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At least 19 recordsLinked to original sources
PubMed2006

T1DBase: integration and presentation of complex data for type 1 diabetes research.

T1DBase (http://T1DBase.org) [Smink et al. (2005) Nucleic Acids Res., 33, D544-D549; Burren et al. (2004) Hum. Genomics, 1, 98-109] is a public website and database that supports the type 1 diabetes (T1D) research community. T1DBase provides a consolidated T1D-oriented view of the complex data world that now confronts medical researchers and enables scientists to navigate from information they know to information that is new to them. Overview pages for genes and markers summarize information for these elements. The Gene Dossier summarizes information for a list of genes. GBrowse [Stein et al. (2002) Genome Res., 10, 1599-1610] displays genes and other features in their genomic context, and Cytoscape [Shannon et al. (2003) Genome Res., 13, 2498-2504] shows genes in the context of interacting proteins and genes. The Beta Cell Gene Atlas shows gene expression in beta cells, islets, and related cell types and lines, and the Tissue Expression Viewer shows expression across other tissues. The Microarray Viewer shows expression from more than 20 array experiments. The Beta Cell Gene Expression Bank contains manually curated gene and pathway annotations for genes expressed in beta cells. T1DMart is a query tool for markers and genotypes. PosterPages are 'home pages' about specific topics or datasets. The key challenge, now and in the future, is to provide powerful informatics capabilities to T1D scientists in a form they can use to enhance their research.

Animals↗
PubMed2026

Dataset Readiness Assessment With Large Language Model (DRAFT-LLM): A Multi-Axis Audit Guided by LLM.

This article details the Dataset Readiness Assessment for Training (DRAFT), a systematic method for determining whether a high-dimensional biological dataset is suitable for developing reliable, equitable (i.e., the extent to which model performance, error patterns, and potential benefits or harms are evaluated and found to be acceptably distributed across relevant demographic, biological, clinical, and contextual subgroups), and scientifically meaningful machine-learning models, and DRAFT Large Language Model (DRAFT-LLM), its optional human-in-the-loop extension for calibrating study-specific audits through structured, critically reviewed LLM guidance. Standard model validation often fails to detect when apparent performance is driven by spurious correlations, technical artifacts, or hidden stratification, leading to irreproducible and inequitable findings. DRAFT-LLM addresses this gap by shifting the focus from model tuning to structured dataset auditing, organized around Support Protocols 1 to 4 that capture the scientific intent, data structure, and governance constraints of a given study. These Support Protocols: (1) elicit and formalize investigator input into a study intake and dataset card; (2) compute standardized dataset statistics and structural summaries suitable for downstream analysis and LLM context; (3) configure the language model using form-based responses, safety guardrails, and governance rules; and (4) generate personalized instructions, prompts, and code templates for running DRAFT audits. Basic Protocols 1 to 3 are instantiated from this support layer for generalization, equity, and stability: they are reusable execution patterns whose concrete behavior is determined by the cards, statistics, and configurations defined in the Support Protocols. DRAFT-LLM and DRAFT are demonstrated in this article through an end-to-end case study on The Cancer Genome Atlas (TCGA). © 2026 Wiley Periodicals LLC. Support Protocol 1: Study intake and dataset card construction Support Protocol 2: Dataset structure and advanced summary statistics for LLM context Support Protocol 3: LLM configuration using structured form responses Support Protocol 4: Generation of personalized instructions for DRAFT audits Basic Protocol 1: Generalization audit Basic Protocol 2: Equity audit Basic Protocol 3: Stability audit.

Large Language Models↗
PubMed2025

BMDx2: A Tool for Integrating Toxicogenomics-Based Dose-Dependency Analysis and AOP-Based Mechanistic Insights.

Despite the advent of mechanistic toxicology using omics data to link molecular perturbations with systemic outcomes, regulatory toxicology still lacks the application of mechanism-anchored metrics from such data. This is partially because traditional gene-centric analysis often falls short of linking molecular changes to adverse outcomes. To address this gap, BMDx2, an open-source tool that transforms multi-dose toxicogenomics datasets into quantitative, mechanistic evidence for human chemical safety assessment is developed. BMDx2 couples benchmark-dose modeling with Adverse Outcome Pathway (AOP) enrichment to derive transcriptomic-based points of departure, enabling potency ranking, chemical prioritization, and mechanistically anchored explanations of the effect of chemical exposures. BMDx2 can process a broad range of data, including DNA microarray and RNA sequencing studies. Here, case studies are used to illustrate the versatility of BMDx2 in characterizing the mechanism of action of chemicals. An initial case study on carbon nanotubes exposure applies integrative analysis of transcriptomics and genome-wide DNA methylation data, uncovering cellular reprogramming processes underlying fibrosis. A second case study on bleomycin exposure demonstrate how transcriptomic data alone can be mapped to fibrosis-related AOPs in a standardized, regulatory appropriate manner. Together, these examples show how BMDx2 supports the regulatory application of toxicogenomics and accelerates mechanism-based chemical safety evaluation.

