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PubMed2025

The Mycobacterium tuberculosis Transposon Sequencing Database (MtbTnDB): A Large-Scale Guide to Genetic Conditional Essentiality.

Characterizing genetic essentiality across various conditions is fundamental for understanding gene function. Transposon sequencing (TnSeq) is a powerful technique to generate genome-wide essentiality profiles in bacteria and has been extensively applied to Mycobacterium tuberculosis (Mtb). Dozens of TnSeq screens have yielded valuable insights into the biology of Mtb in vitro, inside macrophages, and in model host organisms. Despite their value, these Mtb TnSeq profiles have not been standardized or collated into a single, easily searchable database. This results in significant challenges when attempting to query and compare these resources, limiting our ability to obtain a comprehensive and consistent understanding of genetic conditional essentiality in Mtb. We address this problem by building a central repository of publicly available Mtb TnSeq screens, the Mtb transposon sequencing database (MtbTnDB). The MtbTnDB is a living resource that encompasses to date ≈150 standardized TnSeq screens, enabling open access to data, visualizations, and functional predictions through an interactive web app (www.mtbtndb.app). We conduct several statistical analyses on the complete database, such as demonstrating that (i) genes in the same genomic neighborhood have similar TnSeq profiles, and (ii) clusters of genes with similar TnSeq profiles are enriched for genes from similar functional categories. We further analyze the performance of machine learning models trained on TnSeq profiles to predict the functional annotation of orphan genes in Mtb. By facilitating the comparison of TnSeq screens across conditions, the MtbTnDB will accelerate the exploration of conditional genetic essentiality, provide insights into the functional organization of Mtb genes, and help predict gene function in this important human pathogen.

DNA Transposable Elements↗

PubMed2026

MetagenomicKG: a knowledge graph for metagenomic applications.

MOTIVATION: The sheer volume and variety of genomic content within microbial communities makes metagenomics a field rich in biomedical knowledge. To traverse these complex communities and their vast unknowns, metagenomic studies often depend on distinct reference databases, such as the Genome Taxonomy Database (GTDB), the Kyoto Encyclopedia of Genes and Genomes (KEGG), and the Bacterial and Viral Bioinformatics Resource Center (BV-BRC), for various analytical purposes. These databases are crucial for the genetic and functional annotation of microbial communities. Nevertheless, the inconsistent nomenclature or identifiers of these databases present challenges for effective integration, representation, and utilization. Knowledge graphs (KGs) offer an appropriate solution by organizing biological entities from different databases to standardized identifiers, allowing their interrelations to be captured into a cohesive network regardless of the naming conventions used in each source. The graph structure not only facilitates the unveiling of hidden patterns but also enriches our biological understanding with deeper insights. Despite KGs having shown potential in various biomedical fields, their application in metagenomics remains underexplored. RESULTS: We present MetagenomicKG, a novel knowledge graph specifically tailored for metagenomic analysis. MetagenomicKG integrates taxonomic, functional, and pathogenesis-related information on the human microbiome sourced from various databases, and further connects these with existing biomedical KGs to expand the biological network. Through various case studies involving the human microbiome, we demonstrate its utility in enabling hypothesis generation regarding the relationships between microbes and diseases, generating sample-specific graph embeddings, and providing robust pathogen prediction. CODE AVAILABILITY: The source code and technical details for constructing the MetagenomicKG and reproducing all analyses are available on GitHub at https://github.com/KoslickiLab/MetagenomicKG. The data used in this manuscript, including the pre-built files and use case input data, are archived on Zenodo with DOI: 10.5281/zenodo.17546861.

Metagenomics↗

PubMed2026

Palaeoproteomic Deconvolution of Physical and Genetic Collagen Mixtures.

Species identification in palaeoproteomics relies on genome-derived protein sequences which are often poor-quality, and lacks tools to cope with multi-species samples. Here, we address both challenges through the analysis of "physical and genetic mixtures". Species that are absent from our database are considered a "genetic mixture", i.e. a patchwork of peptides from closely related species. Inversely, various overlapping peptide stretches allow us to resolve complex "physical mixtures". This is benchmarked by analysing physical mixtures of modern bone fragments, including genetic mixtures. We illustrate the impact of our approach via a rapid and high-throughput analysis of >2500 bone fragments, revealing the Eemian-era faunal environment around Scladina Cave, including the first Palaeoloxodon antiquus identified at this site.

bioarchaeology↗

PubMed2025

Unique signatures of highly constrained genes across publicly available genomic databases.

