, including reimbursement policies, limited testing accessibility, and the imperative for physician training and infrastructure enhancement. Addressing these issues through collaborative efforts and policy adjustments is paramount for realizing the full potential of CGP in advancing precision oncology.

Atherosclerosis is a chronic inflammatory condition of the arteries, marked by the development of plaques within the arterial intima. The rupture of unstable plaques can lead to thrombosis, downstream vessel occlusion and serious clinical events. The composition of atherosclerotic plaques is complex and highly heterogeneous, posing challenges for their study. The current pathology and histological subtype classification of plaques may fail to fully encompass the microscopic molecular components in the tissue, the disease progress in various stages of atherosclerosis and the potential mechanism of plaque rupture. However, spatial mapping of the heterogeneity in plaque tissue components can enhance our understanding of these lesions. Despite the considerable progress made by traditional omics in the field of disease research, and its status as an indispensable technology, there remain inherent limitations in the investigation of minute molecular. In recent years, spatial omics techniques have advanced significantly, enabling the visualisation and analysis of specific components within plaques that may serve as causal targets associated with disease progression. The effective application of spatial omics in both research and clinical settings represents a promising area for further exploration. This review focuses on the recent advancements and findings related to spatial omics in the study of extracranial carotid atherosclerotic cerebrovascular disease. Spatial omics analysis of atherosclerotic plaques can facilitate the detection of biomarkers with diagnostic significance or potential relevance to disease, offering new methods and insights into the diagnosis of atherosclerosis and its complications.

Epilepsy is a group of neurologic disorders with clinical and genetic heterogeneity. Epilepsy often affects children; thus, early diagnosis and precise treatment are vital to protecting the standard of life of a child. Progress in epilepsy-related gene discovery has caused enormous novelty in specific epilepsy diagnoses. Genetic testing using next-generation sequencing is now reachable, leading to higher diagnosis ratios and understanding of the disease's underlying mechanisms. The study's primary aim was to identify the genetic etiology based on targeted next-generation sequence analysis data and to calculate the diagnostic value of the epilepsy gene panel in the 0-17 age-group diagnosed with epilepsy. The secondary aim was to demonstrate the significance of periodic reinterpretation of variant of uncertain significance (VUS) variants and genotype-phenotype correlation.

This retrospective study comprised 107 patients with epilepsy aged 8 months to 17 years, for whom a targeted gene panel covered 110 genes. VUS variants were reanalyzed, and genotype-phenotype correlation was performed.

In the initial evaluation, causal variants were described in 23 patients (21.5%). After reinterpretation of VUS, we detected causal variants in 30 out of 107 patients (28%). By reinterpreting the VUS and evaluating genotype-phenotype correlations, we enhanced our diagnostic value by 30.32%. After reinterpretation of VUS variants, the ACMG classification of 36 variants, including 15 benign (31%), 15 likely benign (31%), 5 likely pathogenic (10%), and 1 pathogenic (2%), were redefined. We most frequently detected causal variants in TSC2 (n = 5), GRIN2A (n = 4), and ALDH7A1 (n = 4) genes.

The predictive value for epilepsy panel testing was 28% in the cohort. Our study revealed the importance of reanalysis of VUS variants and contributed to enriching the mutation spectrum in epilepsy.

The vast and complex nature of human genomic sequencing data presents challenges for effective analysis. This review aims to investigate the application of natural language processing (NLP) techniques, particularly large language models (LLMs) and transformer architectures, in deciphering genomic codes, focusing on tokenization, transformer models, and regulatory annotation prediction. The goal of this review is to assess data and model accessibility in the most recent literature, gaining a better understanding of the existing capabilities and constraints of these tools in processing genomic sequencing data.

Following Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) guidelines, our scoping review was conducted across PubMed, Medline, Scopus, Web of Science, Embase, and ACM Digital Library. Studies were included if they focused on NLP methodologies applied to genomic sequencing data analysis, without restrictions on publication date or article type.

A total of 26 studies published between 2021 and April 2024 were selected for review. The review highlights that tokenization and transformer models enhance the processing and understanding of genomic data, with applications in predicting regulatory annotations like transcription-factor binding sites and chromatin accessibility.

The application of NLP and LLMs to genomic sequencing data interpretation is a promising field that can help streamline the processing of large-scale genomic data while also providing a better understanding of its complex structures. It has the potential to drive advancements in personalized medicine by offering more efficient and scalable solutions for genomic analysis. Further research is also needed to discuss and overcome current limitations, enhancing model transparency and applicability.

This review highlights the growing role of NLP, particularly LLMs, in genomic sequencing data analysis. While these models improve data processing and regulatory annotation prediction, challenges remain in accessibility and interpretability. Further research is needed to refine their application in genomics.