Toxicogenetics↗
PubMed2025

High-quality chromosome-level genome of three Meretrix species using Nanopore and Hi-C technologies.

Meretrix is a commercially valuable bivalve genus in Asia, but only one reference genome has hindered comprehensive genetic studies and germplasm resource evaluation. In this study, we present three reference genomes of Meretrix species: Meretrix sp. MF1, Meretrix sp. MT1, and Meretrix lamarckii JML1. Meretrix sp. MF1 was assembled at the chromosome level using Nanopore sequencing and Hi-C technologies, whereas Meretrix sp. MT1 and Meretrix lamarckii were assembled as scaffold-level assemblies. The chromosome-level genome of Meretrix sp. MF1 consists of 36 contigs, including 19 chromosomes and 17 scaffolds, with a total length of 883.3 Mb and a scaffold N50 of 46.87 Mb. Notably, the genome of Meretrix sp. MF1, a putative novel species, exhibits an Average Nucleotide Identity (ANI) of 94.33% with its closest relative, Meretrix lamarckii. These genomic resources not only provide a crucial foundation for genetic research on Meretrix but also contribute to the development of effective conservation strategies for its sustainable management.

Animals↗
PubMed2025

Chromosome-level genome assembly and annotation of Spinibarbus caldwelli.

Spinibarbus caldwelli is an economically important freshwater species within the Cyprinidae family, abundant in the middle and lower reaches of the Yangtze River and its adjacent basins. As a promising species suitable for aquaculture in southern China, the lack of genomic resources has hampered the genetic breeding and conservation. Here, we release a chromosome-level genome assembly for S. caldwelli using PacBio HiFi long-reads, Illumina short-reads, and Hi-C sequencing data. The final genome assembly is 1.77 Gb in size, with a contig N50 of 24.27 Mb. Using Hi-C scaffolding, 99.14% of the contigs were successfully anchored to 50 chromosomes, resulting in a scaffold N50 of 35.29 Mb. The final genome assembly shows a BUSCO completeness of 98.27%. The assembled genome contains 49.41% repetitive sequences and 51,505 predicted genes, 90.83% of which have been functionally annotated. This genome provides a genetic basis for S. caldwelli, facilitating the exploration of Cyprinid phylogeny, genetic improvement, and conservation efforts.

Animals↗
PubMed2025

GE-IA-NAM: gene-environment interaction analysis via imaging-assisted neural additive model.

MOTIVATION: Gene-environment (G-E) interaction analysis is crucial in cancer research, offering insights into how genetic and environmental factors jointly influence cancer outcomes. Most existing G-E interaction methods are regression-based, which may lack flexibility to capture complex data patterns. Recent advances have investigated deep neural network-based G-E models. However, these methods may be more vulnerable to information deficiency due to challenges such as limited sample size and high dimensionality. Apart from genetic and environmental data, pathological images have emerged as a widely accessible and informative resource for cancer modeling, presenting its potential to enhance G-E modeling. RESULTS: We propose the pathological imaging-assisted neural additive model for G-E analysis (GE-IA-NAM). The flexible and interpretable additive network architecture is adopted to account for individualized effects associated with genetic factors, environmental factors, and their interactions. To improve G-E modeling, an assisted-learning strategy is investigated, which adopts a joint analysis to integrate information from pathological images. Simulations and the analysis of lung and skin cancer datasets from The Cancer Genome Atlas demonstrate the competitive performance of the proposed method. AVAILABILITY AND IMPLEMENTATION: Python code implementing the proposed method is available at https://github.com/Mr-maoge/NAM-IA-GE. The data that support the findings in this article are openly available in TCGA (The Cancer Genome Atlas) at https://portal.gdc.cancer.gov/.

Gene-Environment Interaction↗
PubMed2025

A genome-wide association study identified 10 novel genomic loci associated with intrinsic capacity.

