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

Gymnosperms are a clade of non-flowering plants that include about 1000 living species. Due to their complex genomes and lack of genomic resources, functional annotation in genomics and transcriptomics on gymnosperms suffers from limitations. Here we present gymnotoa-db, which is a novel, publicly accessible relational database designed to facilitate functional annotation in gymnosperms. This database stores non-redundant records of gymnosperm proteins, encompassing taxonomic and functional information. The complementary software, gymnotoa-app, enables users to download gymnotoa-db and execute a comprehensive functional annotation pipeline for high-throughput sequencing-derived DNA or cDNA sequences. gymnotoa-app's user-friendly interface and efficient algorithms streamline the functional annotation process, making it an invaluable tool for researchers studying gymnosperms. We compared gymnotoa-app's performance against other annotation tools utilizing disparate reference databases. Our results demonstrate gymnotoa-app's superior ability to accurately annotate gymnosperm transcripts, recovering a greater number of transcripts and unique, non-redundant Gene Ontology terms. gymnotoa-db's distinctive features include comprehensive coverage with a non-redundant dataset of gymnosperm protein sequences, robust functional information that integrates data from multiple ontology systems, including GO, KEGG, EC, and MetaCYC, while keeping the taxonomic context, including Arabidopsis homologs. Database URL: https://blogs.upm.es/gymnotoa-db/2024/09/19/gymnotoa-app/.

Mitigating the effects of environmental exploitation on forests requires robust data analysis tools to inform sustainable management strategies and enhance ecosystem resilience. Access to extensive, integrated plant biodiversity data, spanning decades, is essential for this purpose. However, such data are often fragmented across diverse datasets with varying standards, posing two key challenges: first, integrating these datasets into a unified, well-structured data warehouse, and second, handling the vast volume of data using big data technologies to analyze and monitor the temporal evolution of ecosystems. To address these challenges, we developed and used an extract, transform, and load (ETL) protocol that curated and integrates 4482 forestry datasets from around the world, dating back to the 18th century, into a 100-GB data warehouse containing over 172 million records sourced from the Global Biodiversity Information Facility repository. We implemented Python scripts and a NoSQL MongoDB database to streamline and automate the ETL process, using the data warehouse to create the ForestForward web platform. ForestForward is a free, user-friendly application developed using the Django framework, which enables users to consult, download, and visualize the curated data. The platform allows users to explore data layers by year and observe the temporal evolution of ecosystems through visual representations. Database URL: https://forestforward.udl.cat.

T-Cell-derived extracellular vesicles (TcEVs) play key roles in immune regulation and tumor microenvironment modulation. However, the heterogeneity of TcEV remains poorly understood due to technical limitations of EV analysis and the lack of comprehensive data. To address this, we constructed TcEVdb, a comprehensive database that explores the expression and cluster of TcEV by the SEVtras method from T-cell single-cell RNA sequencing data. TcEVdb contains 277 265 EV droplets from 51 T-cell types across 221 samples from 21 projects, covering 9 tissue sources and 23 disease conditions. The database provides two main functional modules. The Browse module enables users to investigate EV secretion activity indices across samples, visualize TcEV clusters, analyze differentially expressed genes (DEGs) and pathway enrichment in TcEV subpopulations, and compare TcEV transcriptomes with their cellular origins. The Search module allows users to query specific genes across all datasets and visualize their expression distribution. Furthermore, our analysis of TcEV in diffuse large B-cell lymphoma revealed increased EV secretion in CD4+ T exhausted cells compared to healthy controls. Subsequent analyses identified distinct droplet clusters with differential expression genes, including clusters enriched for genes associated with cell motility and mitochondrial function. Overall, TcEVdb serves as a comprehensive resource for exploring the transcriptome of TcEV, which will contribute to advancements in EV-based diagnostics and therapeutics across a wide range of diseases. Database URL: https://guolab.wchscu.cn/TcEVdb.

The biotic stress significantly influences the production of potato (Solanum tuberosum L.) all over the world. Long noncoding RNAs (lncRNAs) play key roles in the plant response to environmental stressors. However, their roles in potato resistance to pathogens, insects, and other biotic stress are still unclear. The PotatoBSLnc is a database for the study of potato lncRNAs in response to major biotic stress. Here, we collected 364 RNA sequencing (RNA-seq) data derived from 12 kinds of biotic stresses in 26 cultivars and wild potatoes. PotatoBSLnc currently contains 18 636 lncRNAs and 44 263 mRNAs. In addition, to select the functional lncRNAs and mRNAs under different stresses, the differential expression analyses and the Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) analyses related to the cis/trans-targets of differentially expressed lncRNAs (DElncRNAs) and to the differentially expressed mRNAs (DEmRNAs) were also conducted. The database contains five modules: Home, Browse, Expression, Biotic stress, and Download. Among these, the "Browse" module can be used to search detailed information about RNA-seq data (disease, cultivator, organ types, treatment of samples, and others), the exon numbers, length, location, and sequence of each lncRNA/mRNA. The "Expression" module can be used to search the transcripts per million/raw count value of lncRNAs/mRNAs at different RNA-seq data. The "Biotic stress" module shows the results of differential expression analyses under each of the 12 biotic stresses, the cis/trans-targets of DElncRNAs, the GO and KEGG analysis results of DEmRNAs, and the targets of DElncRNAs. The PotatoBSLnc platform provides researchers with detailed information on potato lncRNAs and mRNAs under biotic stress, which can speed up the breeding of resistant varieties based on the molecular methods. Database URL: https://www.sdklab-biophysics-dzu.net/PotatoBSLnc.

