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.

此职业发展奖中包含的培训将促进申请者作为 在他的跨学科计算基因组学医学博士/博士培训期间，他是一名内科科学家。 申请人之前已完成数学硕士学位，并接受过广泛的指导 在一般的计算生物学中。这种高级计算专业知识和兴趣的独特融合 应用基因组学促成了这一创新项目的发展。他现在正由杜兰特博士与他共同指导。 Barash和Bhoj将在预测算法开发和分子方面获得互补的实践培训 遗传学。在他的临床和研究兴趣中，他致力于提高分子诊断率 适用于患有罕见孟德尔病症的儿童。他的短期目标包括发展和完善他在RNA方面的技能 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、数据解释和科学交流技能的新见解，以确保其 成功完成博士后过渡。他的提议的共同导师是约瑟·巴拉什博士和伊丽莎白·博吉博士， 计算基因组学和分子遗传学的国际领先者。此外，他还将得到 宾夕法尼亚大学MSTP的杰出资源，该计划拥有广泛的已证明的成功记录 获奖者。 申请人一直致力于创建一种改进的计算管道，用于分析 外显子组和基因组数据的变异。具体地说，他正在捕捉内含子和同义变体， 通常从分析管道中移除，因为很难确定病毒的致病性 这样的变种。由于已知有许多内含子和同义变体会导致门德兰疾病， 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来确定一般优先级 指导功能验证的拼接变体。在目标2中，他将确定新的变种和基因 先天性畸形发病机制的评价。该算法将普遍适用， 显著增强了我们为所有疑似孟德尔病毒患者提供分子诊断的能力 精神错乱。此外，这项建议将使应聘者获得经验、知识和新技能，以 作为计算基因组学领域的内科科学家，他成功地奠定了基础。