Overcoming bias and unwanted variability in next generation sequencing

 描述(申请人提供)：下一代测序(NGS)已成为生物学中应用最广泛的高通量技术。今天，NGS的应用远远超出了基因组测序和DNA序列本身的研究，包括对发育和疾病中潜在的基因组功能的定量和动态结果的测量。这些测量，特别是RNA丰度、蛋白质结合、DNA甲基化和微生物组组成，是大型财团和单个实验室进行的研究的核心。然而，在衡量这些定量结果时，NGS数据受到严重的技术和生物偏差、系统误差和不可预见的变异性的影响，这可能会对下游分析产生很大影响。只有当这些问题能够被容易地识别和解决时，这项技术才能最大限度地造福于科学和医学。我们团队在开发将原始高通量数据转换为生物学家和临床医生所依赖的最终测量数据的统计方法方面拥有丰富的经验。我们的基因表达阵列预处理方法实际上是一个行业标准，我们最近在NGS应用方面的工作被广泛引用和使用。此外，Irizarry博士还共同领导了BioConductor项目，这是开发和传播最先进的统计方法的最广泛使用的开源项目之一。我们建议继续利用我们在高通量技术方面的经验，在迫切需要可靠的统计分析工具的四个关键、广泛使用的应用中为NGS数据开发不可或缺的分析工具。我们方法的核心是在这四个应用程序中克服偏差、系统误差和不可预见的变异性的共同需求。为了帮助开发和评估这些工具，我们提出了专门设计作为基准的实验。这些问题与我们的具体专业知识很好地匹配，我们将通过以下目标解决这些问题。1)开发对测序伪像具有鲁棒性的RNA转录本估计的统计方法。2)发展估计DNA甲基化数据中异质细胞组成的统计方法。3)发展微生物群落16S rRNA基因测序研究中无偏量化的统计方法。4)开发在全基因组浓缩扫描中考虑到协议引起的偏差的方法(例如，芯片序列和DNA酶I序列)。

 DESCRIPTION (provided by applicant): Next Generation Sequencing (NGS) has become the most widely used high-throughput technology in biology. Today, NGS applications go far beyond genome sequencing and studies of DNA sequence itself to include the measurement of quantitative and dynamic outcomes underlying genomic function in development and disease. These measurements, specifically, RNA abundance, protein binding, DNA methylation, and microbiome composition, are at the core of studies undertaken by large consortia and individual labs alike. However, when measuring these quantitative outcomes, NGS data are subject to severe technological and biological biases, systematic errors, and unforeseen variability which can greatly impact downstream analyses. Only when these issues can be readily identified and addressed will the technology maximally benefit science and medicine. Our group has extensive experience developing statistical methods that transform raw high- throughput data into the ultimate measurements relied upon by biologists and clinicians. Our gene expression array preprocessing methods are practically an industry standard and our recent work on NGS applications is widely cited and used. Furthermore, Dr. Irizarry co-leads the Bioconductor project, one of the most widely used open-source projects for the development and dissemination of state-of-the-art statistical methodology. We propose to continue to leverage our experience with high-throughput technologies to develop indispensable analysis tools for NGS data in four critical, widely used applications urgently requiring reliable statistical analysis tols. At the core of our methods is the common need, across these four applications, to overcome bias, systematic error, and unforeseen variability. To aid in the development and assessment of these tools we propose experiments specifically designed to serve as benchmarks. These problems are matched well to our specific expertise and we will address them with the following aims. 1) Develop statistical methods for RNA transcript estimation that are robust to sequencing artifacts. 2) Develop statistical methods that estimate heterogenous cell composition in DNA methylation data. 3) Develop statistical methods for unbiased quantification in microbial community 16S rRNA gene sequencing studies. 4) Develop methods that account for protocol-induced bias in genome-wide enrichment scans (e.g., ChIP-seq and DNase I-seq).