Database: The Journal of Biological Databases and Curation最新文献

Pregnancy loss is an important reproductive health problem that affects many couples. Genetic factors play an important role in both spontaneous miscarriage and recurrent pregnancy loss, and the effect of genomic variants is recognized as one of the major causes of pregnancy loss in euploid foetuses. In this work, we extend our previous analysis of the genetic landscape of pregnancy loss and develop a Pregnancy Loss genetic Variant (PLoV) database to aggregate information about mutations that have been implicated in pregnancy loss. The database contains information about 534 genetic variants that have been observed in 421 cases across 47 studies, including foetus-only, parent-only, and trio-based studies. For each case, the database includes a detailed description of the phenotype, including ultrasound data (if provided in the original article). The genetic variants are scattered across all chromosomes in the human genome and affect a total of 292 unique genes. We provide a public access to the PLoV database at https://plovdb.ott.ru/. Database URL: https://plovdb.ott.ru/.

The tumour suppressor gene TP53 encodes the DNA binding transcription factor p53 and is one of the most mutated genes in human cancer. Tumour suppressor activity requires binding of p53 to its DNA response elements and subsequent transcriptional activation of a diverse set of target genes. Despite decades of close study, the logic underlying p53 interactions with its numerous potential genomic binding sites and target genes is not yet fully understood. Here, we present a database of DNA and chromatin-based information focused on putative p53 binding sites in the human genome to allow users to generate and test new hypotheses related to p53 activity in the genome. Users can query genomic locations based on experimentally observed p53 binding, regulatory element activity, genetic variation, evolutionary conservation, chromatin modification state, and chromatin structure. We present multiple use cases demonstrating the utility of this database for generating novel biological hypotheses, such as chromatin-based determinants of p53 binding and potential cell type-specific p53 activity. All database information is also available as a precompiled SQLite database for use in local analysis or as a Shiny web application. Database URL: https://p53motifDB.its.albany.edu.

Accurate annotation of viral genomes is essential for reliable downstream analysis and public data sharing. While National Center for Biotechnology Information's (NCBI's) Viral Annotation DefineR (VADR) pipeline provides standardized annotation and quality control, it only supports six viral groups to date. Here, we developed and validated 12 new reference sequence-based VADR models targeting key human respiratory viruses: measles virus, mumps virus, rubella virus, human metapneumovirus, human parainfluenza virus types 1-4, and seasonal coronaviruses (229E, NL63, OC43, and HKU1). Model construction was guided by a comprehensive analysis of intra-species genomic and phylogenetic diversity, enabling the development of genotype-specific models associated with reference genomes that defined expected genome structure and annotation. Models were trained on 5327 publicly available complete viral genomes and tested on 372 viral genomes not yet submitted to GenBank. VADR passed 96.3% of publicly available viral genomes and 98.1% of viral genomes not in the training set, correctly identifying overlapping ORFs, mature peptides, and transcriptional slippage as well as genome misassemblies. VADR detected novel viral biology including the first reported HCoV-OC43 NS2 knockout in a human infection and novel G and SH coding sequence lengths in human metapneumovirus. These VADR models are publicly available and are used by NCBI curators as part of the GenBank submission pipeline, supporting high-quality, scalable viral genome annotation for research and public health.

Over the past few decades, coronavirus outbreaks have been reported globally. To date, seven human coronaviruses have been identified, among which only SARS-CoV-2 has been extensively studied, resulting in the development of several approved antiviral drugs. To effectively combat both current and emerging coronaviruses, there is an urgent need for a comprehensive database that consolidates information on all known human coronaviruses and their potential antiviral compounds. In response, we present HCoVDB-a comprehensive database that integrates genomic data, viral proteins, and antiviral agents with demonstrated in vitro or in vivo activity against the seven human coronaviruses. Compared to existing coronavirus databases, HCoVDB offers three distinctive features: (i) a curated collection and annotation of over 4 million genomic sequences from all seven human coronaviruses, including key amino acid substitutions that influence viral fitness, drug resistance, and immune evasion; (ii) a protein-drug docking platform for predicting the binding interactions of antiviral agents with demonstrated activity; and (iii) an extensive compilation of antiviral agents, along with their chemical properties and antiviral efficacy profiles (IC50, EC50, or CC50) as reported in the literature. Overall, HCoVDB provides a valuable resource for tracking the evolutionary dynamics of coronaviruses and accelerating the development of broad-spectrum antiviral agents against coronavirus infections in the future. Database URL: http://hcovdb.denglab.org/.

Protein sequence alignments are evolutionary models and offer as starting points for the recognition of additional members of a homologous family and design of experiments. However, the accuracy of sequence alignments is obscured at the superfamily level due to distant relationships. Where structures of proteins are available, distantly related proteins can be aligned, guided by structural features. The Protein Alignment Organized as Structural Superfamilies (PASS2) database offers such structure-based sequence alignments for protein domains classified within superfamilies, as per the Structural Classification of Proteins extended (SCOPe) framework. The present update of PASS2 (PASS2.8) corresponds to the latest SCOPe release (version 2.08). This release comprises data for 26 690 protein domains exhibiting less than 40% sequence identity, organized into 2058 superfamilies. Several features derived from these alignments, including conserved secondary structural motifs, hidden Markov models (HMMs), conserved residues, and interactions across superfamilies, are also provided. For superfamilies containing divergent members, a k-means clustering algorithm has been employed to identify outliers and partition domains into split superfamilies. Novel features in this update include topological diagrams of the domains, potential interactors for each domain, and an updated methodology for identifying conserved interactions across superfamilies. This version of the database can be reached from http://caps.ncbs.res.in/pass2.