PURPOSE: Publicly available genomic databases are critical in understanding human genetic variation. They also provide unique insights into patterns of genetic constraints and their relationship with human disease. METHODS: We utilized one of the largest publicly available databases, Genome Aggregate Database, to determine genes that are highly constrained for only loss-of-function, only missense, and both loss-of-function/missense variants. We identified their unique signatures and explored their causal relationship with human diseases. Those genes were also evaluated for chromosomal location, tissue-level expression, Gene Ontology analysis, and gene family categorization using multiple publicly available databases. RESULTS: We identified unique patterns of inheritance, protein size, and enrichment in distinct molecular pathways for those constrained genes associated with human disease. In addition, we identified genes that are currently not known to cause human disease, which may be excellent gene discovery candidates. CONCLUSION: We elucidate biological pathways of highly constrained genes that expand our understanding of critical cellular proteins. The findings can also advance research in rare diseases.

Humans↗

PubMed2025

dbscATAC: a resource of single-cell super-enhancers/enhancers and gene markers derived from scATAC-seq data.

MOTIVATION: scATAC-seq enables high-resolution mapping of cis-regulatory elements. It has been widely applied to uncover cell-type-specific regulatory networks and complement scRNA-seq analysis in numerous studies. However, a large number of datasets generated by scATAC-seq remain underutilized due to limited exploration of super-enhancers/typical enhancers and gene markers. A comprehensive resource enabling cell-type-specific annotation of cis-regulatory elements and their dynamic enhancer-gene linkages remains an urgent unmet need for scATAC-seq. RESULTS: We present dbscATAC, a specialized single-cell database for annotating super-enhancers, gene markers, and enhancer-gene interactions derived from scATAC-seq data. Using improved machine learning algorithms, we identified 213 835 super-enhancers across 520 tissue/cell types from three species, as well as 347 484 gene markers, 13 470 526 enhancers, and 10 402 346 enhancer-gene interactions derived from 1 668 076 single cells spanning 1028 tissue/cell types in 13 species. An easy-to-use online platform with multiple analytic modules and hierarchical query options was developed for searching, browsing and visualizing single-cell super-enhancers, enhancers, and gene markers. dbscATAC provides a comprehensive resource to facilitate the exploration of enhancer landscapes, gene regulation, and cell-type-specific characteristics in single-cell epigenomics. AVAILABILITY AND IMPLEMENTATION: The database with all the super-enhancer/enhancer annotation data is available at http://singlecelldb.com/dbscATAC/index.php. And the source code of dbscATAC for prediction of SEs, enhancers, and gene markers are available at https://github.com/EvansGao/dbscATAC. The source code, tissue/cell type description, and data summary can be downloaded at DOI: 10.6084/m9.figshare.28706414.scATAC-seq, Database, Super-enhancers/enhancers, Gene markers.

Enhancer Elements, Genetic↗

PubMed2026

Expanding vaginal microbiome pangenomes via a custom MIDAS database reveals Lactobacillus crispatus accessory genes associated with cervical dysplasia.

The vaginal microbiome plays a central role in reproductive health. Vaginal microbiome dysbiosis is associated with many adverse reproductive health outcomes, but most studies have focused on associations at the species level. The potential contribution of intraspecies microbial variation, especially gene content differences across bacterial strains, remains underexplored in reproductive health contexts. The Metagenomic Intra-Species Diversity Analysis (MIDAS) framework enables such analyses, but depends on comprehensive reference databases. We constructed a MIDAS-compatible pangenome database from over 18,000 genomes in the Vaginal Microbiome Genome Collection (VMGC). Compared to the Genome Taxonomy Database (GTDB)-derived reference, the VMGC-derived database expanded the pangenomes of prevalent vaginal species, better capturing vaginal-specific intraspecies diversity. Applying this database to vaginal samples from a cervical dysplasia cohort, we identified 13 Lactobacillus crispatus accessory genes significantly associated with cervical dysplasia, including a HicAB toxin-antitoxin system, three transcriptional regulators, and three phage-derived genes. These findings highlight the utility of body site-specific reference resources and shotgun metagenomic sequencing for uncovering intraspecies microbial variation relevant to reproductive health.IMPORTANCEThe vaginal microbiome plays a critical role in reproductive health, and different bacteria from the same species can carry different genes that influence how the strains interact with the host and other microbes. These strain-level differences are often overlooked when microbiomes are analyzed only at the species level. Existing genomic reference databases are heavily biased toward gut and environmental bacteria, leaving the genetic diversity of vaginal microbes understudied. We built a specialized reference database from over 18,000 vaginal bacterial genomes that better reflects this diversity. We then applied this resource to quantify gene-level variation in vaginal samples from a cervical dysplasia cohort. Focusing on Lactobacillus crispatus, a prevalent and often beneficial vaginal species, we identified 13 genes that were more common in women with cervical dysplasia than in controls. This work demonstrates that body site-specific genomic resources are essential for uncovering strain-level bacterial differences relevant to reproductive health.

