I-Corps: RNA-adenylation sequencing for rapid ribosome profiling
I-Corps: RNA-adenylation sequencing for rapid ribosome profiling
批准号:
2033614
负责人:
SHU-BING QIAN
金额:
$5.0万
依托单位:
依托单位国家:
美国
项目类别:
Standard Grant
财政年份:
2020
资助国家:
美国
项目状态:
已结题
起止时间:
2020-08-15 至 2022-01-31
中文摘要
点击翻译按钮获取中文摘要
英文摘要
The broader impact/commercial potential of this I-Corps project is the development of a next generation sequencing technology that aims to quantify ribosome-protected mRNA fragments, to infer protein synthesis with superior resolution. Given the central role of individual protein production in disease diagnosis and treatment, the proposed technology aims to bridge bench-side to bedside by accelerating personalized medicine. The proposed technology is readily applicable to cells in culture and solid tissues obtained by needle biopsy, and is expected to have application for a wide range of scientists and healthcare professionals. Further, the ability to explore the translation of protein production inside cells towards disease progression may offer novel strategies for treatment and prevention. The technology may enable a genetic testing approach that may have far-reaching impacts on basic research and clinical diagnosis. This I-Corps project is based on the development of an RNA-adenylation sequencing, a next generation sequencing technology. To date, monitoring individual protein production remains a formidable challenge in biomedical and clinical laboratories. Ribosome profiling (Ribo-seq) captures protein-producing mRNAs and reveals their translation step-by-step. This effort defines the requirements to provide a snapshot of ribosome positions and density across the transcriptome at a sub-codon resolution. The current Ribo-seq methodology requires a large amount of starting material, needs expensive equipment and reagents, and is time-consuming. Compared to the conventional Ribo-seq method, the proposed RNA-adenylation sequencing technology dramatically reduces the amount of starting material (~1 ng RNA), shortens the library processing time (~6 hour), and increases the footprint resolution (90% in-frame accuracy of 5' end). The proposed technology may improve genome-wide evaluation of translation with superior sensitivity at single-nucleotide resolution. In addition, the proposed technology may be adapted for RNA sequencing technology. The technology may be used for broad RNA next-generation sequencing in both laboratories and clinical settings.This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.
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Collaborative Research: MFB: Deciphering RNA-based regulatory logic with interpretable machine learning
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批准号:2226633
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项目类别:Standard Grant
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资助金额:$60.5万
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财政年份:2022
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负责人:SHU-BING QIAN
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依托单位:
国内基金
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