Human Phenotype Ontology (HPO) terms are increasingly used in diagnostic settings to aid in the characterization of patient phenotypes. The HPO annotation database is updated frequently and can provide detailed phenotype knowledge on various human diseases, and many HPO terms are now mapped to candidate causal genes with binary relationships. To further improve the genetic diagnosis of rare diseases, we incorporated these HPO annotations, gene-disease databases and gene-gene databases in a probabilistic model to build a novel HPO-driven gene prioritization tool, Phen2Gene. Phen2Gene accesses a database built upon this information called the HPO2Gene Knowledgebase (H2GKB), which provides weighted and ranked gene lists for every HPO term. Phen2Gene is then able to access the H2GKB for patient-specific lists of HPO terms or PhenoPacket descriptions supported by GA4GH (http://phenopackets.org/), calculate a prioritized gene list based on a probabilistic model and output gene-disease relationships with great accuracy. Phen2Gene outperforms existing gene prioritization tools in speed and acts as a real-time phenotype-driven gene prioritization tool to aid the clinical diagnosis of rare undiagnosed diseases. In addition to a command line tool released under the MIT license (https://github.com/WGLab/Phen2Gene), we also developed a web server and web service (https://phen2gene.wglab.org/) for running the tool via web interface or RESTful API queries. Finally, we have curated a large amount of benchmarking data for phenotype-to-gene tools involving 197 patients across 76 scientific articles and 85 patients' de-identified HPO term data from the Children's Hospital of Philadelphia.
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http://dx.doi.org/10.1093/nargab/lqaa032 | DOI Listing |
Blood Adv
January 2025
Simon Fraser University, Burnaby, British Columbia, Canada.
Comprehensive genetic analysis of tumors with exome or whole genome sequencing has enabled the identification of the genes that are recurrently mutated in cancer. This has stimulated a series of exciting advances over the past 15 years, guiding us to new molecular biomarkers and therapeutic targets among the common mature B-cell neoplasms. In particular, diffuse large B-cell lymphoma (DLBCL), follicular lymphoma (FL) and Burkitt lymphoma (BL) have each been the subject of considerable attention in this field.
View Article and Find Full Text PDFBr Poult Sci
January 2025
Department of Microbiology, Faculty of Veterinary Medicine, Ankara University, Ankara, Turkey.
1. is an opportunist pathogen of animals, including food-producing ones and humans. Chickens may be a notable source of pathogenic and antimicrobial resistant for transmission to humans.
View Article and Find Full Text PDFJ Arthropod Borne Dis
June 2024
Division of Immunology, Mochtar Riady Institute for Nanotechnology and Medical Science Group, Pelita Harapan University, Tangerang, Indonesia.
Background: The spectrum of dengue infection ranges from asymptomatic or mild to severe disease. The pathogenic mechanisms are not fully understood. A viral infection can induce the neutrophil extracellular traps (NETs), and the excessive NETs lead to increased vascular permeability, coagulopathy, and platelet dysfunction, a hallmark of severe dengue.
View Article and Find Full Text PDFNoncoding RNA
January 2025
Laboratory of Genetics, Comparative and Evolutionary Biology, Department of Biochemistry and Biotechnology, University of Thessaly, Viopolis, Mezourlo, 41500 Larissa, Greece.
: Asthenozoospermia, characterized by reduced sperm motility, is a common cause of male infertility. Emerging evidence suggests that noncoding RNAs, particularly long noncoding RNAs (lncRNAs), play a critical role in the regulation of spermatogenesis and sperm function. Coding regions have a well-characterized role and established predictive value in asthenozoospermia.
View Article and Find Full Text PDFFront Microbiol
January 2025
Department of Agriculture, Food and Resource Sciences, University of Maryland Eastern Shore, Princess Anne, MD, United States.
Metagenomic sequencing is increasingly being employed to understand the assemblage and dynamics of the oyster microbiome. Specimen collection and processing steps can impact the resultant microbiome composition and introduce bias. To investigate this systematically, a total of 54 farmed oysters were collected from Chesapeake Bay between May and September 2019.
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