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

This manuscript presents PheNormGPT, a framework for extraction and normalization of key findings in clinical text. PheNormGPT relies on an innovative approach, leveraging large language models to extract key findings and phenotypic data in unstructured clinical text and map them to Human Phenotype Ontology concepts. It utilizes OpenAI's GPT-3.5 Turbo and GPT-4 models with fine-tuning and few-shot learning strategies, including a novel few-shot learning strategy for custom-tailored few-shot example selection per request. PheNormGPT was evaluated in the BioCreative VIII Track 3: Genetic Phenotype Extraction from Dysmorphology Physical Examination Entries shared task. PheNormGPT achieved an F1 score of 0.82 for standard matching and 0.72 for exact matching, securing first place for this shared task.

The ever-increasing volume of COVID-19-related articles presents a significant challenge for the manual curation and multilabel topic classification of LitCovid. For this purpose, a novel multilabel topic classification framework is developed in this study, which considers both the correlation and imbalance of topic labels, while empowering the pretrained model. With the help of this framework, this study devotes to answering the following question: Do full texts, MeSH (Medical Subject Heading), and biological entities of articles about COVID-19 encode more discriminative information than metadata (title, abstract, keyword, and journal name)? From extensive experiments on our enriched version of the BC7-LitCovid corpus and Hallmarks of Cancer corpus, the following conclusions can be drawn. Our framework demonstrates superior performance and robustness. The metadata of scientific publications about COVID-19 carries valuable information for multilabel topic classification. Compared to biological entities, full texts and MeSH can further enhance the performance of our framework for multilabel topic classification, but the improved performance is very limited. Database URL: https://github.com/pzczxs/Enriched-BC7-LitCovid.

Sesame (Sesamum indicum L., 2n = 26) is a crucial oilseed crop cultivated worldwide. The ancient evolutionary position of the Sesamum genus highlights its value for genomics and molecular genetics research among the angiosperms of other genera. However, Sesamum is considered a small orphan genus with only a few genomic databases for cultivated sesame to date. The urgent need to construct comprehensive, curated genome databases that include genus-specific gene resources for both cultivated and wild Sesamum species is being recognized. In response, we developed Sesamum Genomics Database (SesamumGDB), a user-friendly genomic database that integrates extensive genomic resources from two cultivated sesame varieties (S. indicum) and seven wild Sesamum species, covering all three chromosome groups (2n = 26, 32, and 64). This database showcases a total of 352 471 genes, including 6026 related to lipid metabolism and 17 625 transcription factors within Sesamum. Equipped with an array of bioinformatics tools such as BLAST (basic local alignment search tool) and JBrowse (the Javascript browser), SesamumGDB facilitates data downloading, screening, visualization, and analysis. As the first centralized Sesamum genome database, SesamumGDB offers extensive insights into the genomics and genetics of sesame, potentially enhancing the molecular breeding of sesame and other oilseed crops in the future. Database URL: http://www.sgbdb.com/sgdb/.

Many drug discovery exercises fail because small molecules that are effective inhibitors of target proteins exhibit high cellular toxicity. Early and effective assessment of toxicity and pharmacokinetics is essential to accelerate the drug discovery process. Conventional methods for toxicity profiling, including in vitro and in vivo assays, are laborious and resource-intensive. In response, we introduce the Small Molecule Cell Viability Database (SMCVdb), a comprehensive resource containing toxicity data for over 24 000 compounds obtained through high-content imaging (HCI). SMCVdb seamlessly integrates chemical descriptions and molecular weight data, offering researchers a holistic platform for toxicity data aiding compound prioritization and selection based on biological and economic considerations. Data collection for SMCVdb involved a systematic approach combining HCI toxicity profiling with chemical information and quality control measures ensured data accuracy and consistency. The user-friendly web interface of SMCVdb provides multiple search and filter options, allowing users to query the database based on compound name, molecular weight range, or viability percentage. SMCVdb empowers users to access toxicity profiles, molecular weights, compound names, and chemical descriptions, facilitating the exploration of relationships between compound properties and their effects on cell viability. In summary, the database provides experimentally derived cellular toxicity information for over 24 000 drug candidate molecules to academic researchers, and pharmaceutical companies. The SMCVdb will keep growing and will prove to be a pivotal resource to expedite research in drug discovery and compound evaluation. Database URL: http://smcvdb.rcb.ac.in:4321/.

Rett syndrome (RTT) is a neurodevelopmental disorder occurring almost exclusively in females and leading to a variety of impairments and disabilities from mild to severe. In >95% cases, RTT is due to mutations in the X-linked gene MECP2, but the molecular mechanisms determining RTT are unknown at present, and the complexity of the system is challenging. To facilitate and provide guidance to the unraveling of those mechanisms, we developed a database resource for the visualization and analysis of the genomic landscape in the context of wild-type or mutated Mecp2 gene in the mouse model. Our resource allows for the exploration of differential dynamics of gene expression and the prediction of new potential MECP2 target genes to decipher the RTT disorder molecular mechanisms. Database URL: https://biomedinfo.di.unipi.it/rett-database/.

