1,720,962 research outputs found
Systematic analysis of SARS-CoV-2 Omicron subvariants’ impact on B and T cell epitopes
Get PDFIntroduction: Epitopes are specific structures in antigens that are recognized by the immune system. They are widely used in the context of immunology-related applications, such as vaccine development, drug design, and diagnosis / treatment / prevention of disease. The SARS-CoV-2 virus has represented the main point of interest within the viral and genomic surveillance community in the last four years. Its ability to mutate and acquire new characteristics while it reorganizes into new variants has been analyzed from many perspectives. Understanding how epitopes are impacted by mutations that accumulate on the protein level cannot be underrated. Methods: With a focus on Omicron-named SARS-CoV-2 lineages, including the last WHO-designated Variants of Interest, we propose a workflow for data retrieval, integration, and analysis pipeline for conducting a database-wide study on the impact of lineages' characterizing mutations on all T cell and B cell linear epitopes collected in the Immune Epitope Database (IEDB) for SARS-CoV-2. Results: Our workflow allows us to showcase novel qualitative and quantitative results on 1) coverage of viral proteins by deposited epitopes; 2) distribution of epitopes that are mutated across Omicron variants; 3) distribution of Omicron characterizing mutations across epitopes. Results are discussed based on the type of epitope, the response frequency of the assays, and the sample size. Our proposed workflow can be reproduced at any point in time, given updated variant characterizations and epitopes from IEDB, thereby guaranteeing to observe a quantitative landscape of mutations' impact on demand. Conclusion: A big data-driven analysis such as the one provided here can inform the next genomic surveillance policies in combatting SARS-CoV-2 and future epidemic viruses
Transformer-Based Biomedical Text Extraction
No full textUnlike formal languages, natural languages are unstructured and more complex. Understanding and generating meaningful text are the goals of natural language processing (NLP). Deep learning recently had a significant impact on this field. Innovative and effective transformer-based models have achieved state-of-the-art results on a wide range of NLP tasks, including those working on specialized clinical and biomedical text. The most widely-adopted models (BERT and GPT) are here described along with their domain-specific versions and applications in the biomedical domain
CoV2K model, a comprehensive representation of SARS-CoV-2 knowledge and data interplay
Get PDFSince the outbreak of the COVID-19 pandemic, many research organizations have studied the genome of the SARS-CoV-2 virus; a body of public resources have been published for monitoring its evolution. While we experience an unprecedented richness of information in this domain, we also ascertained the presence of several information quality issues. We hereby propose CoV2K, an abstract model for explaining SARS-CoV-2-related concepts and interactions, focusing on viral mutations, their co-occurrence within variants, and their effects. CoV2K provides a clear and concise route map for understanding different connected types of information related to the virus; it thus drives a process of data and knowledge integration that aggregates information from several current resources, harmonizing their content and overcoming incompleteness and inconsistency issues. CoV2K is available for exploration as a graph that can be queried through a RESTful API addressing single entities or paths through their relationships. Practical use cases demonstrate its application to current knowledge inquiries
Biological and Medical Ontologies: Human Phenotype Ontology (HPO)
No full textThe Human Phenotype Ontology, known as HPO, provides a standardized vocabulary of phenotypic abnormalities involved in human disease, including both clinical and pathological features, with definitions and synonyms. It is widely used in the field of human genomics and has been integrated into many databases and tools for the analysis of genetic diseases. The primary aim of the ontology is to provide the scientific community with a thorough and logical framework for delineating phenotypic anomalies associated with human diseases. Moreover, it facilitates computational inference and algorithms for integrated genomics and phenotypic analyses. The HPO is constantly updated, and it is a valuable resource for researchers and clinicians working in the field of human genetics and genomics
Biological and Medical Ontologies: Disease Ontology (DO)
No full textHuman Disease Ontology (DO) is a standardized vocabulary for annotating and classifying diseases and their clinical features. It includes over 18,000 disease concepts (classes) and is organized hierarchically, with each concept representing a specific disease or condition. The DO is designed to facilitate the exchange and integration of disease-related information and to support the identification of relationships between diseases. It is widely used in the biomedical community for a variety of purposes, including disease classification, data integration, and knowledge representation
