Hierarchical Modeling and Parallelized Bayesian Inference for RNAseq Analysis

RNAseq 分析的分层建模和并行贝叶斯推理

• 批准号: 8639666
• 负责人: DANIEL NETTLETON
• 金额: $ 27.2万
• 依托单位: IOWA STATE UNIVERSITY
• 依托单位国家: 美国
• 财政年份: 2013
• 资助国家: 美国
• 起止时间: 2013-09-01 至 2017-05-31
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/8639666
• 关键词: Alleles; Bayesian Method; Bioinformatics; Biology; Complex; Computing Methodologies; Data; Data Analyses; Detection; Development; Dimensions; Disease; Exhibits; Funding; Gene Expression; Gene Expression Profiling; Genes; Genetic; Genetic Phenomena; Genomics; Hybrid Vigor; Hybrids; Inbreeding; Lead; Linear Models; Medical; Methods; Modeling; Molecular; Parents; Pattern; Public Health; RNA Sequence Analysis; RNA Sequences; Research; Research Personnel; Sample Size; Source; Technology; Work; complex biological systems; design; flexibility; gene function; offspring; public health relevance; response; statistics; tool

DESCRIPTION (provided by applicant): This proposal focuses on the development of hierarchical models and parallelized Bayesian inference for the analysis of RNA sequencing (RNAseq) data. Special emphasis is placed on gene expression profiling of parental inbred lines and their hybrid offspring for the discovery of key genes underlying heterosis, the genetic phenomenon otherwise known as hybrid vigor. The project will be led by a collaborative team of researchers with expertise in the analysis of high-dimensional gene expression data, Bayesian inference, bioinformatics, biology, computational methods, genetics, genomics, and statistics. The proposed research provides new tools for the analysis of high-dimension and low-sample-size count data generated by RNAseq technology. Hierarchical modeling allows for flexible information sharing across dimensions to extract as much information as possible from data. Parallel methods for Bayesian inference harness the power of modern computing to produce comprehensive results in a timely manner. Specific methods will be developed for (i) the identification of genes that exhibit expression heterosis, (ii) the detection of expressed and non-expressed genes, and (iii) the discovery of differential allele usage in hybrids. These methods will provide a deeper understanding of the molecular mechanisms of heterosis and lead to the discovery of key genes whose expression patterns provide hybrids with advantages over their parents. This information can be used to efficiently predict which of thousands of possible crosses will result in top performing hybrids. In addition to the specific methods mentioned above, hierarchical generalized linear models for the simultaneous analysis of tens of thousands of response variables will be developed. This work will permit the analysis of RNAseq data from complex designs with multiple sources of variability and will greatly extend the range of applicability for the funded research to encompass a variety of challenges in high-dimensional data analysis.

描述（由申请人提供）：该提案着重于层次模型的开发和并行化的贝叶斯推断，用于分析RNA测序（RNASEQ）数据。 特别强调父母近交系的基因表达分析及其在发现基因杂种下的关键基因的杂种后代，这种遗传现象是遗传现象。 该项目将由一个研究人员组成的合作团队，在分析高维基因表达数据，贝叶斯推论，生物信息学，生物学，计算方法，遗传学，基因组学和统计学方面具有专业知识。 拟议的研究为分析RNASEQ技术生成的高维度和低样本尺寸计数数据提供了新的工具。 层次建模允许跨维度共享灵活的信息，以从数据中提取尽可能多的信息。 贝叶斯推理的平行方法利用了现代计算的力量及时产生全面的结果。 将开发出（i）鉴定表现出表达杂种的基因，（ii）检测表达和非表达基因的基因，以及（iii）在杂种中发现差异等位基因使用情况。 这些方法将对杂种的分子机制有更深入的了解，并导致发现关键基因的表达模式为杂种提供了比父母优势的杂种。 这些信息可用于有效预测成千上万个可能的杂交中的哪个将导致最佳性能杂种。除了上述特定方法外，还将开发用于数以万计的响应变量的层次概括线性模型。 这项工作将允许对具有多种可变性来源的复杂设计的RNASEQ数据进行分析，并将大大扩展资助研究的适用性范围，以应对高维数据分析中的各种挑战。