Methods for improving clinical diagnostic by detection, prediction, interpretation and prioritization of aberrant transcriptome variations

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: 10674723
• 负责人: Yoseph Barash
• 金额: $ 33.99万
• 依托单位: UNIVERSITY OF PENNSYLVANIA
• 依托单位国家: 美国
• 财政年份: 2020
• 资助国家: 美国
• 起止时间: 2020-08-03 至 2025-07-31
• 项目状态: 未结题
• 来源: https://reporter.nih.gov/project-details/10674723
• 关键词: Address; Adopted; Age; Algorithms; Alternative Splicing; Benchmarking; Cells; Child; ClinVar; Clinical; Code; Complex; Computers; DNA; Data Analyses; Data Set; Defect; Detection; Development; Diagnosis; Diagnostic; Disease; Event; Excision; Exclusion; Exhibits; Exons; Gender; Genes; Genetic; Genetic Diseases; Genetic Transcription; 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; Visualization; Work; candidate identification; causal variant; clinical application; clinical diagnosis; clinical diagnostics; cohort; computational pipelines; congenital anomaly; deep learning model; exome; exome sequencing; follow-up; genetic testing; genetic variant; heterogenous data; improved; interest; mRNA Precursor; novel; prediction algorithm; predictive modeling; programs; rare mendelian disorder; standard of care; tool; transcriptome; transcriptome sequencing

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).

该研究计划项目的目标是开发改进儿童临床诊断的方法 罕见的孟德尔疾病。即使采用最先进的外显子组测序 (ES) 标准护理基因测试 诊断率仍低于50%。造成这一比率的原因之一是非蛋白质编码区或 那些同义的（相同蛋白质的代码）通常被丢弃，即使它们可能是 由于它们对从基础基因转录的 RNA 的加工有影响，因此是有害的。我们建议2 帮助改善临床诊断的补充方法：第一个是“RNA优先”，我们的算法建议 用于 RNA 测序的临床可访问组织 (CAT)，然后将结果与更大的池进行比较 供体检测哪些 RNA 加工变异可能是有害的。第二种是“DNA优先”的方法 我们开发了“RNA 剪接代码”模型，可以预测遗传变异对 RNA 加工的影响 给定的感兴趣组织。 “RNA优先”和“DNA优先”这两种方法将被结合到临床中 费城儿童医院 (CHOP) 的诊断流程，并应用于解决以下机构的未确诊病例 CHOP 和其他中心，包括 NIH 的未确诊疾病网络 (UDN)。

项目成果

Direct long-read RNA sequencing identifies a subset of questionable exitrons likely arising from reverse transcription artifacts.

• DOI: 10.1186/s13059-021-02411-1
• 发表时间: 2021-06-28
• 期刊: Genome biology
• 影响因子: 12.3
• 作者: Schulz L;Torres-Diz M;Cortés-López M;Hayer KE;Asnani M;Tasian SK;Barash Y;Sotillo E;Zarnack K;König J;Thomas-Tikhonenko A
• 通讯作者: Thomas-Tikhonenko A

MAJIQlopedia: an encyclopedia of RNA splicing variations in human tissues and cancer.

• DOI: 10.1093/nar/gkad1043
• 发表时间: 2024-01-05
• 期刊: NUCLEIC ACIDS RESEARCH
• 影响因子: 14.9
• 作者: Quesnel-Vallieres, Mathieu;Jewell, San;Lynch, Kristen W.;Thomas-Tikhonenko, Andrei;Barash, Yoseph
• 通讯作者: Barash, Yoseph

Identifying common transcriptome signatures of cancer by interpreting deep learning models.

• DOI: 10.1186/s13059-022-02681-3
• 发表时间: 2022-05-17
• 期刊: Genome biology
• 影响因子: 12.3

Contrasting and Combining Transcriptome Complexity Captured by Short and Long RNA Sequencing Reads.

对比和组合短和长 RNA 测序读取捕获的转录组复杂性。

• DOI: 10.1101/2023.11.21.568046
• 发表时间: 2023
• 期刊: bioRxiv : the preprint server for biology
• 作者: Han,SeongWoo;Jewell,San;Thomas-Tikhonenko,Andrei;Barash,Yoseph
• 通讯作者: Barash,Yoseph