BackgroundDuring the COVID-19 pandemic, large-scale sequencing generated millions of SARS-CoV-2 genomes in public repositories including GenBank and GISAID. However, most records lack detailed patient metadata, including demographic information and clinical outcomes. This lack of host-associated information limits their utility for large-scale pathogen genomics analyses. Although sequence records linked to journal publications may contain relevant metadata, systematically extracting and linking this information requires substantial manual effort. ObjectiveThis study aimed to assess host metadata completeness in GenBank SARS-CoV-2 records and to demonstrate, through an exploratory case study, analytical opportunities enabled by enriched clinical and demographic annotations for genomic epidemiology. MethodsThe authors searched LitCovid for PubMed Central (PMC) articles published between January 2023 and December 2024 that reported original complete SARS-CoV-2 genome sequences deposited in GenBank with sequence-specific patient metadata from human hosts. Two independent reviewers screened eligible articles and manually extracted metadata on sample collection, demographics, treatments, serology, vaccination status, infection presentation, and clinical outcomes. Enriched metadata were defined as patient information in the publication but absent from GenBank records. Synonymous clinical and demographic terms were standardized using SNOMED CT, and Charlson Comorbidity Index scores were calculated when metadata were available. SARS-CoV-2 genomes were retrieved, assembled, and subjected to quality control using standard bioinformatics tools. To demonstrate enriched metadata’s analytical value, we selected a subset of genomes with longitudinal sequencing, comprising 100 genomes from 34 patients across 4 studies for exploratory analysis of within-host viral evolution and patient outcomes. ResultsAmong 116,600 articles identified through our PMC/LitCovid screening framework, approximately 0.02% (n=21) reported original complete SARS-CoV-2 genomes deposited in GenBank with accessible sequence-specific metadata. Within this eligible set, GenBank records contained on average 22% completeness for host metadata in our extraction schema. Completeness was confined to sample fields (averaging 81%); host demographic and clinical fields averaged 2%. Manual enrichment increased overall completeness by 30% on average, recovering 56% (14/25) of metadata types absent from GenBank records. In the longitudinal subset, enriched metadata enabled host-stratified analyses, showing nominal associations between immunocompromised status and higher within-host evolutionary rates (P=.02) and unique amino acid mutations (P=.04). Models with enriched patient and treatment metadata outperformed mutation-only models for mortality, hospitalization, and infection duration. ConclusionsEnriched host metadata improve the utility of pathogen genomic data by enabling analyses linking viral variation with demographics and clinical outcomes. Despite limited clinical and demographic information in examined GenBank records, manual enrichment facilitated a more comprehensive view of viral evolution and disease dynamics than sequence data alone. The case study’s genotype-phenotype associations are exploratory and require validation in larger, independently collected cohorts. These findings highlight the need for more standardized, structured, and accessible patient metadata deposition with genomic sequences to strengthen pathogen genomics and precision public health research.
Unlabelled:JMIR Bioinformatics and Biotechnology announced a strategic partnership with the MidSouth Computational Biology and Bioinformatics Society (MCBIOS) in late 2025; this partnership establishes JMIR Bioinformatics and Biotechnology as the official journal of MCBIOS. This collaboration reflects a shared commitment to advancing computational biology, bioinformatics, and biotechnology through open science, interdisciplinary collaboration, and real-world data. By connecting MCBIOS' community-building and professional development initiatives with JMIR Publications' open-access publishing platform, this partnership aims to support emerging researchers, accelerate the dissemination of innovative computational methods and artificial intelligence applications, and strengthen data-driven research across medicine and biology.
