搜索结果：Genomics & informatics

Advances in artificial intelligence (AI)-driven bioinformatics promise democratized discovery, yet major inequities persist. Equitable adoption of bioinformatics tools will require sustained investment in infrastructure, training, institutions, and global communities, not just access.

Liver fibrosis is a common pathological stage in the progression of various chronic liver diseases. Current interventions primarily focus on anti-inflammatory and antioxidant effects, lacking safe and universally applicable strategies for reversal. This work aimed to clarify the chemical basis and the potential mechanism of the ethyl acetate fraction of Prismatomeris tetrandra(HG). The constituents of HG were characterized by liquid chromatography-mass spectrometry(LC-MS). Transcriptomic dataset was analyzed for differential expression, which was followed by weighted gene co-expression network analysis(WGCNA). Gene Ontology(GO) and Kyoto Encyclopedia of Genes and Genomes(KEGG) analyses were performed to build a "constituent-target-pathway" network for key pathway screening. A mouse liver fibrosis model was induced by carbon tetrachloride(CCl_4). After mice were administered continuously for 4 weeks, serum alanine aminotransferase(ALT), aspartate aminotransferase(AST), malondialdehyde(MDA), hyaluronic acid(HA), laminin(LN), procollagen type Ⅲ(PCⅢ), and type Ⅳ collagen(COL4) were detected. Hematoxylin and eosin(HE) and Masson staining were performed to assess histological changes, and Western blot was employed to detect the expression of alpha-smooth muscle actin(α-SMA) and the proteins related to death receptor pathway(Fas, Fas ligand [FasL], and Fas-associated death domain protein [FADD]). The results showed that the fraction was rich in coumarins and flavonoids. Bioinformatics analysis indicated that the signaling primarily acted on apoptosis-related pathways. In vivo, HG ameliorated liver injury and oxidative stress, reduced serum fibrosis markers, alleviated collagen deposition, and suppressed hepatic stellate cell(HSC) activation, with an overall dose-dependent trend. Network pharmacology results indicated that HG may reduce extracellular matrix(ECM) deposition by rebalancing inflammatory receptor-kinase signaling and promoting death receptor-mediated programmed cell death. Collectively, HG exhibits a clear anti-fibrotic effect in hepatic fibrosis models, potentially through mobilization of the extrinsic apoptotic pathway, which provides a basis for further validation of key monomers and causal mechanisms.

Artificial intelligence (AI) strategies are revolutionizing genomics by extracting complex patterns that traditional statistical pipelines are likely to miss. This mini-review aims to provide a concise overview of how AI is transforming major genomic technologies including variant calling, gene expression analysis, single-cell transcriptomics, CRISPR-Cas9 optimization, and multi-omics integration. In genome sequencing, machine learning variant callers greatly improve the accuracy and the rate at which single nucleotide and structural variants are called. In bulk RNA-Seq, AI augmented quantification, denoising, and differential expression modules complement the highly established STAR-featureCounts-DESeq2 pipeline, revealing subtle signals in big data sets. In single cell transcriptomics, deep learning approaches enhance batch correction, automate cell type annotation, and track developmental trajectories, hence clarifying cellular heterogeneity. AI-assisted guide RNA design, outcome prediction, and nuclease engineering enable more efficient CRISPR-Cas9 editing, reducing experimental cycles, and off-target effects. Finally, integrated platforms that combine genomic, transcriptomic, epigenomic, proteomic, and metabolomic layers provide an integrative view of cellular regulation and disease mechanisms. The review also covers current limitations, sparsity of data, model bias, privacy, and the need for standardized benchmarks and offers future directions in the form of interpretable models, collaborative learning, and open science practices. Together, these developments render AI an indispensable partner to unravel genomic complexity and accelerate precision medicine applications.