Acinetobacter baumannii has emerged as a prominent nosocomial pathogen, exhibiting a progressive rise in resistance to therapeutic interventions. This rise in resistance calls for alternative strategies. Here, we propose an alternative yet specialized resource on antimicrobial peptides (AMPs) against A. baumannii. Database 'AbAMPdb' is the manually curated collection of 300 entries containing the 250 experimental AMP sequences and 50 corresponding synthetic or mutated AMP sequences. The mutated sequences were modified with reported amino acid substitutions intended for decreasing the toxicity and increasing the antimicrobial potency. AbAMPdb also provides 3D models of all 300 AMPs, comprising 250 natural and 50 synthetic or mutated AMPs. Moreover, the database offers docked complexes comprising 5000 AMPs and their corresponding A. baumannii target proteins. These complexes, accessible in Protein Data Bank format, enable the 2D visualization of the interacting amino acid residues. We are confident that this comprehensive resource furnishes vital information concerning AMPs, encompassing their docking interactions with virulence factors and antibiotic resistance proteins of A. baumannii. To enhance clinical relevance, the characterized AMPs could undergo further investigation both in vitro and in vivo. Database URL: https://abampdb.mgbio.tech/.

The development of therapeutic agents has mainly focused on designing small molecules to modulate target proteins or genes which are conventionally druggable. Therefore, targeted protein degradation (TPD) for undruggable cases has emerged as promising pharmaceutical approach. TPD, often referred PROTACs (PROteolysis TArgeting Chimeras), uses a linker to degrade target proteins by hijacking the ubiquitination system. Therefore, unravel the relationship including reversal and co-expression between E3 ligands and other possible target genes in various human tissues is essential to mitigate off-target effects of TPD. Here, we developed the atlas of E3 ligases in human tissues (ELiAH), to prioritize E3 ligase-target gene pairs for TPD. Leveraging over 2900 of RNA-seq profiles consisting of 11 human tissues from the GTEx (genotype-tissue expression) consortium, users of ELiAH can identify tissue-specific genes and E3 ligases (FDR P-value of Mann-Whitney test < .05). ELiAH unravels 933 830 relationships consisting of 614 E3 ligases and 20 924 of expressed genes considering degree of tissue specificity, which are indispensable for ubiquitination based TPD development. In addition, docking properties of those relationships are also modeled using RosettaDock. Therefore, ELiAH presents comprehensive repertoire of E3 ligases for ubiquitination-based TPD drug development avoiding off-target effects. Database URL: https://eliahdb.org.

Nonsense variations, characterized by premature termination codons, play a major role in human genetic diseases as well as in cancer susceptibility. Despite their high prevalence, effective therapeutic strategies targeting premature termination codons remain a challenge. To understand and explore the intricate mechanisms involved, we developed StopKB, a comprehensive knowledgebase aggregating data from multiple sources on nonsense variations, associated genes, diseases, and phenotypes. StopKB identifies 637 317 unique nonsense variations, distributed across 18 022 human genes and linked to 3206 diseases and 7765 phenotypes. Notably, ∼32% of these variations are classified as nonsense-mediated mRNA decay-insensitive, potentially representing suitable targets for nonsense suppression therapies. We also provide an interactive web interface to facilitate efficient and intuitive data exploration, enabling researchers and clinicians to navigate the complex landscape of nonsense variations. StopKB represents a valuable resource for advancing research in precision medicine and more specifically, the development of targeted therapeutic interventions for genetic diseases associated with nonsense variations. Database URL: https://lbgi.fr/stopkb/.

The unifying element of all biodiversity data is the issue of taxon hierarchy modeling. We compared 25 existing databases in terms of handling taxa hierarchy and presentation of this data. We used documentation or demo installations of databases as a source of information and next in line was the analysis of structures using R packages provided by inspected platforms. If neither of these was available, we used the public interface of individual databases. For almost half (12) of the databases analyzed, we did not find any formalized taxa hierarchy data structure, providing only biological information about taxon membership in higher ranks, which is not fully formalizable and thus not generally usable. The least effective Adjacency List model (storing parentId of a taxon) dominates among the remaining providers. This study demonstrates the lack of attention paid by current biodiversity databases to modeling taxon hierarchy, particularly to making it available to researchers in the form of a hierarchical data structure within the data provided. For biodiversity relational databases, the Closure Table type is the most suitable of the known data models, which also corresponds to the ontology concept. However, its use is rather sporadic within the biodiversity databases ecosystem.

Despite numerous research efforts by teams participating in the BioCreative VIII Track 01 employing various techniques to achieve the high accuracy of biomedical relation tasks, the overall performance in this area still has substantial room for improvement. Large language models bring a new opportunity to improve the performance of existing techniques in natural language processing tasks. This paper presents our improved method for relation extraction, which involves integrating two renowned large language models: Gemini and GPT-4. Our new approach utilizes GPT-4 to generate augmented data for training, followed by an ensemble learning technique to combine the outputs of diverse models to create a more precise prediction. We then employ a method using Gemini responses as input to fine-tune the BioNLP-PubMed-Bert classification model, which leads to improved performance as measured by precision, recall, and F1 scores on the same test dataset used in the challenge evaluation. Database URL: https://biocreative.bioinformatics.udel.edu/tasks/biocreative-viii/track-1/.