BACKGROUND: Intrinsic capacity (IC) is a multidimensional concept within the World Health Organization framework for healthy aging. It refers to the composite of an individual's physical and mental capacities that enable them to maintain well-being, functional ability, and engagement in valued activities throughout life. While substantial evidence supports the biological basis of IC and its subdomains, the extent to which genetic factors influence IC remains largely unexplored, with no studies currently available. METHODS: Using datasets from the UK Biobank (UKB; N = 44 631) and the Canadian Longitudinal Study on Aging (CLSA; N = 13 085), we implemented the restricted maximum likelihood method to estimate SNP-based heritability (h2snp), followed by a Genome-Wide Association Study (GWAS) to identify genetic variants associated with IC, and post-GWAS analyses to pinpoint biological implications. RESULTS: The h2snp for IC was estimated at 25.2% in UKB and 19.5% in CLSA. Our GWAS identified 38 independent SNPs for IC across 10 genomic loci and 4289 candidate SNPs, mapped to 197 genes. Post-GWAS analysis revealed the role of these genes in cellular processes such as cell proliferation, immune function, metabolism, and neurodegeneration, with high expression in muscle, heart, brain, adipose, and nerve tissues. Of the 52 traits tested, 23 showed significant genetic correlations with IC, and a higher genetic loading for IC was associated with higher IC scores. CONCLUSIONS: Overall, this study provides comprehensive evidence on the genetic architecture of IC, identifying novel genetic variants and biological pathways, advancing our current knowledge and laying the foundation for ongoing and future research on healthy aging.

Adult↗
PubMed2026

Integrative multi-omics quantitative trait loci prioritize CASP7 as a candidate protective gene for cataract.

Cataracts are the leading cause of vision loss worldwide. Despite surgery being the only effective treatment, its economic burden highlights the necessity of exploring the pathogenesis of cataracts. In this study, we analyzed 4 large-scale GWAS (genome-wide association study) datasets for cataracts and performed SMR analysis along with heterogeneity in dependent instruments (HEIDI) testing to explore the effects of methylation, expression, and protein QTLs on cataracts. We further validated shared genetic variants through COLOC analysis. Additionally, we searched datasets related to cataracts from the Gene Expression Omnibus (GEO) database for differentially expressed genes (DEGs) and Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes pathway (KEGG) enrichment analyses. By integrating summary-based Mendelian randomization (SMR) results with bioinformatics findings, CASP7 showed a consistent protective-direction association with cataract risk (mQTL: OR [95% CI] = 0.959 [0.941-0.977], FDR-adjusted P = .039; eQTL: OR [95% CI] = 0.897 [0.860-0.937], FDR-adjusted P = .0046; pQTL: OR [95% CI] = 0.597 [0.483-0.738], FDR-adjusted P = .00083). GEO-based analyses provided transcriptomic support for CASP7 involvement in cataract-related lens biology. These findings prioritize CASP7 as a genetically supported candidate protective gene associated with cataract risk. Because this study is based on public summary-level and transcriptomic datasets, the results should be interpreted cautiously and require functional validation in human lens-relevant systems.

Quantitative Trait Loci↗
PubMed2025

SIGEL: a context-aware genomic representation learning framework for spatial genomics analysis.

Spatial transcriptomics (ST) integrates spatial information into genomics, yet methods for generating spatially-informed gene representations are limited and computationally intensive. We present SIGEL, a cost-effective framework that derives gene manifolds from ST data by exploiting spatial genomic context. The resulting SIGEL-generated gene representations (SGRs) are context-aware, biologically meaningful, and robust across samples, making them highly effective for key downstream tasks, including imputing missing genes, detecting spatial expression patterns, identifying disease-related genes and interactions, and improving spatial clustering. Extensive experiments across diverse ST datasets validate SIGEL's effectiveness and highlight its potential in advancing spatial genomics research.

Genomics↗
PubMed2025

Associations Between Walking Pace, APOE-ε4 Genotype, and Brain Health in Middle-Aged to Older Adults.

