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（由应用提供）：下一代测序带来了革命性基因组，密集的分辨率和高通量能力，以执行各种类型的OMICS分析，包括基因表达，甲基化，融合基因，体细胞突变等。随着成本下降，该技术越来越流行。但是，实验费用仍然显着，功率计算工具对于充分设计和指导NGS分析至关重要。与传统实验或微阵列中的功率计算不同，NGS中的功率计算需要简单考虑样本量，测序深度和计数数据也带来了统计挑战。我们提出以下目的在此提案中：（1A）开发功率计算工具，用于从RNA-Seq实验中进行差异表达分析。最佳样本量和测序深度由功率功能和预算约束共同确定。 （1B）开发用于甲基季节实验中差甲基化的功率计算工具。 （2a）使用RNA-Seq开发用于癌症融合基因检测的功率计算工具。确定融合基因患病率较低且等位基因分数所需的样本量和测序深度。 （2b）在初步前列腺研究中进行其他超深测序，以识别其他低平行分数 和预后预测融合基因。这些目标的成功完成将为快速增长的项目提供最新的功率计算工具，该工具使用NGS技术进行候选标记和融合基因检测。