Lactobacillus crispatus↗

PubMed2026

Genetic diversity and molecular mechanisms in hypertrophic cardiomyopathy: toward personalized therapy.

Hypertrophic cardiomyopathy (HCM) is the most common inherited cardiac muscle disorder, yet contemporary genomic and mechanistic research still lacks a cohesive model explaining how diverse genetic architectures give rise to heterogeneous phenotypes. This review synthesizes advances across sarcomeric and nonsarcomeric mutations, including intermediate-effect variants, polygenic modifiers, and ancestry-dependent sources of variant misclassification to elucidate how these factors govern disease penetrance and clinical expression. It critically evaluates how genetic diversity intersects with key molecular pathways, including sarcomeric hypercontractility, calcium dysregulation, mitochondrial energy deficiency, and transforming growth factor-β (TGF-β) and protein kinase B (AKT)/mammalian target of rapamycin (mTOR) signaling, to drive hypertrophic and fibrotic remodeling. Emerging mechanism-based therapies, such as myosin inhibition, allele-specific silencing, clustered regularly interspaced short palindromic repeats (CRISPR)-based correction, and metabolic modulation, are examined with respect to their capacity to modify upstream molecular drivers rather than downstream hemodynamic consequences. Persistent challenges, including variants of uncertain significance classification, ancestry-biased databases, inequitable access to genetic testing, and unresolved safety concerns for gene-based therapies, are critically assessed as major barriers to precision-medicine integration. By linking genetic architecture, molecular pathogenesis, and targeted interventions, this review advances a contemporary, mechanistically grounded framework that informs both individualized management and future research directions. Future research should prioritize pathway-specific therapeutics, functional and mechanistic validation of emerging variants, deeper physiologic phenotyping to refine disease modeling, and accelerate translation throughout the continuum of HCM pathophysiology.

Humans↗

PubMed2025

HCSeeker: A classification tool for human genetic variant hot and cold spots designed for PM1 and benign criteria in the ACMG-AMP guideline.

PURPOSE: The PM1 criterion, which states that a variant is located in a mutational hot spot and/or critical and well-established functional domain without benign variation (such as the active site of an enzyme), is considered moderate evidence for assessing its pathogenicity. Although guidelines from the American College of Medical Genetics and Genomics and the Association for Molecular Pathology are widely adopted, the PM1 criterion remains limited from lacking a reliable database of variant hot spots. Compared with hot spots, cold spots are neglected by the guidelines. To improve variant classification, we suggest including cold spots for supporting benign classifications. Consequently, we have developed the HCSeeker to provide data support for PM1 and the "Benign" criteria. METHODS: HCSeeker uses the Kernel Density Estimation and the Expectation-Maximization algorithm to identify hot- and cold-spot regions. RESULTS: Through HCSeeker, we identified 988 hot spots and 682 cold spots across 889 genes and provided a public database (http://www.genemed.tech/hcseeker/) for researchers and clinicians to query variant locations, facilitating the application of American College of Medical Genetics and Genomics and the Association for Molecular Pathology PM1 or "Benign" criteria. CONCLUSION: We developed the HCSeeker tool, which can effectively identify variant hot and cold spots within genes to enhance the interpretability of gene variants.

Humans↗

PubMed2026

The Saccharomyces Genome Database-a history of ideas and accomplishments, 1994-2026.

The Saccharomyces Genome Database (SGD) is one of the longest-running and most consequential biological databases in the world. Founded in the early 1990s at Stanford University under the visionary leadership of David Botstein and developed under the long-term technical direction of J. Michael Cherry, SGD has served for more than three decades not only as the authoritative knowledge center for the budding yeast Saccharomyces cerevisiae, but also as the source for much of the fundamentals of eukaryotic biology. This history traces the arc of a remarkable intellectual and scientific project: beginning with the challenge of building the very first integrated eukaryotic genome database and evolving across 30 years into a global knowledge hub for genetics, functional genomics, and human disease research. The history is organized chronologically, with each section highlighting the central ideas, technical developments, and concrete accomplishments of that period.