Plant viruses pose a significant threat to global agriculture. For a long time, conventional methods including detection based on visual symptoms, host range investigations, electron microscopy, serological assays (e.g., ELISA, Western blotting), and nucleic acid-based techniques (PCR, RT-PCR) have been used for virus identification. With increased sensitivity, speed, and specificity, new technologies like loop-mediated isothermal amplification (LAMP), high-throughput sequencing (HTS), nanotechnology-based biosensors, and CRISPR diagnostics have completely changed the way plant viruses are detected. Recent advances in detection techniques integrate artificial intelligence (AI), machine learning (ML), and the Internet of Things (IoT) for real-time monitoring. Innovations like hyperspectral imaging, deep learning, and cloud-based IoT platforms further support disease identification and surveillance. Nanotechnology-based lateral flow assays and CRISPR-Cas systems provide rapid, field-deployable solutions. Despite these advancements, challenges such as sequence limitations, multiplexing constraints, and environmental concerns remain. Future research should focus on refining portable on-site diagnostic kits, optimizing nanotechnology applications, and enhancing global surveillance systems. Interdisciplinary collaboration across molecular biology, bioinformatics, and engineering is essential to developing scalable, cost-effective solutions for plant virus detection, ensuring agricultural sustainability and ecosystem protection.

Cancer arises from genetic alterations that impact both the genome and transcriptome. The utilization of nanopore sequencing offers a powerful means of detecting these alterations due to its unique capacity for long single-molecule sequencing. In the context of DNA analysis, nanopore sequencing excels in identifying structural variations (SVs), copy number variations (CNVs), gene fusions within SVs, and mutations in specific genes, including those involving DNA modifications and DNA adducts. In the field of RNA research, nanopore sequencing proves invaluable in discerning differentially expressed transcripts, uncovering novel elements linked to transcriptional regulation, and identifying alternative splicing events and RNA modifications at the single-molecule level. Furthermore, nanopore sequencing extends its reach to detecting microorganisms, encompassing bacteria and viruses, that are intricately associated with tumorigenesis and the development of cancer. Consequently, the application prospects of nanopore sequencing in tumor diagnosis and personalized treatment are expansive, encompassing tasks such as tumor identification and classification, the tailoring of treatment strategies, and the screening of prospective patients. In essence, this technology stands poised to unearth novel mechanisms underlying tumorigenesis while providing dependable support for the diagnosis and treatment of cancer.

Viral infectious diseases, characterized by their intricate nature and wide-ranging diversity, pose substantial challenges in the domain of data management. The vast volume of data generated by these diseases, spanning from the molecular mechanisms within cells to large-scale epidemiological patterns, has surpassed the capabilities of traditional analytical methods. In the era of artificial intelligence (AI) and big data, there is an urgent necessity for the optimization of these analytical methods to more effectively handle and utilize the information. Despite the rapid accumulation of data associated with viral infections, the lack of a comprehensive framework for integrating, selecting, and analyzing these datasets has left numerous researchers uncertain about which data to select, how to access it, and how to utilize it most effectively in their research.This review endeavors to fill these gaps by exploring the multifaceted nature of viral infectious diseases and summarizing relevant data across multiple levels, from the molecular details of pathogens to broad epidemiological trends. The scope extends from the micro-scale to the macro-scale, encompassing pathogens, hosts, and vectors. In addition to data summarization, this review thoroughly investigates various dataset sources. It also traces the historical evolution of data collection in the field of viral infectious diseases, highlighting the progress achieved over time. Simultaneously, it evaluates the current limitations that impede data utilization.Furthermore, we propose strategies to surmount these challenges, focusing on the development and application of advanced computational techniques, AI-driven models, and enhanced data integration practices. By providing a comprehensive synthesis of existing knowledge, this review is designed to guide future research and contribute to more informed approaches in the surveillance, prevention, and control of viral infectious diseases, particularly within the context of the expanding big-data landscape.

Extraction of meaningful biological insight from gene expression profiling often focuses on the identification of statistically enriched terms or pathways. These methods typically use gene sets as input data, and subsequently return overrepresented terms along with associated statistics describing their enrichment. This approach does not cater to analyses focused on a single gene-of-interest, particularly when the gene lacks prior functional characterization. To address this, we formulated GeneCOCOA, a method which utilizes context-specific gene co-expression and curated functional gene sets, but focuses on a user-supplied gene-of-interest (GOI). The co-expression between the GOI and subsets of genes from functional groups (e.g. pathways, GO terms) is derived using linear regression, and resulting root-mean-square error values are compared against background values obtained from randomly selected genes. The resulting p values provide a statistical ranking of functional gene sets from any collection, along with their associated terms, based on their co-expression with the gene of interest in a manner specific to the context and experiment. GeneCOCOA thereby provides biological insight into both gene function, and putative regulatory mechanisms by which the expression of the GOI is controlled. Despite its relative simplicity, GeneCOCOA outperforms similar methods in the accurate recall of known gene-disease associations. We furthermore include a differential GeneCOCOA mode, thus presenting the first implementation of a gene-focused approach to experiment-specific gene set enrichment analysis. GeneCOCOA is formulated as an R package for ease-of-use, available at https://github.com/si-ze/geneCOCOA.