Power calculation and design issues in next-generation sequencing

下一代测序中的功率计算和设计问题

• 批准号: 8963892
• 负责人: George C. Tseng
• 金额: $ 22.5万
• 依托单位: UNIVERSITY OF PITTSBURGH AT PITTSBURGH
• 依托单位国家: 美国
• 财政年份: 2015
• 资助国家: 美国
• 起止时间: 2015-07-07 至 2019-06-30
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/8963892
• 关键词: Binomial Model; Bioconductor; Bioinformatics; Biological Markers; Budgets; Computer software; Cost-Benefit Analysis; DNA Methylation; Data; Detection; Diagnosis; Drops; Equilibrium; Event; Expenditure; Experimental Designs; Future; Galaxy; Gene Expression; Gene Fusion; Genes; Genomics; Goals; Health; Java; Low Prevalence; Malignant Neoplasms; Malignant neoplasm of prostate; Massive Parallel Sequencing; Methods; Methylation; Modeling; Online Systems; Outcome; Patients; Prevalence; Prostate; Reading; Research; Research Design; Research Personnel; Resolution; Sample Size; Sampling; Software Tools; Solutions; Somatic Mutation; Specific qualifier value; System; Technology; Transcript; base; candidate marker; collaborative environment; cost; cost effective; data modeling; deep sequencing; design; differential expression; fusion gene; genome-wide; genome-wide analysis; human disease; improved; next generation sequencing; novel; open source; outcome forecast; public health relevance; research study; sample collection; software development; statistics; tool; transcriptome sequencing; tumor; two-dimensional

 DESCRIPTION (provided by applicant): Next-generation sequencing has brought revolutionary genome-wide, dense resolution and high-throughput capability to perform various types of omics analyses, including gene expression, methylation, fusion gene, somatic mutation and many others. With the dropping costs, the technology is gaining popularity. The experimental expenses, however, remain significant and power calculation tools are essential to adequately design and guide an NGS analysis. Unlike power calculation in traditional experiments or microarrays, power calculation in NGS require simultaneous consideration of sample size and sequencing depth and count-data also bring statistical challenges. We propose the following aims in this proposal: (1a) Develop power calculation tools for differential expression analysis from RNA-seq experiments. Optimal sample size and sequencing depth are jointly determined by power function and budget constraints. (1b) Develop power calculation tools for differential methylation in methyl-seq experiments. (2a) Develop power calculation tools for fusion gene detection in cancer using RNA-seq. Identify sample size and sequencing depth needed for fusion genes with low prevalence and low allelic-fraction. (2b) Perform additional ultra-deep sequencing in the preliminary prostate study to identify additional low-allelic-fraction and prognosis predictive fusion genes. Successful completion of these aims will provide state-of-the-art power calculation tools for the fast growing projects using NGS technology for candidate marker and fusion gene detection.

 描述(申请人提供)：下一代测序带来了革命性的全基因组、高分辨率和高通量能力，可以执行各种类型的组学分析，包括基因表达、甲基化、融合基因、体细胞突变等许多其他方面。随着成本的下降，这项技术越来越受欢迎。然而，实验费用仍然很高，功率计算工具对于充分设计和指导NGS分析是必不可少的。与传统实验或微阵列中的功率计算不同，NGS中的功率计算需要同时考虑样本大小和测序深度，而计数数据也带来了统计挑战。我们在这个建议中提出了以下目标：(1A)从RNA-SEQ实验中开发用于差异表达分析的功率计算工具。最优样本量和测序深度由幂函数和预算约束共同决定。(1B)开发甲基-SEQ实验中差异甲基化的功率计算工具。(2a)利用RNA-seq开发用于癌症融合基因检测的功率计算工具。确定低患病率和低等位基因比例的融合基因所需的样本量和测序深度。(2B)在初步的前列腺研究中执行额外的超深测序，以确定额外的低等位基因部分 和预测预后的融合基因。这些目标的成功实现将为使用NGS技术进行候选标记和融合基因检测的快速增长项目提供最先进的功率计算工具。