PURPOSE: This study aimed to investigate whether self-reported walking pace (a marker of physical function) and the presence of APOE-&#x3b5;4 allele interact to modify brain health outcomes. METHODS: We used data from a prospective cohort study of middle-aged to older adults from the UK Biobank who self-reported walking pace (slow or steady-to-brisk) and who were initially free of dementia ( n = 415,110). Incident all-cause dementia was obtained from hospital and death registry records, and structural brain volumes (right and left hippocampus volumes, total gray matter volume, and volume of white matter hyperintensities) were measured from a subset of participants ( n = 33,113). Cox proportional hazard models and generalized linear models were used to assess associations between exposures and outcomes. RESULTS: Slow walking pace and the presence of APOE-&#x3b5;4 allele were associated with increased dementia risk (HR = 1.79 [95% CI = 1.66-1.93], P < 0.001; HR = 3.06 [2.90-3.23], P < 0.001, respectively), and there was an interaction between these associations, indicating that the association of walking pace with dementia risk is modified by APOE-&#x3b5;4 status (reference group: HR Steady-Brisk/APOE-&#x3b5;4- = 1; HR Slow/APOE-&#x3b5;4- = 2.03 [1.84-2.25], P < 0.001; HR Steady-Brisk/APOE-&#x3b5;4+ = 3.21 [3.02-3.41], P < 0.001; HR Slow/APOE-&#x3b5;4+ = 4.99 [4.48-5.58], P < 0.001). Slow self-reported walking pace was associated with worse brain volume outcomes, and these associations were not modified by APOE-&#x3b5;4 genotype. CONCLUSIONS: These results suggest walking pace and APOE-&#x3b5;4 status independently influence brain volume outcomes, but both factors independently and jointly contribute to increased dementia risk. Individuals with both risk factors (slow walking pace and APOE-&#x3b5;4 allele) show the strongest associations with dementia risk.

Self Report↗
PubMed2002

Cluster analysis of Wisconsin Breast Cancer dataset using self-organizing maps.

This work deals with multidimensional data analysis, precisely cluster analysis applied to a very well known dataset, the Wisconsin Breast Cancer dataset. After the introduction of the topics of the paper the cluster analysis concept is shortly explained and different methods of cluster analysis are compared. Further, the Kohonen model of self-organizing maps is briefly described together with an example and with explanations of how the cluster analysis can be performed using the maps. After describing the data set and the methodology used for the analysis we present the findings using textual as well as visual descriptions and conclude that the approach is a useful complement for assessing multidimensional data and that this dataset has been overused for automated decision benchmarking purposes, without a thorough analysis of the data it contains.

Breast Neoplasms↗
PubMed2002

How useful are observational reports in the evaluation of interventions for radiation morbidity?: an analysis of formalin therapy for late radiation proctitis.

BACKGROUND AND PURPOSE: Evidence-based medicine requires the systematic and critical evaluation of published and unpublished trials. Problems arise when a clinical condition is relatively rare and the only available data relate to experiential knowledge. The way forward would be to recommend the development of good quality randomized controlled studies. Until then, we are left with the situation where some information exists, albeit in the form of case reports and small series. Should this information be used and what features would determine its strength? METHODS: Using the example of formalin therapy in haemorrhagic radiation proctitis, a treatment for a rare condition, we were able to identify 16 published studies, 13 of which were retrospective and three of which were prospective. The quality of reporting detail was assessed by comparison to the features in a 'proposed minimum dataset' for an uncontrolled study addressing this topic. RESULTS: The mean score for quality of reporting detail for these studies was 50.6% (range 25-70%). Earlier studies reported a significantly higher response rate than subsequent studies and although there was a tendency for smaller studies to report higher response rates, this was not significant. The score for detail of reporting did not improve with year of publication and the correlation between the size of the study and the detail of reporting was not statistically significant. CONCLUSIONS: The information presented is of exceedingly variable quality. If these studies are to be used, where insufficient controlled trials are available, they should be scored for methodology, and these scores used to assist interpretation of results. This would be facilitated if an accepted reporting format including specific criteria was available.

Evaluation Studies as Topic↗
PubMed2006

Mining gene expression data by interpreting principal components.

BACKGROUND: There are many methods for analyzing microarray data that group together genes having similar patterns of expression over all conditions tested. However, in many instances the biologically important goal is to identify relatively small sets of genes that share coherent expression across only some conditions, rather than all or most conditions as required in traditional clustering; e.g. genes that are highly up-regulated and/or down-regulated similarly across only a subset of conditions. Equally important is the need to learn which conditions are the decisive ones in forming such gene sets of interest, and how they relate to diverse conditional covariates, such as disease diagnosis or prognosis. RESULTS: We present a method for automatically identifying such candidate sets of biologically relevant genes using a combination of principal components analysis and information theoretic metrics. To enable easy use of our methods, we have developed a data analysis package that facilitates visualization and subsequent data mining of the independent sources of significant variation present in gene microarray expression datasets (or in any other similarly structured high-dimensional dataset). We applied these tools to two public datasets, and highlight sets of genes most affected by specific subsets of conditions (e.g. tissues, treatments, samples, etc.). Statistically significant associations for highlighted gene sets were shown via global analysis for Gene Ontology term enrichment. Together with covariate associations, the tool provides a basis for building testable hypotheses about the biological or experimental causes of observed variation. CONCLUSION: We provide an unsupervised data mining technique for diverse microarray expression datasets that is distinct from major methods now in routine use. In test uses, this method, based on publicly available gene annotations, appears to identify numerous sets of biologically relevant genes. It has proven especially valuable in instances where there are many diverse conditions (10's to hundreds of different tissues or cell types), a situation in which many clustering and ordering algorithms become problematic. This approach also shows promise in other topic domains such as multi-spectral imaging datasets.