Analysis of co-occurring and mutually exclusive amino acid changes and detection of convergent and divergent evolution events in SARS-CoV-2
Get PDFThe inflation of SARS-CoV-2 lineages with a high number of accumulated mutations (such as the recent case of Omicron) has risen concerns about the evolutionary capacity of this virus. Here, we propose a computational study to examine non-synonymous mutations gathered within genomes of SARS-CoV-2 from the beginning of the pandemic until February 2022. We provide both qualitative and quantitative descriptions of such corpus, focusing on statistically significant co-occurring and mutually exclusive mutations within single genomes. Then, we examine in depth the distributions of mutations over defined lineages and compare those of frequently co-occurring mutation pairs. Based on this comparison, we study mutations’ convergence/divergence on the phylogenetic tree. As a result, we identify 1,818 co-occurring pairs of non-synonymous mutations showing at least one event of convergent evolution and 6,625 co-occurring pairs with at least one event of divergent evolution. Notable examples of both types are shown by means of a tree-based representation of lineages, visually capturing mutations’ behaviors. Our method confirms several well-known cases; moreover, the provided evidence suggests that our workflow can explain aspects of the future mutational evolution of SARS-CoV-2
CoVEffect: interactive system for mining the effects of SARS-CoV-2 mutations and variants based on deep learning
Get PDFBackground: Literature about SARS-CoV-2 widely discusses the effects of variations that have spread in the past 3 years. Such information is dispersed in the texts of several research articles, hindering the possibility of practically integrating it with related datasets (e.g., millions of SARS-CoV-2 sequences available to the community). We aim to fill this gap, by mining literature abstracts to extract-for each variant/mutation-its related effects (in epidemiological, immunological, clinical, or viral kinetics terms) with labeled higher/lower levels in relation to the nonmutated virus. Results: The proposed framework comprises (i) the provisioning of abstracts from a COVID-19-related big data corpus (CORD-19) and (ii) the identification of mutation/variant effects in abstracts using a GPT2-based prediction model. The above techniques enable the prediction of mutations/variants with their effects and levels in 2 distinct scenarios: (i) the batch annotation of the most relevant CORD-19 abstracts and (ii) the on-demand annotation of any user-selected CORD-19 abstract through the CoVEffect web application (http://gmql.eu/coveffect), which assists expert users with semiautomated data labeling. On the interface, users can inspect the predictions and correct them; user inputs can then extend the training dataset used by the prediction model. Our prototype model was trained through a carefully designed process, using a minimal and highly diversified pool of samples. Conclusions: The CoVEffect interface serves for the assisted annotation of abstracts, allowing the download of curated datasets for further use in data integration or analysis pipelines. The overall framework can be adapted to resolve similar unstructured-to-structured text translation tasks, which are typical of biomedical domains
CoV2K: A Knowledge Base of SARS-CoV-2 Variant Impacts
Get PDFIn spite of the current relevance of the topic, there is no universally recognized knowledge base about SARS-CoV-2 variants; viral sequences deposited at recognized repositories are still very few, and the process of tracking new variants is not coordinated. CoV2K is a manually curated knowledge base providing an organized collection of information about SARS-CoV-2 variants, extracted from the scientific literature; it features a taxonomy of variant impacts, organized according to three main categories (protein stability, epidemiology, and immunology) and including levels for these effects (higher, lower, null) resulting from a coherent interpretation of research articles
Going Beyond Counting First Authors in Author Co-citation Analysis
Get PDFThe present study examines one of the fundamental aspects of author co-citation analysis (ACA) - the way co-citation
counts are defined. Co-citation counting provides the data on which all subsequent statistical analyses and mappings
are based, and we compare ACA results based on two different types of co-citation counting - the traditional type that
only counts the first one among a cited work's authors on the one hand and a non-traditional type that takes into
account the first 5 authors of a cited work on the other hand. Results indicate that the picture produced through this non-traditional author co-citation counting contains more coherent author groups and is therefore considerably clearer. However, this picture represents fewer specialties in the research field being studied than that produced through the traditional first-author co-citation counting when the same number of top-ranked authors is selected and analyzed. Reasons for these effects are discussed
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