Functional annotation of new genes aided by deep learning

深度学习辅助新基因的功能注释

• 批准号: 10386314
• 负责人: Pora Kim
• 金额: $ 10万
• 依托单位: UNIVERSITY OF TEXAS HLTH SCI CTR HOUSTON
• 依托单位国家: 美国
• 财政年份: 2020
• 资助国家: 美国
• 起止时间: 2020-09-01 至 2025-06-30
• 项目状态: 未结题
• 来源: https://reporter.nih.gov/project-details/10386314
• 关键词: ABL1 gene; Achievement; Antineoplastic Agents; Binding; Binding Sites; Biological Markers; CCND2 gene; Cell Death; Chimeric Proteins; Complex; DNA Binding Domain; Development; Disease; Disease Progression; Drug Targeting; EWSR1 gene; Future; Gene Structure; Genes; Human; Human Genome; In Vitro; Individual; Knowledge; Leukemic Cell; Malignant Neoplasms; Methods; NUP214 gene; Phosphotransferases; Procedures; Public Health; RUNX1 gene; Recruitment Activity; Regulator Genes; Research; Role; Source; Stat5 protein; Tissues; Trans-Splicing; Transcript; Update; base; bioinformatics tool; cancer therapy; chimeric gene; cost; deep learning; feature extraction; fusion gene; gene function; human disease; in vivo; insight; knowledge base; leukemia; novel; precision medicine; predictive tools; therapeutic target; tool; transcription factor

New genes (NGs) are generated by multiple mechanisms and their end-piece sequences are identified as the chimeric transcript sequence from multiple human sources including healthy and disease tissues. Therefore, NGs have been recognized as important biomarkers and therapeutic targets for precision medicine. Many efforts have been made to study individual NG function and to identify relevant drug targets. However, the current in-depth research and achievements are mainly concentrated on several driver NGs, and classical cancer drugs have been directly used to target the NG domains, such as the kinase domain of BCR-ABL1 fusion protein in leukemia. Some of the fusion proteins with retaining DNA-binding domains such as transcription factors can directly bind their target genes, such as the EWSR1-FLI fusion actively recruiting BAF complex. Recently, the downstream effectors of driver FGs have emerged as therapeutic targets. For example, targeting the downstream CCND2 inhibited RUNX1/ETO-driven leukemic expansion in vitro and in vivo and inhibition of STAT5, the downstream factor of NUP214-ABL1 led to the induction of leukemia cell death. However, the functions of most identified FGs have not been systematically investigated. This is mainly due to the limitations of traditional tools and the high cost of experimental procedures. Therefore, there is an urgent need to develop new tools for analyzing NG breakpoint-specific features systemically in the human genome and predict their originating and regulatory mechanisms, such as upstream and downstream effectors. In-depth annotation based on NG structure is important for understanding the cellular mechanisms of NGs. Effective use of systematic bioinformatics tools for functional annotation can provide a deeper insight into the role of NGs in the development and progression of diseases such as cancers to find direct and indirect therapeutic targets. In this study, we will develop five bioinformatics tools for the functional annotation and feature analysis of NGs, a predictive pipeline for automatic analysis of downstream effects of NGs, and a predictive method for tracing the origin of NGs.

新基因（NGS）是通过多种机制生成的，其末端序列被确定为 来自包括健康和疾病组织在内的多个人类来源的嵌合转录序列。所以， NG被认为是精确医学的重要生物标志物和治疗靶标。许多 已经努力研究单个NG功能并确定相关的药物靶标。但是， 当前的深入研究和成就主要集中在几个驱动程序NG上 癌症药物已直接用于靶向NG结构域，例如BCR-ABL1的激酶结构域 白血病中的融合蛋白。一些融合蛋白具有保留DNA结合结构域的融合蛋白，例如 转录因子可以直接结合其靶基因，例如EWSR1-FLI融合积极募集BAF 复杂的。最近，驾驶员FG的下游效应子已成为治疗靶标。例如， 靶向下游CCND2抑制RUNX1/ETO驱动的白血病扩张体外和体内和 STAT5的抑制作用，NUP214-ABL1的下游因子导致白血病细胞死亡的诱导。 但是，尚未系统地研究大多数鉴定的FG的功能。这主要是由于 传统工具的局限性和实验程序的高成本。因此，有一个紧急的 需要开发新工具以系统地分析NG断点特定特征在人类基因组中 并预测它们的起源和调节机制，例如上游和下游效应子。深入 基于NG结构的注释对于理解NG的细胞机制很重要。有效的 使用系统的生物信息学工具进行功能注释可以提供更深入的了解 NG在疾病的发展和发展中，例如癌症，以找到直接和间接的治疗 目标。在这项研究中，我们将开发五种用于功能注释和功能分析的生物信息学工具 NGS，一种自动分析NGS效应的预测管道，以及一种预测方法 追踪NGS的起源。