Databases, Genetic↗

PubMed2025

PAHG: the database of human multi-gene families.

BACKGROUND: In the early vertebrate history, gene duplications, including single-gene, segmental-gene (SSD), and whole-genome duplication (WGD), formed multigene families. Despite efforts to classify metazoan multigene families hierarchically for evolutionary insight, a gap exists in accessible, curated resources for human/vertebrate multigene families. RESULTS: Addressing this, we present the Phylogenomic Analysis of Human Genome (PAHG) database. It focuses on curated multigene families in the human genome, particularly within four paralogons: HOX-bearing (Hsa:2/7/12/17), FGFR-bearing (Hsa:4/5/8/10), MHC-bearing (Hsa:1/6/9/19), and chromosomes 1/2/8/20. CONCLUSION: The current PAHG version details the phylogenetic history of 221 human multigene families (1247 gene members) with 15,231 protein sequences from diverse metazoans. It provides insights into gene duplication timings, co-duplication events, and their relationships with human genome syntenic organization. The PAHG database addresses the lack of accessible resources, offering valuable information on human/vertebrate multigene family evolution. Access the PAHG database at: https://www.pahgncb.com/ and http://pahg.qau.edu.pk/ . This resource enriches our understanding of vertebrate genetic evolution.

Humans↗

PubMed2026

National genomic projects in Asia and Africa: a review.

National genome projects (NGPs) are increasingly shaping precision medicine by improving representation of population-specific genetic diversity. This review compiles findings from NGPs across Asia and Africa, regions that remain underrepresented in global genomic databases despite their extensive demographic and genetic diversity. A total of 53 studies from 24 countries were identified to understand (1) the genomic approach utilized, (2) novel findings that have emerged, and (3) strategies for improving research in these regions. The NGPs implement population-based variome databases (20 NGPs), linear reference genome assemblies (8 NGPs), and graph-based pangenome assemblies (1 NGP). Novel variants ranged between 0.28% (China) and 19.6% (Iran), whereas rare variants accounted for up to 88.9% of the detected variants in the Chinese population. Each NGP documents its country's evolutionary and migration history, which impacts disease frequency and pharmacogenomic variants. Clinically, NGPs revealed strong population stratification in disease-associated and pharmacogenomic variants. For example, the GJB2 rs72474224 hearing-loss variant ranged from 13% in Vietnam and 12% in Hong Kong to 0.0894% in Turkey, while the VKORC1 rs9923231 pharmacogenomic variant reached 89.2% in Taiwan but was 20%-25% in European-related Russian subpopulations. These findings demonstrate that clinically relevant allele frequencies, pathogenicity assessments, and drug-response markers differ substantially across ancestries. This review highlights ongoing efforts and strategies to enhance the representativeness of genomic data through NGPs in Asia and Africa. We also suggest future directions for national projects, including integrating family-based studies, multi-omic data, and standardized pipelines to accelerate discovery and support the equitable implementation of precision medicine.

Humans↗

PubMed2025

EucaMOD: a comprehensive multi-omics database for functional genomics research and molecular breeding of fast-growing eucalyptus trees.

Eucalyptus, one of the most widely planted plantation tree species globally, is primarily found in tropical and subtropical regions and contributes significantly to economic and social benefits. With advances in sequencing technologies, there is an increasing demand for the systematic analysis of multi-omics data among Eucalyptus species to enhance genetic breeding efforts. Although several early genomic databases have been established for eucalyptus, they have not been updated in a timely manner and lack recent multi-omics data, rendering them insufficient for current research needs. To address this gap, we developed the eucalyptus multi-omics database (EucaMOD, http://eucalyptusggd.net/eucamod), a comprehensive resource for cross-omics studies. In this study, we functionally annotated 45 eucalyptus genomes and structurally annotated 15, conducting comparative genomics and pan-proteomics analyses across all genomes. Additionally, we analyzed eucalyptus transcriptome, epigenome, and variome data through standardized workflows, enabling the in-depth mining and reanalysis of multi-omics datasets. EucaMOD is the most comprehensive multi-omics database for eucalyptus to date and includes data from 45 genomes (39 species), 870 mRNA-seq samples, 17 miRNA-seq samples, 52 epigenomic datasets (histone modifications and transcription factor binding), and genetic variation data from 1219 samples. To support functional genomics and molecular breeding research, the database is organized into the following 11 modules: Home, Species, Genomics, Comparative genomics, Pan-proteomics, Transcriptomics, Epigenetics, Variomics, Tools, Download, and Help. EucaMOD also offers online analysis tools for data mining, providing free public services to aid eucalyptus gene function and genetic engineering studies.