Algorithms↗
PubMed2005

GrainGenes 2.0. an improved resource for the small-grains community.

GrainGenes (http://wheat.pw.usda.gov) is an international database for genetic and genomic information about Triticeae species (wheat [Triticum aestivum], barley [Hordeum vulgare], rye [Secale cereale], and their wild relatives) and oat (Avena sativa) and its wild relatives. A major strength of the GrainGenes project is the interaction of the curators with database users in the research community, placing GrainGenes as both a data repository and information hub. The primary intensively curated data classes are genetic and physical maps, probes used for mapping, classical genes, quantitative trait loci, and contact information for Triticeae and oat scientists. Curation of these classes involves important contributions from the GrainGenes community, both as primary data sources and reviewers of published data. Other partially automated data classes include literature references, sequences, and links to other databases. Beyond the GrainGenes database per se, the Web site incorporates other more specific databases, informational topics, and downloadable files. For example, unique BLAST datasets of sequences applicable to Triticeae research include mapped wheat expressed sequence tags, expressed sequence tag-derived simple sequence repeats, and repetitive sequences. In 2004, the GrainGenes project migrated from the AceDB database and separate Web site to an integrated relational database and Internet resource, a major step forward in database delivery. The process of this migration and its impacts on database curation and maintenance are described, and a perspective on how a genomic database can expedite research and crop improvement is provided.

Breeding↗
PubMed1992

The ESRC/UFC-ISC 1991 census of population initiative: delivering the data of the decade.

"This article describes the programme of data purchase, dissemination, training and development support which the Economic and Social Research Council (ESRC) in collaboration with the Information Systems Committee of the Universities Funding Council (UFC-ISC) has put in place to...[inform users about] new products derived from the Census [of the United Kingdom]. As a preliminary some background to the census itself and to the Census Initiative are provided." Topics covered include census outputs, new features of the census, linked datasets, linked software, and Census Initiative datasets, training and development programs, and organization.

Censuses↗
PubMed2003

Phylogeny for the faint of heart: a tutorial.

Phylogenetic trees seem to be finding ever broader applications, and researchers from very different backgrounds are becoming interested in what they might have to say. This tutorial aims to introduce the basics of building and interpreting phylogenetic trees. It is intended for those wanting to understand better what they are looking at when they look at someone else's trees or to begin learning how to build their own. Topics covered include: how to read a tree, assembling a dataset, multiple sequence alignment (how it works and when it does not), phylogenetic methods, bootstrap analysis and long-branch artefacts, and software and resources.

Amino Acid Sequence↗
PubMed2005

Expert knowledge without the expert: integrated analysis of gene expression and literature to derive active functional contexts.

MOTIVATION: The interpretation of expression data without appropriate expert knowledge is difficult and usually limited to exploratory data analysis, such as clustering and detecting differentially regulated genes. However, comparing experimental results against manually compiled knowledge resources might limit or bias the perspective on the data. Thus, manual analysis by experts is required to obtain confident predictions about involved processes. RESULTS: We present an algorithm to simultaneously derive interpretations of expression measurements together with biological hypotheses from biomedical publications. It identifies active functional contexts ('concepts'), i.e. gene clusters that exhibit both a significant gene expression as well as a coherent literature profile. Manual intervention by an expert in specifying prior knowledge is not required. The approach scales to realistic applications and does not rely on controlled vocabularies or pathway resources. We validated our algorithm by analyzing a current juvenile arthritis dataset. A number of gene clusters and accompanying literature topics are identified as an interpretation of the data that coincide well with the phenotype and biological processes known to be involved in the disease. We demonstrate that generated clusters are both more sensitive and more specific than Gene Ontology categories detected on the same data. The method allows for in-depth investigation of subsets of genes, the associated literature topics and publications. AVAILABILITY: Supplementary data on clusters is available upon request.

Expert Systems↗