The immunopeptidome corresponds to the repertoire of peptides presented at the cell surface by the major histocompatibility complex (MHC) molecules. Cytotoxic T cells scan this repertoire to identify nonself antigens that can arise from tumors or infected cells. The identification of actionable antigenic targets is key to the development of therapeutic cancer vaccines, T-cell therapy, and other T-cell receptor-based biologics. The growing clinical interest for immunopeptidomics has accelerated the development of high throughput proteogenomic platforms that provide a system-level analysis of MHC-associated peptides. Improvement in sensitivity and throughput of mass spectrometers now allows the detection of a few thousands of peptides from less than 100 million cells. To manage the amount of data generated by these instruments, we have developed the MHC-associated peptide discovery platform (MAPDP), a novel open-source cloud-based computational platform for immunopeptidomic analyses. It provides convenient access from a web portal to immunopeptidomes stored in the database, filtering tools, various visualizations, annotations (e.g., IEDB, dbSNP, gnomAD), peptide-binding affinity prediction (mhcflurry, NetMHC), HLA genotyping, and the generation of personalized proteome databases. MAPDP functionalities are demonstrated here by the discovery of MHC peptides featuring new genetic variants identified in two previously published ovarian carcinoma data sets.
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http://dx.doi.org/10.1021/acs.jproteome.9b00859 | DOI Listing |
JMIR Med Inform
December 2024
Center for Geriatrics & Gerontology, Taichung Veterans General Hospital, 1650 Taiwan Boulevard Sect 4, Taichung, 407219, Taiwan, 886 4-2359-2525, 886 4-2359-5046.
Background: Telehealth programs and wearable sensors that enable patients to monitor their vital signs have expanded due to the COVID-19 pandemic. The electronic National Early Warning Score (e-NEWS) system helps identify and respond to acute illness.
Objective: This study aimed to implement and evaluate a comprehensive telehealth system to monitor vital signs using e-NEWS for patients receiving integrated home-based medical care (iHBMC).
Front Chem
November 2024
African Society for Bioinformatics and Computational Biology, Cape Town, South Africa.
Introduction: Homology modeling is a widely used computational technique for predicting the three-dimensional (3D) structures of proteins based on known templates,evolutionary relationships to provide structural insights critical for understanding protein function, interactions, and potential therapeutic targets. However, existing tools often require significant expertise and computational resources, presenting a barrier for many researchers.
Methods: Prostruc is a Python-based homology modeling tool designed to simplify protein structure prediction through an intuitive, automated pipeline.
Pac Symp Biocomput
December 2024
Department of Biomedical Informatics and Medical Education, University of Washington School of Medicine, Seattle, Washington, 98195, USA.
Artificial Intelligence (AI) algorithms showcase the potential to steer a paradigm shift in clinical medicine, especially medical imaging. Concerns associated with model generalizability and biases necessitate rigorous external validation of AI algorithms prior to their adoption into clinical workflows. To address the barriers associated with patient privacy, intellectual property, and diverse model requirements, we introduce ClinValAI, a framework for establishing robust cloud-based infrastructures to clinically validate AI algorithms in medical imaging.
View Article and Find Full Text PDFClin Transl Sci
December 2024
Critical Path Institute, Tucson, Arizona, USA.
In the rapidly evolving landscape of healthcare and drug development, the ability to efficiently collect, process, and analyze large volumes of real-world data (RWD) is critical for advancing drug development. This article provides a blueprint for establishing an end-to-end data and analytics pipeline in a cloud-based environment. The pipeline presented here includes four major components, including data ingestion, transformation, visualization, and analytics, each supported by a suite of Amazon Web Services (AWS) tools.
View Article and Find Full Text PDFF1000Res
December 2024
Institute for Implementation Science in Population Health, City University of New York School of Public Health, New York, New York, USA.
Advancements in sequencing technologies and the development of new data collection methods produce large volumes of biological data. The Genomic Data Science Analysis, Visualization, and Informatics Lab-space (AnVIL) provides a cloud-based platform for democratizing access to large-scale genomics data and analysis tools. However, utilizing the full capabilities of AnVIL can be challenging for researchers without extensive bioinformatics expertise, especially for executing complex workflows.
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