Circular RNAs (circRNAs) were first identified approximately 50 years ago in pathogenic viroids as single-stranded, covalently closed RNA molecules. Initially considered by-products of splicing, circRNAs are now recognised as an important class of regulatory RNAs involved in microRNA sponging, RNA-protein interactions, and cellular pathways. Their closed-loop structure, generated through backsplicing, confers resistance to exonucleolytic degradation and contributes to their stability. Owing to their tissue- and disease-specific expression, circRNAs have emerged as promising biomarkers for cancer, neurodegenerative disorders, and cardiovascular disease. Over the past decade, numerous bioinformatics tools utilising RNA-sequencing (RNA-seq) data have been developed for circRNA detection and analysis. Detection methods have evolved from manual split-read inspection to automated identification of the back spliced junction, while annotation pipelines now resolve the genomic origins and structural characteristics of circRNAs. Because individual circRNA callers vary considerably in sensitivity and specificity, a combined usage of tools in circRNA detection has become the preferred strategy for generating high-confidence datasets. Beyond their non-coding functions, increasing evidence suggests that some circRNAs possess protein-coding potential through open reading frames, cap-independent translation mechanisms, internal ribosome entry sites (IRESs), and N6-methyladenosine modifications. A new generation of bioinformatic tools can now assess the protein-coding potential of circRNAs, integrating the above features, as well as machine learning and deep learning approaches refining these predictions. This review summarises recently developed short-read RNA-seq bioinformatics tools for circRNA detection, consensus calling, annotation, and protein-coding potential prediction, with a particular focus on advances from the past five years that facilitate the identification of translatable circRNAs.

Biosynthetic gene clusters (BGCs) are often encoding specialized metabolic pathways in plants, yet effective methods for the comparison across multiple species are still evolving. In this protocol, we present a bioinformatics approach combining plantiSMASH and MCScan for the identification, annotation, and comparative analysis of BGCs in plant genomes. The methodology involves using plantiSMASH to predict and annotate potential BGCs, followed by the use of MCScan to perform syntenic analysis, enabling the exploration of the conservation and evolutionary dynamics of BGCs across different plant species. Here, we provide a step-by-step guide for installing and configuring the necessary software, preparing genomic data, and executing the analysis. This methodology, integrated with transcriptomic and metabolomic data, can be used to verify the functional relevance of the identified BGCs in specific biosynthetic pathways. It is applicable to a broad range of plant species and serves as a framework for the discovery and characterization of BGCs. The described methodology can significantly enhance research in plant genomics and metabolic engineering by offering new insights into the organization and function of BGCs.

The Mammalian Phenotype Ontology is a comprehensive controlled vocabulary encompassing phenotypic terms related to congenital anomalies, developmental abnormalities, and other mammalian phenotypes, developed by Mouse Genome Informatics. Although this ontology was originally created in English, multilingualization is essential for broadening its impact by improving accessibility for researchers worldwide. To address this need, we developed a Japanese translation of the Mammalian Phenotype Ontology to support non-English-speaking research communities in Japan. The Japanese translation of this ontology was produced using a systematic human-in-the-loop workflow consisting of machine translation, systematic manual review, and expert curation. Manual curation is a critical component of this process, as automated translation alone is insufficient for accurately rendering domain-specific phenotype terminology. The Japanese-translated Mammalian Phenotype Ontology has been integrated into multiple data resources and search platforms, including experimental animal resource databases and disease-oriented data integration services. This integration enables users to search for and interpret annotated mouse phenotypes using Japanese terminology and has significantly improved the accessibility and usability of Mammalian Phenotype Ontology-based services for non-English-speaking Japanese researchers, students, and laboratory animal caretakers. In addition, the Japanese translation of the Mammalian Phenotype Ontology is publicly released and formally incorporated into the official Mammalian Phenotype Ontology distribution through collaboration with Mouse Genome Informatics, allowing regular updates synchronized with ongoing development. This study highlights the importance of localizing ontology vocabularies to advance global accessibility and presents a practical framework for systematic ontology translation.

