通过异常转录组变异的检测、预测、解释和优先排序来改进临床诊断的方法-猫眼课题宝

通过异常转录组变异的检测、预测、解释和优先排序来改进临床诊断的方法

批准号：

10033447

负责人：

Yoseph Barash

金额：

$34.77万

依托单位：

UNIVERSITY OF PENNSYLVANIA

依托单位国家：

美国

项目类别：

财政年份：

2020

资助国家：

美国

项目状态：

已结题

起止时间：

2020-08-03 至 2024-07-31

来源：

https://reporter.nih.gov/project-details/10033447

关键词：

Address Adopted Age Algorithms Alternative Splicing Benchmarking Cells Child Clinical Code Complex Computers DNA Data Analyses Data Set Defect Detection Development Diagnosis Diagnostic Disease Event Excision Exhibits Exons Gender Genes Genetic Genetic Diseases Genetic Variation Genomics Genotype-Tissue Expression Project Goals Human International Letters Libraries Manuals Medical Mendelian disorder Messenger RNA Methods Modeling Molecular Diagnosis Mutation Normal tissue morphology Outcome Pathogenicity Patients Pediatric Hospitals Performance Phenotype Philadelphia Positioning Attribute Program Research Project Grants Protein Isoforms Proteins RNA RNA Processing RNA Splicing Regulation Reverse Transcriptase Polymerase Chain Reaction Sampling Site Technology Testing Tissues Training United States National Institutes of Health Untranslated RNA Variant Work causal variant clinical Diagnosis clinical application clinical diagnostics cohort computational pipelines congenital anomaly deep learning exome exome sequencing follow-up genetic testing genetic variant heterogenous data improved interest novel prediction algorithm predictive modeling programs standard of care tool transcriptome transcriptome sequencing

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英文摘要

The goal of this research program project is to develop methods to improve clinical diagnosis of children with rare Mendelian disorders. Even with the most advanced standard-of-care genetic test of exome sequencing (ES) diagnostic rate is still below 50%. One reason for this rate is that mutations in non-protein coding regions or those that are synonymous (code for the same protein) are generally discarded even though these could be deleterious due to their effect on the processing of RNA transcribed from the underlying gene. We propose 2 complementary methods to help improve clinical diagnosis: The first is “RNA-first”, where our algorithms suggest which clinically accessible tissue (CAT) to use for RNA sequencing, then compare the results to a larger pool of donors to detect which RNA processing variations may be deleterious. The second is a “DNA-first” approach where we develop “RNA splicing code” models that predict the effect of genetic variations on RNA processing in a given tissue of interest. The two approaches, “RNA-first” and “DNA-first”, will be combined into a clinical diagnostic pipeline at the Children Hospital of Philadelphia (CHOP) and applied to solve undiagnosed cases at CHOP and other centers, including the NIH’s Undiagnosed Disease Network (UDN).

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