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

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 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.

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.

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/.

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.