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A New Platform to Identify Utilized Coding Sequences

A New Platform to Identify Utilized Coding Sequences
识别已使用编码序列的新平台
批准号:
1042335
负责人:
Igor Libourel
金额:
$25.69万
依托单位国家:
美国
项目类别:
Standard Grant
财政年份:
2010
资助国家:
美国
项目状态:
已结题
起止时间:
2010-09-15 至 2013-08-31

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中文摘要
翻译
智力优势:廉价、高保真和高通量测序技术的快速发展促进了无数基因组的确定。测序数据为物种的进化提供了有价值的见解,并在相关生物的基因注释中发挥了关键作用。然而,阐明基因与其精确功能之间的直接关系仍然很费力。同样,尽管蛋白质中的功能域通常是已知的,但确定哪些氨基酸是必需的,哪些氨基酸被允许替代仍然需要巨大的时间投入。该项目利用高通量测序技术在核苷酸水平上测量全基因组对基因的利用。细菌细胞将在明确的实验条件下,在强诱变剂的持续存在下生长。这种处理将导致未利用的基因或编码可替代氨基酸的部分已利用基因的突变积累。引入的变异将通过DNA测序进行检测,结果应该区分已利用的基因和未利用的基因,以及可替换的氨基酸和蛋白质功能所需的氨基酸。广泛的影响:该平台将广泛应用于工业和学术目的,使协议驱动的基因发现成为可能。该方法不需要太多关于新陈代谢的前期知识,并使用生物计算来评估基因功能。该项目将为博士后研究员和本科生提供教育机会。
英文摘要
Intellectual Merit: The rapid development of inexpensive, high fidelity, and high-throughput sequencing technologies has facilitated the determination of countless genomes. Sequencing data have led to valuable insights into the descent of species and have played a key role in the annotation of genes in related organisms. However, the elucidation of the direct relationship between a gene and its precise function is still laborious. Similarly, although functional domains in proteins are often known, determining which amino acids are essential and which amino acid substitutions are allowed still requires an enormous time investment. This project leverages high-throughput sequencing technology to measure genome-wide utilization of genes at the nucleotide level. Bacterial cells will be grown under well-defined experimental conditions in the continual presence of a strong mutagen. This treatment will result in the accumulation of mutations in unutilized genes or in parts of utilized genes coding for replaceable amino acids. The introduced variation will be detected by DNA sequencing and the results should distinguish utilized genes from unutilized ones, and replaceable amino acids from those required for protein function.Broader Impacts: This platform will be widely applicable, both for industrial and academic purposes to enable protocol-driven gene discovery. The method does not require much upfront knowledge about metabolism, and uses biocomputing for assessing gene function. The project will provide educational opportunities for a postdoctoral fellow and for undergraduate students.
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