BackgroundCattle are among the most important livestock resources in Ethiopia, contributing significantly to the agricultural economy and rural livelihoods. They provide meat, milk, hides, draft power for crop production, and serve as a major source of income for farmers. Despite their vital role, cattle productivity is often constrained by various diseases, particularly parasitic diseases. One of the most significant of these is bovine fasciolosis, a condition caused by ingestion of metacercariae of liver flukes belonging to the genus Fasciola. ObjectiveThis study aimed to assess the prevalence and associated risk factors of bovine fasciolosis in Bahir Dar, Ethiopia. MethodsA cross-sectional study was conducted from November 2021 to April 2022. A total of 384 cattle were randomly selected from different locations within the study area. Animals of all age groups and both sexes were included. Fecal samples were collected directly from the rectum of each animal using clean, labeled containers. The samples were examined using standard coprological techniques, specifically the sedimentation method, to detect liver fluke eggs. All findings were recorded, and the data were analyzed using descriptive statistical methods. ResultsThe overall prevalence of fasciolosis was 49.21% (n=189). Based on origin, Sebatamit had the most incidence at 61.84% (n=47), followed by Kebele 11 at 59.37% (n=57), Tikurit at 50% (n=59), and Latammba at 27.65% (n=26). Statistical analysis revealed significant disparities in occurrence among areas. Cattle in poor condition had the largest prevalence (n=80, 64%), followed by medium condition (n=85, 50%) and fat cattle (n=24, 26.96%). This variation was statistically significant. Age-group analysis revealed comparable prevalence rates, with young cattle at 50.38% (n=65), adults at 47.33% (n=71), and elderly cattle at 50.47% (n=53), with no significant differences found. There were no significant sex-related variations in prevalence, with males exhibiting a prevalence of 49.73% (n=93) and females 48.73% (n=96). Local cattle had a slightly higher prevalence (n=111, 51.62%) than crossbreeds (n=78, 46.15%), although the difference was not statistically significant (P=.29). ConclusionsThese findings underscore the need for targeted, location-specific control strategies and highlight the importance of improved nutritional and health management practices to reduce the burden of fasciolosis in cattle populations.
Background:Non-small cell lung cancer (NSCLC) is one of the leading causes of cancer-related mortality. Programmed cell death receptor-1 (PD-1) immunotherapy has shown results in the treatment of NSCLC; however, not all patients respond effectively to it. Identifying predictive biomarkers for PD-1 therapy response is critical to improving patient outcomes and treatment strategies. Traditional methods of biomarker discovery often fall short in terms of accuracy and comprehensiveness. Recent advancements in deep learning provide a powerful approach to analyze complex genomic data to resolve this issue. Objective:This study aims to leverage deep neural networks (DNNs) to identify genomic biomarkers predictive of patient responses to PD-1 immunotherapy in NSCLC. DeepImmunoGene is a model designed using a reduced feature set to identify the most critical biomarkers. We use feature selection to reduce the space and apply deep learning to identify the highly predictive gene subset. Methods:Differentially expressed genes were identified in RNA-seq data from 355 patients with NSCLC using the LIMMA package in R, followed by preprocessing with log2 transformation, removing outliers, and detecting easily identified genes. Machine learning models, including support vector machines, extreme gradient boosting (XGBoost), and DNNs, were applied to gene expression data to predict patient responses to immunotherapy. Key predictive genes were identified through model interpretation techniques, and differences in model performance were assessed for statistical significance. Primarily, the metric used identifies which genes serve as key biomarkers in regard to immunotherapy detection. Results:Initially, we identified 1093 differentially expressed genes from RNA-seq data of 355 patients. We then trained models using SVM, XGBoost, and DNN to predict immunotherapy response. The DNN model outperformed both SVM and XGBoost with an accuracy of 82%, an area under the curve of 90%, and recall of 85%. To identify key biomarkers, we performed a permutation importance analysis, narrowing down the gene set to 98 genes. DeepImmunoGene, trained on these 98 genes, showed superior results, with an accuracy of 87% and an area under the curve of 95%. The top 36 upregulated genes in responders and 62 upregulated genes in nonresponders were identified, which could serve as potential biomarkers for predicting response to PD-1 inhibitors. These findings suggest that DeepImmunoGene can reliably forecast immunotherapy outcomes and aid in biomarker discovery, supporting the development of more personalized treatment strategies in NSCLC. Conclusions:The DeepImmunoGene predictive model identified 36 upregulated genes that may represent candidate genomic biomarkers associated with response to PD-1 immunotherapy in patients with NSCLC. Notably, the 10 most significant genes offer valuable insights into the underlying mechanisms of treatment responses. These biomarkers may not only aid in predicting which patients are more likely to respond to PD-1 immunotherapy but also offer insights into the molecular differences associated with nonresponse.
