Beyond heuristics: a tool for the rigorous statistical analysis of *-seq assays.

超越启发式：对 *-seq 检测进行严格统计分析的工具。

• 批准号: 8096347
• 负责人: peter J bickel
• 金额: $ 18.51万
• 依托单位: UNIVERSITY OF CALIFORNIA BERKELEY
• 依托单位国家: 美国
• 财政年份: 2011
• 资助国家: 美国
• 起止时间: 2011-06-27 至 2013-05-31
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/8096347
• 关键词: Algorithms; Animals; Biochemical; Biodiversity; Biological; Biological Assay; Biological Phenomena; Caenorhabditis elegans; Communities; Complex; Computer software; Data; Deoxyribonucleases; Drug Formulations; Event; Exons; Foundations; Frequencies; Generic Drugs; Genes; Genetic Polymorphism; Genetic Transcription; Genome; Genomics; Human Cell Line; Lasso; Letters; Location; Maps; Measures; Methods; Modeling; Names; Organism; Pathway interactions; Probability; Process; Property; Protein Isoforms; RNA; RNA Editing; Reading; Relative (related person); Research Personnel; Site; Software Validation; Statistical Models; System; Techniques; Technology; Testing; Transcript; Validation; Work; base; design; fly; heuristics; human tissue; member; next generation; prototype; research study; theories; tool

DESCRIPTION (provided by applicant): Assays based upon next generation sequencing technologies (*-seq assays) are widely used in the genomics community. As these assays mature and attempt to probe more subtle biological phenomenon, new tools based upon powerful statistical techniques will be needed to provide confidence in the resulting biological conclusions. To date, *-seq assay analysis tools can be split into two distinct classes, mapping and quantification. Mapping tools attempt to match each read with a genomic location, whereas quantification tools infer biological features from the "mapped" reads. The results of the mapping are often very dependent on tuning parameters and rarely, if ever, provide any notion of confidence. The analysis tools typically take the provided mappings as gospel. This project will take a different approach. The investigators propose to use known physical and biochemical properties of the assay to model the assay. Such an approach will yield better mappings, while providing a notion of confidence that can be made an integral part of downstream analysis. Extensive validation of the software and underlying models is planned in three organisms using data from five different validatory experiments. The work proposed in this project will result in significant improvements in the analyses of *-seq data. If successful, this project will replace a host of mapping algorithms, peak callers, and transcript quantifiers, forming the foundation of a software suite for the integrative analysis of *-seq assays. PUBLIC HEALTH RELEVANCE: The results of the mapping reads in assays based on next generation sequencing, (e.g. ChIP-seq, RNA-seq, DNase-seq) are often very dependent on tuning parameters without ever providing a notion of confidence, and downstream analysis tools typically take the provided mappings as gospel. Our approach is different: we make the known physical and biochemical properties of the assay and biological properties of the feature assayed an integral part of the mapping process and then on the basis of our assay model, set confidence limits on our mappings that can then be made an integral part of downstream analysis, analytical or biological. Our working prototype is called Statmap, which we intend to replace a host of mappers, peak callers, and transcript quantifiers as the principle tool for analysis and quantification in the computational genomicists arsenal by the end of 2012.

描述（由申请人提供）：基于下一代测序技术（*-seq检测）的检测广泛用于基因组学领域。随着这些检测方法的成熟，并试图探索更微妙的生物学现象，将需要基于强大的统计技术的新工具，以提供对所产生的生物学结论的信心。到目前为止，*-seq检测分析工具可以分为两个不同的类别，映射和定量。作图工具试图将每个读段与基因组位置匹配，而定量工具从“作图”读段推断生物学特征。映射的结果通常非常依赖于调优参数，并且很少（如果有的话）提供任何置信度概念。分析工具通常将所提供的映射视为福音。这个项目将采取不同的方法。研究者建议使用测定的已知物理和生化性质来模拟测定。这种方法将产生更好的映射，同时提供一个可以成为下游分析的组成部分的置信度概念。计划在三种生物体中使用来自五个不同验证实验的数据对软件和基础模型进行广泛验证。该项目中提出的工作将显著改善 *-seq数据的分析。如果成功，该项目将取代大量的映射算法，峰值调用器和转录本定量器，为 *-seq分析的综合分析软件套件奠定基础。 公共卫生相关性：基于下一代测序的测定（例如ChIP-seq、RNA-seq、DNase-seq）中的映射读数的结果通常非常依赖于调谐参数，而从未提供置信度的概念，并且下游分析工具通常将所提供的映射视为福音。我们的方法是不同的：我们使测定的已知物理和生物化学性质以及测定的特征的生物学性质成为映射过程的组成部分，然后基于我们的测定模型，对我们的映射设置置信限度，然后可以使其成为下游分析、分析或生物学的组成部分。我们的工作原型被称为Statmap，我们打算在2012年底之前取代大量的绘图器，峰值调用器和转录量化器，成为计算基因组学家武器库中分析和量化的主要工具。