Eucalyptus↗

PubMed2026

Genome-wide annotation of human multi-nucleotide variants reveals widespread functional differences from single nucleotide variants.

Multi-nucleotide variants (MNVs) represent a crucial yet underexplored category of genetic variation. Despite previous studies highlighting the prevalence and potential biological impact of MNVs in populations, comprehensive identification and detailed functional annotation of MNVs remain challenging. Here, we develop MNVAnno, a toolbox for rapid identification and annotation of complex MNVs, and utilize it to identify 3,984,258 MNVs from 700,134 human samples, expanding the human MNV list to 8,199,654. Our analysis reveals that MNVs can not only lead to distinct amino acid changes from their constituent single-nucleotide variants, but also significantly impact the function of non-coding regions. Furthermore, through genome-wide association studies, we identify some MNVs associated with multiple cancers, and establish the Human MNV Database to facilitate MNV research. Our study emphasizes the importance of MNV annotation, broadens the human MNV landscape, and opens avenues for exploring genetic variation in phenotypes and diseases.

Humans↗

PubMed2026

A Genetic Study of 66 Individuals With Syndromic Velopharyngeal Insufficiency.

ObjectiveVelopharyngeal insufficiency (VPI) is a form of velopharyngeal dysfunction caused by anatomical anomalies in the velopharyngeal sphincter. Although genetic causes such as 22q11 deletion syndrome are recognised, the broader genetic basis remains poorly understood. This study investigated the genetic aetiology of VPI.DesignWe conducted a phenotypic search on the DECIPHER database using the term 'Velopharyngeal Insufficiency' and identified genetic variants in these patients. These were classified using ACMG guidelines. Literature searches and network analyses examined gene roles and their contribution to sphincter development.PatientsWe identified 66 patients on DECIPHER with VPI.ResultsNinety-five percent of patients presented with syndromic VPI, commonly observed phenotypes included neurodevelopmental abnormalities and facial dysmorphology. Five patients (7.6%) had cleft palate. Pathogenic or likely pathogenic variants were identified in 56.1% of those with reported genetic variants (32/57); 26.3% through copy number variants and 29.8% through sequence variants (SVs). Chromosome 22q11.2 aberrations were the most frequently observed finding in the cohort; 7 patients carried deletions and 2 carried duplications. Independent truncating SVs in KMT2A and CAMTA1 were observed in multiple individuals. Network analyses and literature review of 26 genes prioritised for potential relevance to VPI revealed 2 broad functions: regulating gene expression and signalling pathways, contributing to palatogenesis and cranial-base development.ConclusionThis study demonstrates a high rate of pathogenic or likely pathogenic genetic findings in a syndromic VPI cohort. The findings highlight several recurrent genomic regions and biologically plausible genes that may contribute to VPI beyond the well-known 22q11 deletion syndrome.

development↗

PubMed2026

CamK-DB: A k-mer MinHash fingerprint database for reference-free genotyping of Camellia accessions.

Tea (Camellia sinensis L.), a major global economic crop in Asia, poses challenges for genetic identification because its highly heterozygous, repetitive genome reduces the efficacy of conventional single-nucleotide polymorphism (SNP) and microsatellite markers, and interspecific hybridization further complicates the situation. To address these issues, CamK-DB was developed as a reference-free Camellia fingerprinting database built on MIKE MinHash sketches. We curated 418 candidate resequencing datasets, and built a database using standardized 5× genome-coverage fingerprints. Each accession is stored as a MIKE. jac fingerprint generated with k = 21 and recommended sketch/pre_cnt = 2000. CamK-DB provides a command-line interface for data management and a custom C++ query engine that computes top-10 matches using Jaccard similarity, complemented by a QT-based graphical interface for interactive analysis. This resource offers a robust and scalable framework for precise and routine germplasm identification, genomic phylogenetic inference, and strategic breeding program design. CamK-DB (database and code) is publicly available at https://github.com/sc-zhang/CamK-DB. CamK-DB binaries are provided for Windows 10/11 and Linux (x86_64, glibc ≥ 2.27).

