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

基本信息

批准号：
9760051
负责人：
Joseph Krittameth Aicher
金额：
$ 5万
依托单位：
UNIVERSITY OF PENNSYLVANIA
依托单位国家：
美国
项目类别：
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/9760051
关键词：
Address Algorithms Alternative Splicing Benchmarking Cells Child Childhood 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 Genetic screening method Genome Genomics Genotype-Tissue Expression Project Goals Human Human Genetics Instruction International Introns K-Series Research Career Programs Knowledge Libraries Manuals Master&apos 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 analysis pipeline career causal variant clinical application cohort congenital anomaly deep learning exome exome sequencing experience genetic variant improved innovation insight interest novel prediction algorithm reference genome skills standard of care transcriptome sequencing

项目摘要

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.

该职业发展奖中包括的培训促进了申请人的发展医师科学家在他的跨学科计算基因组学中MD/博士学位培训的博士学位阶段。申请人以前已经完成了数学硕士学位，并且有广泛的指导在一般计算生物学中。这种高级计算专业知识和兴趣的独特融合应用基因组学导致了这一创新项目的发展。现在，他被Drs授予。 Barash和Bhoj获得预测算法开发和分子的互补实用培训遗传学。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罕见的孟德尔疾病的儿童。他的短期目标包括发展和完善他在RNA中的技能外显子和基因组数据中的剪接预测和人类遗传变异分析。此外，他会收获对实验设计，数据解释和科学沟通技巧的新见解，以确保他的成功的博士后过渡。他提出的提案的联合官员是Drs。 Yoseph Barash和Elizabeth Bhoj，计算基因组学和分子遗传学的国际领导者。此外，他将得到 MSTP在宾夕法尼亚获奖者。申请人一直在追求创建改进的计算管道来分析的工作外显子和基因组数据的变体。具体而言，他正在捕获中内含子和同义的变体通常从分析管道中删除，因为难以确定这样的变体。由于有许多内含子和同义变体已知会引起门德莱素疾病，所以这显然导致遗体诊断。在AIM 1中，他将生成一种可解释的算法以优先考虑一般性引导功能验证的剪接变体。在AIM 2中，他将确定新颖的变体和基因先天性异常发病机理中的机械评估。该算法通常适用，显着增强了我们为所有可疑Mendelian患者提供分子诊断的能力疾病。此外，该建议将使候选人能够获得经验，知识和新技能成功地奠定了计算基因组学医师科学家的基础。