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The systematic functional analysis of regulatory novel open reading frames.

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。
批准号:
2116112
负责人:
金额:
$0.0万
依托单位:
依托单位国家:
英国
项目类别:
Studentship
财政年份:
2018
资助国家:
英国
项目状态:
已结题
起止时间:
2018 至 --

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中文摘要
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英文摘要
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)
期刊论文(1)
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会议论文
DOI: 10.1101/gr.263202.120
发表时间: 2021-03
期刊: Genome research
影响因子: 7
作者: [Neville MDC, Kohze R, Erady C, Meena N, Hayden M, Cooper DN, Mort M, Prabakaran S]
通讯作者: Prabakaran S
国内基金
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