Project-002

Bioconductor是一个成功的高通量数据分析和理解项目。一个关键 这一成功的一个组成部分是与前沿技术相关的创新统计算法的开发 生物信息学数据类型该提案侧重于算法开发的三个方面， 项目的持续成功。第一个目标是（1）实施方法和数据结构支持 用于现代基因组测定中的信号恢复和推断。信号恢复和推理是 将原始数据转化为有意义的摘要的基本步骤。第二个目标是（2）发展 为基因组生物学和基因组医学提供注释和工作流程支持。这就认识到以下方面的重要性： 将统计学观点与生物学意义联系起来。最终目标是（3）提供必要的 基础设施，以支持基因组规模数据的可扩展计算分析。由此产生的一个关键想法是， STAMP（扫描、转换、合并、建模、打包）模式用于基因组分析。

Bioconductor is a successful project for the analysis and comprehension of high-throughput data. A key component of this success is the development of innovative statistical algorithms relevant to leading-edge bioinformatic data types. This proposal focuses on three aspects of algorithm development, to enable continued success of the project. The first aim is (1) to implement methodological and data structure support for signal recovery and inference across modern genomic assays. Signal recovery and inference are fundamental steps in the transformation of raw data into meaning summaries. The second aim is (2) to develop annotation and workflow support for genome biology and genomic medicine. This recognizes the importance of associating statistical insight with biological meaning. The final aim is (3) to provide the necessary infrastructure to support scalable computational analysis of genome-scale data. A key idea emerging from this work is the STAMP (scan, transform, amalgamate, model, pack) pattern for genomic analysis.