Posttraumatic stress disorder (PTSD) is a psychiatric condition that may develop after trauma exposure. PTSD is characterized by considerable clinical heterogeneity. The amygdala's key role in fear conditioning makes it an important focus for investigating the neurobiology of PTSD. However, associations between amygdala volume and PTSD have been inconsistent. The amygdala consists of functionally distinct nuclei. Specific associations between amygdala nuclei volumes and PTSD may account for previous discrepancies between PTSD and whole amygdala volume. This study investigates the associations between amygdala nuclei volumes, PTSD diagnosis, severity, symptom cluster scores, age of onset and childhood trauma. Individuals with a PTSD diagnosis (n = 771) and controls (n = 1 081, 72% trauma-exposed) were sourced from the Enhancing Neuro-Imaging Genetics through Meta-Analysis and Psychiatric Genomics Consortium (mean age = 32.4 years, (SD = 13 years), 60% male). Nine amygdala nuclei volumes were compared to PTSD diagnosis, age of onset, overall severity, symptom cluster scores (re-experiencing, arousal, and avoidance/emotional numbing), and childhood trauma subscales. Analyses were performed using ordinary least-squares regression, corrected for age, sex, intracranial volume, and whole amygdala volume. PTSD diagnosis was not significantly associated with amygdala nuclei volumes. PTSD severity scores were associated with smaller right lateral nucleus volume (β = -0.26, pBON = 0.01). Smaller right lateral nucleus volume was also associated with re-experiencing (β = -1.01, pBON = 0.04) and arousal (β = -0.9, pBON = 0.04), smaller left paralaminar nucleus volume was associated with re-experiencing (β = -0.1, pBON = 0.04), smaller left corticoamygdaloid transition area volume was associated with avoidance (β = -0.31, pBON = 0.02). Larger left and right central nucleus volumes were significantly associated with childhood physical abuse (β = 0.24, pBON = 9 × 10-3) and neglect (β = 0.29, pBON = 0.04), respectively. Differences in select amygdala nuclei volumes among adults are associated with PTSD severity, symptom cluster scores, and childhood physical abuse and neglect. These findings demonstrate nuclei-specific patterns consistent with their functional roles in fear learning and expression.

Artificial intelligence (AI) is reshaping every stage of leukemia diagnostics, from digital morphology and multiparameter flow cytometry to next-generation sequencing, multi-omics analysis, and emerging computational frontiers such as quantum-inspired feature selection. This review outlines how contemporary AI tools can automate labor-intensive quantitation, flag diagnostically salient patterns, and standardize interpretation, while the pathologist or hematologist retains authority over validation, context-specific integration, and clinical decision-making. We present an illustrative "human-in-the-loop" workflow that embeds AI modules within current laboratory information systems, emphasizing points where expert oversight mitigates algorithmic bias and resolves discordant findings. We further map the validator-integrator role across morphology, flow cytometry, and genomic/multi-omic interpretation and provide practical training competencies and use cases for AI-assisted hematopathology. Beyond technical deployment, the article addresses the educational transformation required for sustainable adoption. Drawing on international competency frameworks, including the Digital Health Competencies in Medical Education Framework and recently proposed AI-specific Entrustable Professional Activities, we map core skills that future hematopathologists must master: data-science literacy, critical appraisal of AI outputs, and ethical governance. We highlight evaluated training models such as the Pathology Informatics Essentials for Residents curriculum, Stanford Artificial Intelligence in Machine and Imaging workshops, and College of American Pathologists bootcamps and propose integration strategies adaptable across resource settings. By pairing rigorous validation with targeted education, AI can elevate rather than eclipse the diagnostic role of the leukemia specialist, enabling more timely, reproducible, and personalized patient care.

As an important component of systems biology, omics technologies, with their advantages of high-throughput and holistic analysis, are profoundly transforming the research model of TCM. Pinellia ternata, a commonly used TCM with the effects of drying dampness and resolving phlegm, directing rebellious Qi downward to stop vomiting, and relieving stuffiness and dissipating binds, presents major challenges in quality control and in the elucidation of its complex pharmacological mechanisms. This article systematically reviews the application progress of omics technologies such as genomics, transcriptomics, proteomics, and metabolomics in the study of P. ternata's quality evaluation(covering factors such as cultivation environment, germplasm resources, and processing) and pharmacological mechanisms(including antiemetic effects, phlegm resolution and asthma relief, treatment of gastrointestinal diseases, and antitumor activities). By sorting through existing research findings, it summarizes the great potential of omics technologies in revealing the pharmacodynamic material basis, quality formation mechanisms, and multi-target action networks of P. ternata, and further looks ahead to future research directions such as multi-omics integration, data mining, and artificial intelligence, with a view to providing systematic theoretical and methodological references for the modernization and internationalization of P. ternata research.

