Identification of Transposable Element Insertions in the Kids First Data

Kids First 数据中转座元件插入的识别

• 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
• 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

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 将转座因子（TE，有时称为“跳跃基因”）插入到细胞中， 人类基因组可能是致病性。我们在这个项目中的目标是使用复杂的计算 在全基因组测序数据中表征TE插入的方法， 加布里埃拉米勒儿童第一儿科研究计划，并确定任何插入突变 这可能会破坏基因功能。儿童第一计划的大规模提供了一个 这是一个前所未有的机会，可以研究TE插入在儿童癌症中的作用， 结构性出生缺陷，以及创建参考TE地图资源 这对其他所有研究都很重要。我们将首先修改我们现有的算法，称为xTEA， 三人组设计的Kids First研究，提高了算法的准确性和效率。 然后，我们将把它应用到成千上万的三人组中，这些三人组已经在儿童第一计划中被描述过， 使用针对云环境优化的管道。TE插入的结果集 （特别是L1、Alu、SVA和HERV插入）将与所有相关功能一起策划， 做成社区的数据库。我们还将机器学习方法应用于 一旦获得了足够量的训练数据，就改进呼叫。探讨 突变的潜在致病性，我们将首先关注基因内的插入，但我们 还将探讨那些从表观遗传学分析数据推断的调控元件。

项目成果

The landscape of human SVA retrotransposons.

• DOI: 10.1093/nar/gkad821
• 发表时间: 2023-11-27
• 期刊: Nucleic acids research
• 影响因子: 14.9