Identification of Transposable Element Insertions in the Kids First Data

Kids First 数据中转座元件插入的识别

基本信息

批准号：
10172875
负责人：
Peter J Park
金额：
$ 16.9万
依托单位：
HARVARD MEDICAL SCHOOL
依托单位国家：
美国
项目类别：
财政年份：
2020
资助国家：
美国
起止时间：
2020-06-01 至 2023-05-31
项目状态：
已结题

来源：
https://reporter.nih.gov/project-details/10172875
关键词：
Algorithms Communities Computer software Computing Methodologies DNA DNA Insertion Elements DNA Transposable Elements Data Data Set Databases Disease Elements Endogenous Retroviruses Environment Gene Frequency Genes Genetic Diseases Genome Genomics Genotype Human Genome Individual Inherited Insertion Mutation Jumping Genes Length Location Machine Learning Malignant Childhood Neoplasm Malignant Neoplasms Maps Mendelian disorder Methods Modeling Mutation Neurons Output Parents Paste substance Pathogenicity Pediatric Research Play Population Regulatory Element Reporting Resources Retrotransposon Role Sampling Sensitivity and Specificity Single Nucleotide Polymorphism Site Source Speed Structural Congenital Anomalies Training base cloud based cohort design disease phenotype epigenetic profiling gene function genetic element genome sequencing improved machine learning method proband programs transcriptome sequencing whole genome

项目摘要

Project Summary Insertion of transposable elements (TEs, sometimes referred to as “jumping genes”) into the human genome can be pathogenic. Our aim in this project is to use sophisticated computational approaches to characterize TE insertions in the whole-genome sequencing data generated in the Gabriella Miller Kids First Pediatric Research Program and identify any insertional mutations that may disrupt gene function. The large scale of the Kids First program provides an unprecedented opportunity to investigate the role of TE insertions in childhood cancers and structural birth defects, as well as to create a resource of reference TE maps that will be important for all other TE studies. We will first modify our existing algorithm called xTEA for the trio design of the Kids First studies and increase the accuracy and efficiency of the algorithm. Then, we will apply it to the thousands of trios that have been profiled in the Kids First program, using a pipeline optimized for the cloud environment. The resulting set of TE insertions (especially L1, Alu, SVA, and HERV insertions) will be curated with all relevant features and be made into a database for the community. We will also apply machine learning methods to improve the calls once a sufficient amount of training data have been obtained. To investigate the potential pathogenicity of the mutation, we will first focus on insertions within genes, but we will also explore those in regulatory elements inferred from epigenetic profiling data.

项目摘要插入转座元素（TE，有时称为“跳跃基因”）人基因组可能是致病性的。我们在这个项目中的目标是使用复杂的计算在整个基因组测序数据中生成的全基因组测序数据中表征Te插入的方法 Gabriella Miller Kids首次小儿研究计划并确定任何插入突变这可能会破坏基因功能。孩子们的大规模第一个程序提供了史无前例的机会，调查插入在儿童期癌症中的作用结构性先天缺陷以及创建参考地图的资源对于所有其他TE研究都很重要。我们将首先修改现有的算法称为XTEA 儿童的三重奏设计首次研究并提高算法的准确性和效率。然后，我们将将其应用于在儿童第一个节目中介绍的成千上万的三重奏，使用针对云环境优化的管道。由此产生的TE插入（尤其是L1，Alu，SVA和HERV插入）将策划所有相关功能，并成为制作到社区数据库中。我们还将将机器学习方法应用于一旦获得了足够数量的培训数据，请改善呼叫。调查突变的潜在致病性，我们将首先关注基因内的插入，但我们还将探索从表观遗传分析数据推断出的调节元素中的那些。