Low-dose ionizing radiation (LDIR, ≤100 mGy) is a public health concern due to its extensive use in diagnostic and therapeutic imaging. This study examined somatic variants (SV) among Korean industrial radiographers exposed to LDIR using whole-genome sequencing (WGS). WGS data from 65 workers (mean age 36.7 years) collected between 2016 and 2023 were analyzed. Participants had a mean employment duration of 12.7 years and an average cumulative radiation dose of 33.9 mSv from National Dose Registry records. SV were identified via the GATK Mutect2 single-sample workflow applied to peripheral blood-derived DNA and sequentially filtered using standard pipelines, including FilterMutectCalls, population frequency, recurrent artifacts, Funcotator annotation, and manual Integrative Genomics Viewer review. A total of 105 170 somatic variants were identified, with a median of 1744 variants per individual, approximately 98% of which were single nucleotide variants. Cumulative radiation dose showed a significant positive correlation with total SV burden (R=0.32, P=0.013), including both coding (R=0.34, P=0.008) and non-coding regions (R=0.31, P=0.015). Age, smoking, alcohol consumption, hypertension, and hyperlipidemia were not significantly associated with variant burden. These WGS-based findings provide preliminary insight into blood-derived SV patterns among occupationally exposed radiation workers. Given the small sample size, blood-only design, and absence of matched unexposed controls, the findings should be interpreted as hypothesis-generating and require validation in larger longitudinal studies with comprehensive exposure assessment.

Researchers are increasingly interested in identifying different parts of the genome which work together to influence a phenotypic trait. A major objective in bioinformatics involves finding groups of variables determined from omics technologies such as DNA methylation sites, transcriptome profiling, etc. Given one set of variables, one could determine how variables within work together to influence an outcome. These groups of variables are called functional modules and previous work has identified them through sparse matrix decomposition techniques such as sparse principal components analysis. To determine how different parts of the genome work together, we present methods to extend functional modules and identify variables that influence an outcome variable through a stepwise mediating fashion. Traditionally, module discovery involves sparse matrix decomposition accomplished through tuning regularization constraints. In this paper, we efficiently tune a cardinality-based sparse singular value decomposition to discover balanced mediated functional modules. These methods will be tested on simulated stepwise functional modules that contain several signal and non-signal variables and applied to real omics data collected in The Cancer Genome Atlas.

The 17th Annual Frontiers in Cancer Science (FCS) conference (2025) highlighted the convergence of multiomics, computational biology, and ancestry-specific genomics to advance proactive cancer care. Key insights included the role of epigenetic plasticity in maintaining tumor-propagating states and the identification of metabolic vulnerabilities, such as the WNK1-mTORC1 axis in leukemia and MAF-driven glutamine metabolism in myeloma. The meeting underscored the systemic nature of cancer, detailing how "cancer-educated" neutrophils prime premetastatic niches and how spatial exclusion mechanisms hinder immunotherapy. Breakthroughs in therapeutic engineering were showcased, including CD7-directed chimeric antigen receptor T cells and irreversible KRASG12C inhibitors. A critical focus remained on precision oncology for diverse populations, advocating for ancestry-aware datasets and long-read sequencing to address genomic disparities in Asian cohorts. Furthermore, the integration of artificial intelligence-driven "fragmentomics" and machine learning offers new pathways for early detection and tracking disease lethality. Collectively, FCS 2025 demonstrated that the future of oncology lies in integrating high-resolution disease models with robust data science to transition from reactive treatment to personalized, interceptive management.

The genus Micromonospora is a prolific producer of specialized metabolites with pharmacological and agronomic relevance. Natural products derived from the genus Micromonospora have a distinctive chemical diversity and enormous therapeutic potential, thus represent a potential source for drugs and drug leads. To explore the biosynthetic potential of four Micromonospora strains isolated from cold desert of NW Himalayas through genome mining and to correlate predicted biosynthetic gene clusters with chemical features detected by untargeted LC-HRMS metabolomics. High-quality genomes were annotated for BGCs and matched against untargeted LC-HRMS features (peak picking, alignment, and annotation to chemical classes). Each isolate was grown in triplicate, and fermented broth was pooled for further metabolomic studies. By integrating genomic and metabolomic approaches, specialized biosynthetic gene clusters and strain-based putative metabolite classes were identified. LRS1 showed elevated xanthines (RiPP/siderophore), LRS3 had phenolic glycosides (hybrid PKS/NRPS), LRS4 showed 70-fold hydroxycinnamate enrichment (Type II PKS), and LRS5 displayed p-benzoquinone enrichment (Type III PKS). The metabolite profile of each strain aligned with its predicted biosynthetic gene cluster composition. Under a single growth regime, each Micromonospora strain exhibits a distinct metabolomic profile. This metabologenomics workflow can be further explored to isolate specialized metabolites with potential therapeutic and agricultural value.

