Enhancing molecular diagnosis in children with multiple congenital anomalies using clinically focused splicing prediction algorithms

使用临床重点剪接预测算法增强对多种先天性异常儿童的分子诊断

• 批准号: 9907858
• 负责人: Joseph Krittameth Aicher
• 金额: $ 5.05万
• 依托单位: UNIVERSITY OF PENNSYLVANIA
• 依托单位国家: 美国
• 财政年份: 2019
• 资助国家: 美国
• 起止时间: 2019-04-01 至 2022-03-31
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/9907858
• 关键词: Address; Algorithms; Alternative Splicing; Benchmarking; Cells; Child; Childhood; ClinVar; Clinical; Clinical Research; Code; Communication; Computational Biology; Computational Molecular Biology; Computers; Critical Thinking; Data; Data Analyses; Data Set; Defect; Development; Diagnosis; Diagnostic; Disease; Doctor of Philosophy; Ensure; Evaluation; Excision; Exhibits; Exons; Experimental Designs; Foundations; Genes; Genetic; Genetic Variation; Genome; Genomics; Genotype-Tissue Expression Project; Goals; Human; Human Genetics; Instruction; International; Introns; K-Series Research Career Programs; Knowledge; Libraries; Manuals; Master' s Degree; Mathematics; Medical; Mendelian disorder; Mentors; Mentorship; Messenger RNA; Modeling; Molecular Diagnosis; Molecular Genetics; Normal tissue morphology; Pathogenesis; Pathogenicity; Patients; Performance; Phase; Phenotype; Physicians; Postdoctoral Fellow; Protein Isoforms; RNA Splicing; Regulation; Resources; Reverse Transcriptase Polymerase Chain Reaction; Scientist; Site; Syndrome; Technology; Testing; Tissues; Training; Untranslated RNA; Validation; Variant; Work; algorithm development; analysis pipeline; career; causal variant; clinical application; cohort; computational pipelines; congenital anomaly; deep learning; exome; exome sequencing; experience; genetic testing; genetic variant; improved; innovation; insight; interest; novel; prediction algorithm; reference genome; skills; standard of care; transcriptome sequencing

The training included in this career development award promotes the applicant's development as a physician-scientist during his the PhD phase of his MD/PhD training in transdisciplinary computational genomics. The applicant has previously completed a Master's Degree in Mathematics, and has had extensive instruction in general computational biology. This unique melding of high-level computational expertise and interest in applied genomics has led to the development of this innovative project. He is now being co-mentored by Drs. Barash and Bhoj to gain complementary practical training in predictive algorithm development and molecular genetics. In both his clinical and research interests he is dedicated to improving the rate of molecular diagnosis for children with rare Mendelian disorders. His short-term goals include developing and refining his skills in RNA splicing prediction and human genetic variation analysis in exome and genome data. In addition, he will gain new insight into experimental design, data interpretation, and scientific communication skills to ensure his successful post-doctoral transition. His co-mentors for the proposal are Drs. Yoseph Barash and Elizabeth Bhoj, international leaders in computational genomics and molecular genetics. In addition he will be supported by outstanding resources of the MSTP at Penn, which has an extensive proven track record of successful previous awardees. The applicant has been pursuing work in creating an improved computational pipeline for the analysis of variants from exome and genome data. Specifically he is capturing the intronic and synonymous variants that are generally removed from the analysis pipeline because of the difficulty in determining the pathogenicity of such variants. As there are many intronic and synonymous variants that are known to cause Mendelain disorders, this clearly leads to missed diagnoses. In Aim 1 he will generate an interpretable algorithm for prioritizing general splicing variants that guides functional validation. In Aim 2 he will identify novel variants and genes for mechanistic evaluation in the pathogenesis of congenital anomalies. This algorithm will be generally applicable, significantly enhancing our ability to provide molecular diagnoses for all patients with suspected Mendelian disorders. In addition, this proposal will allow the candidate to gain experience, knowledge, and new skills to successfully lay the foundation as a physician-scientist in computational genomics.

该职业发展奖中包含的培训促进了申请人作为一名 在跨学科计算基因组学MD/PhD培训的博士阶段，医生兼科学家。 申请人以前完成了数学硕士学位，并有广泛的指导， 在一般计算生物学中。这种独特的融合高层次的计算专业知识和兴趣， 应用基因组学导致了这一创新项目的发展。他现在由博士共同指导。 Barash和Bhoj将获得预测算法开发和分子生物学方面的补充实践培训 遗传学在他的临床和研究兴趣，他致力于提高分子诊断率 治疗罕见的孟德尔遗传病他的短期目标包括发展和完善他的RNA技能 外显子组和基因组数据中的剪接预测和人类遗传变异分析。此外，他还将获得 对实验设计，数据解释和科学沟通技巧的新见解，以确保他的 博士后顺利过渡。他的共同导师的建议是博士约瑟夫巴拉什和伊丽莎白Bhoj， 计算基因组学和分子遗传学的国际领导者。此外，他还将得到以下方面的支持： 宾夕法尼亚大学MSTP的杰出资源，该公司在成功的 获奖者 申请人一直致力于创建用于分析的改进的计算流水线的工作。 来自外显子组和基因组数据的变体。具体来说，他正在捕捉内含子和同义变体， 由于难以确定病原体的致病性， 这样的变种。由于已知有许多内含子和同义变体会导致孟德尔遗传病， 这显然会导致漏诊。在目标1中，他将生成一个可解释的算法，用于对一般 指导功能验证的剪接变体。在目标2中，他将确定新的变异和基因， 先天性畸形发病机制的机制评价。该算法将是普遍适用的， 显著提高了我们为所有疑似孟德尔遗传病患者提供分子诊断的能力， 紊乱此外，该提案将使候选人获得经验，知识和新技能， 成功地奠定了基础，作为一个物理学家，科学家在计算基因组学。