Databases, Genetic↗

PubMed2026

PubMind: literature-based genetic variant extraction and functional annotation using large language models.

Biomedical literature contains extensive functional knowledge on genetic variants, but much remains inaccessible in unstructured text. Existing resources such as ClinVar and HGMD remain limited by coverage, submission bias, update frequency, and sparse annotation. We develop PubMind, an artificial intelligence (AI) framework that uses large language models (LLMs) to triage and extract variant-function-disease associations and supporting evidence from biomedical text. PubMind captures single-nucleotide, copy-number, structural, and gene-fusion variants, and normalizes records to genomic and transcriptomic coordinates. Benchmarking shows >90% accuracy for variant recognition and 99% precision for disease extraction. Applied to >41 million PubMed abstracts and >5 million full-text articles, PubMind generates PubMind-DB, a database of ~1.3 million unique variants with contextual annotations, accessible via web interface and API. Only ~10% of PubMind variants overlap with ClinVar, and >80% of them show concordant pathogenicity labels. PubMind transforms unstructured biomedical text into structured genomic knowledge, advancing variant interpretation for precision medicine.

Large Language Models↗

PubMed2025

Alzheimer's subtypes A supervised, unsupervised, multimodal, multilayered embedded recursive (SUMMER) AI study.

Since Alzheimer's disease (AD) is a heterogeneous disease, different subtypes may have distinct biological, genetic, and clinical characteristics, requiring tailored interventions. While several proposed subtypes of AD exist, there is still no clear consensus on a definitive classification. By leveraging complementary AI approaches, including supervised and unsupervised learning, within a recursive pipeline (SUMMER) that integrates multimodal datasets encompassing MRI measurements, phenotypes, and genetic data, our goal was to generate robust scientific evidence for identifying AD subtypes. Data was downloaded from the Alzheimer's Disease Neuroimaging Initiative (ADNI) database and included neuroimaging data (MRI), genetics (SNPs), clinical diagnosis, and demographics. 1133 European American participants' images, aged 55-95, were included in this study. The analysis was multi-fold, where the first step involved applying an unsupervised application to a subset of the MRI sample (AD + cognitively normal (CN) aged matched groups, 100 men aged 68-85 years, and 76 women aged 68-85 years). The MRI brain gray matter was segmented into 44 regions of interest (ROIs) according to a standard atlas, and 618 features were extracted, including ROI voxel intensity measurements such as minimum, maximum, and histogram variables. Results identified a cluster of subtype AD men and a cluster of subtype AD women that were distinct from the rest of their respective samples. In the next step, the integrity of the identified subtype AD clusters was investigated using the XGBoost supervised machine learning application with genetic features (SNPs, N=36,724) and labels: the identified subtype AD cluster vs. the rest of the sample, stratified by sex. A significant AD subtype men model (accuracy=0.85, F1=0.72, AUC=0.83) and a significant women AD subtype model (accuracy=0.81, F1=0.81, AUC=0.81) were built, confirming the homogeneity of the isolated AD subtype clusters. Discriminative biomarkers were extracted from the significant models, including selected ROIs and SNPs. Finally, the subtype models were tested on an unseen subset of ADNI data. The genetic-based models identified clusters of AD subtype participants consisting of 34% of the men AD group and 47% of the women AD group. Phenotypic analysis indicates that lower body weight was associated with the women's AD subtype. Complex diseases like AD demand a sophisticated, multimodal approach for precise diagnosis. Effectively identifying disease subtypes enhances the potential for personalized treatment, ultimately improving patient outcomes.

Journal Article↗

PubMed2026

Reporting and representation of population descriptors in public RNA-seq databases.

Diverse and globally representative datasets are essential to genomic science and medicine. Here, we analyzed population descriptor metadata from RNA sequencing (RNA-seq) studies in two major public repositories: the Sequence Read Archive (SRA) and the Database of Genotypes and Phenotypes. We examined geographic and economic characteristics of institutions depositing the data and compared SRA-deposited descriptors to empirical estimates of genetic ancestry and to those reported in publications, analyzing trends over time. We found that 55% of RNA-seq samples were deposited by United States (US) institutions and 90% by institutions in high-income countries. Only 3% of SRA samples were associated with population descriptors, and among those with US Census terms, 69% were labeled as White. Among samples with continental descriptors, 56% were labeled as European. Our analyses emphasize widespread bias in the composition of public RNA-seq datasets and, more generally, a lack of consistent and careful reporting of population descriptors needing urgent improvement.

Humans↗