Metabolic gene clusters (MGCs) are genomic loci that contain multiple genes that are functionally and genetically linked. MGCs collectively encode a spectrum of metabolic functions, including small molecule biosynthesis, nutrient assimilation, metabolite degradation, and production of proteins essential for growth and development. Due to their diverse ecological functions, identifying gene clusters is a powerful tool for small molecule discovery and provides insight into the ecology and evolution of organisms. Gene cluster detection algorithms have historically been specialized for detecting biosynthetic gene clusters that contain canonical "core" biosynthetic functions, while overlooking uncommon or unknown cluster classes. These overlooked clusters are a potential source of novel natural products and comprise an untold portion of overall gene cluster repertoires. Unbiased, function-agnostic detection algorithms therefore provide an opportunity to reveal novel classes of gene clusters and more precisely define genome organization.We developed CLOCI (Co-occurrence Locus and Orthologous Cluster Identifier) as a generalized, unbiased gene cluster detection algorithm. CLOCI generalizes gene cluster detection by identifying signatures of coordinated gene evolution that underlie all classes of MGCs. CLOCI first detects selection on gene colocalization by identifying and circumscribing shared synteny loci across a dataset of genomes into homologous locus groups. Gene clusters comprise a subset of these homologous locus groups, and CLOCI implements orthogonal proxies of coordinated gene evolution, such as quantifying loss and horizontal transfer of a locus, to enrich MGCs from homologous loci. Here, we describe the conceptual framework of the CLOCI algorithm and present a description of its implementation (see Note 1).

During the COVID-19 pandemic, the need for quick public health action often conflicted with the careful methods required in phylogenetics. To explore this, we reviewed 217 SARS-CoV-2 studies published from January 2020 to March 2025 in 121 journals. We found many methodological problems that weaken the reliability and reproducibility of these studies. Key issues include missing outgroup sampling, which affects ingroup topology, tree rooting and how we interpret evolutionary changes and relationships. Another issue is the lack of gene annotations, which can cause characters from one gene to align with those of a different gene. Many studies also misinterpret support or other branch statistics, treating them as proof of clade accuracy instead of as measures of relative evidence. In addition, 91% of the studies do not follow FAIR (Findable, Accessible, Interoperable, and Reusable) data principles, with data and code often unavailable. Finally, we also found a "prestige paradox": journals with higher impact factors do not necessarily have better methods or transparency. Therefore, we offer simple guidelines for authors, reviewers and editors to improve transparency and FAIR data standards in viral phylogenetics, making writing and reviewing papers easier, ensuring published phylogenetic analyses remain a trustworthy resource for future pandemics.

Efficient discovery of regulatory genes and elements is essential for understanding cell identity, differentiation, and disease mechanisms. The TRIAGE methods are a set of well-established computational approaches that identify context-specific regulatory genes and prioritize regulatory elements across the genome. Previous publications have described the development of these algorithms, their benchmarking, and biological applications. Here, we provide step-by-step protocols for applying the TRIAGE methods to identify regulatory drivers from diverse input types, including gene expression matrices, gene lists, and genomic loci. It covers analyses of both bulk and single-cell RNA-seq datasets and enables genome-wide interrogation of regulatory elements at single-base resolution. The analysis is efficient, typically requiring <30 min of computation time on a personal computer. In addition to the step-by-step description of the TRIAGE analysis workflow, we provide the TRIAGE toolkit, available as both an R package and a Python implementation, to support flexible and scalable regulatory analysis across platforms. &#xa9; 2026 The Author(s). Current Protocols published by Wiley Periodicals LLC. Basic Protocol 1: Prioritization of regulatory genes from bulk RNA-seq data Basic Protocol 2: Identification of cell populations and regulatory genes in single-cell RNA-seq data Basic Protocol 3: Prioritization of regulatory long noncoding RNAs Basic Protocol 4: Prioritization of functional genetic variants from eQTL data Alternate Protocol: Python-based implementation of the TRIAGE workflow for regulatory gene and element prioritization Support Protocol: Preparing a normalized expression matrix from bulk RNA-seq count data.