Metabarcoding has emerged as a robust method for assessing biodiversity patterns by retrieving environmental DNA directly from ecosystems. While the 18S rRNA gene is the primary genetic marker used for broad eukaryotic metabarcoding, it has limitations in resolving lower taxonomic levels. A potential alternative is the mitochondrial cytochrome oxidase subunit I (COI) gene because it offers resolution at the species level. However, the COI gene lacks a comprehensive, curated taxonomically informed database including protists. To address this gap, we introduce eKOI, a novel, curated COI gene database designed to enhance the taxonomic annotation for protists that can be used for COI-based metabarcoding. eKOI integrates data from GenBank and mitochondrial genomes, followed by extensive manual curation to eliminate redundancies and contaminants, recovering 15 947 sequences within 80 eukaryotic phyla. We validated the use of eKOI by reannotating several COI metabarcoding datasets, revealing previously unidentified protist biodiversity and demonstrating the database utility for community-level analyses.

Population growth and the impacts of climate change are placing increasing pressure on global agriculture and breeding programmes. Recent advancements in phenotyping techniques, genotyping technologies, and predictive modelling are accelerating genetic gains in breeding programmes, helping researchers and breeders develop improved crops more efficiently. However, these advancements have also led to an overwhelming torrent of fragmented data, creating significant challenges in data integration and management. To address this issue, the Breeding Application Programming Interface (BrAPI) project was established as a standardized data model for breeding data. BrAPI is an international, community-driven effort that facilitates interoperability among databases and tools, improving the sharing and interpretation of breeding-related data. This open-source standard is software-agnostic and can be used by anyone interested in breeding, phenotyping, germplasm, genotyping, and agronomy data management. This manuscript provides an overview of the BrAPI project, highlighting the significant progress made in the development of the data standard and the expansion of its community. It also presents a showcase of the wide variety of BrAPI-compatible tools that have been built to enhance breeding and research activities, demonstrating how the project is advancing agricultural innovation and data management practices.

GExplore is an online tool to assist with large-scale data mining of selected datasets related to gene and protein function in Caenorhabditis elegans. Here, we describe the current version GExplore 1.5, which contains new datasets and display options as well as a completely redesigned web interface. GExplore now consists of six databases. The gene database contains protein domain information, general expression, and phenotype data as well as interacting genes, gene ontology annotations, and disease associations. The mutation database contains a curated list of more than 200 000 mutations affecting the protein sequences of all protein-coding genes. The protein database contains proteome data from 19 different nematode species, four genetic model organisms and the human proteome for comparison. Three genome-scale RNAseq expression databases contain expression profiles of different developmental stages from embryo to adult, tissues-specific expression profiles at the L2 stage, and expression profiles of the major tissues in the developing embryo at five different time points from gastrulation to the beginning of terminal differentiation. The web-based user interface has been completely redeveloped for the current version. The search interfaces allow users to explore content of the individual databases in detail. The interactive display pages enable the user to fine-tune the results, display additional data, and download the results. GExplore is a tool to quickly obtain an overview of biological and biochemical functions of large groups of genes or identify genes with a certain combination of features for further experimental analysis. Database URL: https://genome.science.sfu.ca/gexplore.

Substitution models of protein evolution describe the patterns of amino acid substitutions over evolutionary time and are fundamental for probabilistic methods of phylogenetic inference. At the protein level, a variety of substitution models are available, but only empirical substitution models are well established in phylogenetics due to their mathematical simplicity. Despite their importance, a database compiling the large number of currently available empirical substitution models of protein evolution is lacking, although such a resource could facilitate access, assessment, and subsequent implementation of these models into phylogenetic frameworks. Besides, little is known about formal comparisons between the current set of empirical substitution models. We present EModelDB, a database of empirical substitution models of protein evolution required for probabilistic protein phylogenetics that includes the corresponding exchangeability matrices, model classification, and model-specific biological information. The database is integrated into a graphical user interface, written in Python and SQL, that facilitates its usability. We also compared common empirical substitution models in terms of the distance between their relative rates of amino acid substitution and amino frequencies at equilibrium. We found that substitution models derived from proteins related in nature tend to cluster together, reflecting similar evolutionary patterns. Indeed, we evaluated the empirical substitution models in terms of the folding stability of the derived modeled proteins and found that they generally produce less stable proteins compared to real proteins, suggesting that substitution models with additional evolutionary constraints can be preferred for studying protein evolution accounting for folding stability. Database URL: https://github.com/Paula-Iglesias-Rivas/EModelDB.

Recent advancements highlight the importance of large-scale causal inference in elucidating disease mechanisms and guiding public health strategies. Mendelian randomization (MR) has become a cornerstone method for identifying causal relationships by leveraging genetic variants as instrumental variables. However, existing tools lack flexibility for multivariable analyses and fail to integrate diverse datasets effectively. To address these challenges, we introduce MRdb, a comprehensive database designed for conducting both univariable and multivariable MR analyses. MRdb encompasses 12 distinct categories of exposure data, including but not limited to 19 126 expression quantitative trait loci genes, 4907 plasma proteins, and 1400 plasma metabolites. Additionally, it integrates 48 507 disease outcomes sourced from FinnGen R10 and the IEU Open GWAS Project. MRdb offers robust data preprocessing features, including handling missing statistics, harmonizing datasets, and selecting instrumental variables to ensure high-quality analyses. Collectively, MRdb bridges the gaps in existing tools by integrating diverse datasets with user-friendly functionalities, empowering researchers to explore complex causal mechanisms.