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The systematic functional analysis of regulatory novel open reading frames.

监管小说开放阅读框架的系统功能分析。

基本信息

批准号：
2116112
负责人：
金额：
--
依托单位：
University of Cambridge
依托单位国家：
英国
项目类别：
Studentship
财政年份：
2018
资助国家：
英国
起止时间：
2018 至无数据
项目状态：
已结题

来源：
https://gtr.ukri.org/projects?ref=studentship-2116112
关键词：
systematic functional analysis regulatory novel

项目摘要

Nearly 93% of all disease and trait associated single nucleotide polymorphisms (SNPs) are mapped to non-coding regions. To gain a better understanding of the pathogenicity of non-coding regions, we here introduce the gene class of novel open reading frames (nORFs) and investigate the functional identity of non-canonical transcriptional and translational products. At the time being, the nORF database openProt provides mass spectroscopy based translational evidence of 143,102 proteins. Despite decisive evidence of nORF translation and regulation, the comprehensive understanding of nORF function and disease-association remains to be challenging. However, recent advances in bioinformatics and data acquisition promise new opportunities to develop data-driven pipelines and might thus unveil the identity and regulatory function of yet overlooked (non-canonical) proteins. In this report, we share preliminary results and propose a plan to further establish the relevance of nORFs with the means of bioinformatic analysis and experimental validation to gain a better understanding of a wide array of diseases. This report begins with a general introduction of the fields relevance and progress followed up by 4 chapters that each contribute to the rigorous top-down analysis and prioritization of functional nORF candidates. In chapter one we introduce the creation of the nORF database and platform (nORFs.org) that from then on constitutes as reference database for all later research. Chapter two utilizes the created database to engineer feature descriptor dimensions for later analysis pipelines. Chapter three introduces a high dimensional analysis pipeline to quality control and analyze a combined dataset of nORFs, RefSeq genes and random sequences. In the analysis, we utilize unsupervised clustering methods (t-SNE and UMAP) as groundwork for sophisticated machine learning methods. In chapter four 37 sequence samples were taken from t-SNE clusters to identify potential functional relationships. Subsequent to those analysis steps the report concludes with preliminary regulatory nORF insights and future research considerations. (VIVA excerpt)

近93%的疾病和性状相关的单核苷酸多态性（SNP）被定位到非编码区。为了更好地了解非编码区的致病性，我们在这里介绍了新的开放阅读框架（nORFs）的基因类，并调查非典型的转录和翻译产物的功能一致性。目前，nORF数据库openProt提供了143，102种蛋白质的基于质谱的翻译证据。尽管nORF翻译和调控的决定性证据，nORF功能和疾病相关性的全面理解仍然是具有挑战性的。然而，生物信息学和数据采集的最新进展为开发数据驱动的管道提供了新的机会，从而可能揭示尚未被忽视的（非经典）蛋白质的身份和调控功能。在这份报告中，我们分享了初步结果，并提出了一个计划，以进一步建立nORF与生物信息学分析和实验验证手段的相关性，以更好地了解各种疾病。本报告首先对该领域的相关性和进展进行了一般性介绍，随后分为4章，每一章都有助于对功能性nORF候选人进行严格的自上而下的分析和优先排序。在第一章中，我们介绍了nORF数据库和平台（nORFs.org）的创建，从那时起，它构成了所有后续研究的参考数据库。第二章利用所建立的数据库设计特征描述符的尺寸，为后续的分析流程提供依据。第三章介绍了一个高维分析管道，用于质量控制和分析nORF、RefSeq基因和随机序列的组合数据集。在分析中，我们利用无监督聚类方法（t-SNE和UMAP）作为复杂机器学习方法的基础。第四章从t-SNE聚类中提取了37个序列样本，以确定潜在的功能关系。在这些分析步骤之后，报告得出了初步的监管nORF见解和未来的研究考虑。(VIVA摘录）