There is an ongoing genetic flow from the mitochondrial genome to the nuclear genome. The mitochondrial sequences that have integrated into the nuclear genome have been shown to be drivers of evolutionary processes and cancerous transformations. In addition to their fundamental biological importance, these sequences have significant consequences for genome assembly and phylogenetic and forensic analyses as well. Previously, our research group developed a computational pipeline that provides a uniform way of identifying these sequences in mammalian genomes. In this paper, we publish MANUDB-the MAmmalian NUclear mitochondrial sequences DataBase, which makes the results of our pipeline publicly accessible. With MANUDB one can retrieve and visualize mitochondrial genome fragments that have been integrated into the nuclear genome of mammalian species. Database URL: manudb.streamlit.app.

The Comparative Toxicogenomics Database (CTD) is a manually curated knowledge- and discovery-base that seeks to advance understanding about the relationship between environmental exposures and human health. CTD's manual curation process extracts from the biomedical literature molecular relationships between chemicals/drugs, genes/proteins, phenotypes, diseases, anatomical terms, and species. These relationships are organized in a highly systematic way in order to make them not only informative but also scientifically computational, enabling inferential hypotheses to be formed to address gaps in understanding. Integral to CTD's functionality is the use of structured, hierarchical ontologies and controlled vocabularies to describe these molecular relationships. Normalizing text (i.e. translating raw text from the literature into these controlled vocabularies) can be a time-consuming process for biocurators. To facilitate the normalization process and improve the efficiency with which our scientists curate the literature, CTD evaluated and integrated into the curation process PubTator 3.0, a state-of-the-art, AI-powered resource which extracts and normalizes from the literature many of the key biomedical concepts CTD curates. Here, we describe CTD's long-standing history with Natural Language Processing (NLP), how this history helped form our objectives for NLP integration, the evaluation of PubTator against our objectives, and the integration of PubTator into CTD's curation workflow. Database URL: https://ctdbase.org.

Enzymes, serving as eco-friendly catalysts, are progressively supplanting traditional chemical catalysts in light industry sectors such as feed, papermaking, textiles, detergents, leather, and sugar production. Despite this advancement, the variability in the performance of natural enzymes and the fragmentation and diversity of existing data formats pose significant challenges to researchers. Furthermore, AI-driven enzyme design is limited by the quality and quantity of available data. To address these issues, we introduce the light industrial core enzyme database (LICEDB), the first database dedicated exclusively to managing and standardizing enzymes for light industry applications. LICEDB, with its integrated modules for data retrieval, similarity analysis, and structural analysis, will enhance the efficient industrial application of enzymes and strengthen AI-driven predictive research, thereby advancing data sharing and utilization in the field of enzyme innovation. Database URL: http://lujialab.org.cn/on-line-databases/.

Curated resources at centralized repositories provide high-value service to users by enhancing data veracity. Curation, however, comes with a cost, as it requires dedicated time and effort from personnel with deep domain knowledge. In this paper, we investigate the performance of a large language model (LLM), specifically generative pre-trained transformer (GPT)-3.5 and GPT-4, in extracting and presenting data against a human curator. In order to accomplish this task, we used a small set of journal articles on wheat and barley genetics, focusing on traits, such as salinity tolerance and disease resistance, which are becoming more important. The 36 papers were then curated by a professional curator for the GrainGenes database (https://wheat.pw.usda.gov). In parallel, we developed a GPT-based retrieval-augmented generation question-answering system and compared how GPT performed in answering questions about traits and quantitative trait loci (QTLs). Our findings show that on average GPT-4 correctly categorized manuscripts 97% of the time, correctly extracted 80% of traits, and 61% of marker-trait associations. Furthermore, we assessed the ability of a GPT-based DataFrame agent to filter and summarize curated wheat genetics data, showing the potential of human and computational curators working side-by-side. In one case study, our findings show that GPT-4 was able to retrieve up to 91% of disease related, human-curated QTLs across the whole genome, and up to 96% across a specific genomic region through prompt engineering. Also, we observed that across most tasks, GPT-4 consistently outperformed GPT-3.5 while generating less hallucinations, suggesting that improvements in LLM models will make generative artificial intelligence a much more accurate companion for curators in extracting information from scientific literature. Despite their limitations, LLMs demonstrated a potential to extract and present information to curators and users of biological databases, as long as users are aware of potential inaccuracies and the possibility of incomplete information extraction.

With the continuous advancements in cancer immunotherapy, neoantigen-based therapies have demonstrated remarkable clinical efficacy. However, accurately predicting the immunogenicity of neoantigens remains a significant challenge. This is mainly due to two core factors: the scarcity of high-quality neoantigen datasets and the limited prediction accuracy of existing immunogenicity prediction tools. This study addressed these issues through several key steps. First, it collected and organized immunogenic neoantigen peptide data from publicly available literature and neoantigen databases. Second, it analyzed the data to identify key features influencing neoantigen immunogenicity prediction. Finally, it integrated existing prediction tools to create TumorAgDB1.0, a comprehensive tumor neoantigen database. TumorAgDB1.0 offers a user-friendly platform. Users can efficiently search for neoantigen data using parameters like amino acid sequence and peptide length. The platform also offers detailed information on the characteristics of neoantigens and tools for predicting tumor neoantigen immunogenicity. Additionally, the database includes a data download function, allowing researchers to easily access high-quality data to support the development and improvement of neoantigen immunogenicity prediction tools. In summary, TumorAgDB1.0 is a powerful tool for neoantigen screening and validation in tumor immunotherapy. It offers strong support to researchers. Database URL: https://tumoragdb.com.cn.