Hepatocellular carcinoma (HCC) related to hepatitis B virus (HBV) infection predominantly affects males, yet few studies have investigated the association between sex hormones and HBV integrations, and their involvement in HCC prognosis. We assessed estrogen receptor alpha (ER&#x3b1;) and androgen receptor (AR) expression via immunohistochemistry on tissue microarrays constructed from 426 HBV-related HCC samples. HBV integration features were determined using HBV-captured sequencing data. Logistic regression models were utilized to evaluate the association between sex hormone receptor expression level and HBV integration features. Cox regression models, combined with machine learning (ML) methods, were implemented to investigate the prognostic value of sex hormone receptors and HBV integrations concerning overall survival. We found high AR expression level was significantly associated with higher HBV integration levels (adjusted odds ratio [aOR]&#x2009;=&#x2009;1.84, 95% confidence interval [CI]: 1.09-3.11, P for trend&#x2009;=&#x2009;0.012), TERT integration (aOR&#x2009;=&#x2009;2.34, 95% CI: 1.16-4.74, P for trend&#x2009;=&#x2009;0.047), intergenic integration (aOR&#x2009;=&#x2009;2.25, 95% CI: 1.20-4.24, P for trend&#x2009;=&#x2009;0.021), and promoter integration (aOR&#x2009;=&#x2009;1.81, 95% CI: 1.00-3.31, P for trend&#x2009;=&#x2009;0.034). The inclusion of sex hormone receptors and HBV integrations in the predictive models led to improvements across all performance metrics in the Cox regression analyses (AUC improvement: 0.014 [Training], 0.026 [Validation]) and the ML (AUC improvement: 0.022 [Training]), although a slight deterioration in performance was noted in the ML validation set. The results suggested a relationship between AR expression level and HBV integration events, as well as the potential utility of HBV integration biomarkers and sex hormone receptor profiles in assessing post-surgical prognosis among HCC patients.

We present a genome assembly from an individual female Phaleria cadaverina (darkling beetle; Arthropoda; Insecta; Coleoptera; Tenebrionidae). The genome sequence has a total length of 367.34 megabases. Most of the assembly (75.2%) is scaffolded into 11 chromosomal pseudomolecules, including the X sex chromosome. The mitochondrial genome has also been assembled, with a length of 15.61 kilobases. This assembly was generated as part of the Darwin Tree of Life project, which produces genomes for eukaryotic species found in Britain and Ireland.

Essential genes are defined as indispensable for an organism's survival. The loss of function of these genes results in cell death or an inability to complete the normal life cycle. Research on essential genes is pivotal in elucidating the origin and evolution of life, as well as in identifying potential therapeutic targets. Therefore, predicting essential genes is of great scientific importance and has many applications in basic research and the biomedical field. In this study, we propose EssTFNet, a novel, interpretable deep learning framework that combines adaptive time-frequency analysis with a DNA language model to achieve accurate prediction of human essential genes while enabling mechanistic biological interpretation. EssTFNet leverages the architecture of ATFNet, which maps DNA and protein sequences into equivalent time-series signals to extract periodic and nonstationary features, enhancing the model's capacity to capture complex sequence patterns. Through feature selection and architectural optimization, EssTFNet achieves a favorable balance among prediction accuracy, model interpretability, and cross-tissue generalization. On the S1 benchmark task, EssTFNet outperformed mainstream sequence-based deep learning methods, achieving an area under the curve of 0.9679 and an area under the precision-recall curve of 0.8491. Additionally, the DeepLIFT attribution method was employed to identify functional motifs associated with gene essentiality, offering valuable insights for experimental validation. For the convenience of researchers, we have developed an easy-to-use web server and made it along with the source code in a GitHub repository: https://github.com/QIANJINYDX/EssTFNet. Overall, this study presents a potentially useful methodological framework for human essential genes prediction, which could provide valuable insights for future research and applications in this field.