Methods for sequencing data analysis and archive-scale data science

项目摘要 我们将开发方法和维护软件，使生物医学研究人员更容易地 使用和理解测序数据。该项目将支持我们维持和改善我们的受欢迎的 用于分析测序数据的“上游”工具。其中包括Bowtie和Bowtie 2工具， 用于宏基因组学分类的Kraken 2工具和用于基因组草图绘制的Dashing工具 和比较。我们还将开发新的系统，使研究人员能够使用这些相同的核心工具 （Bowtie，Kraken 2，Dashing）快速发现和审查归档数据集。我们将使研究人员能够 快速确定数据集是否具有高质量，存在哪些物种，是否存在污染物， 存在，执行了什么测定，哪些数据集彼此相似，以及哪些数据集 与注释的元数据不一致。通过这种方式，研究人员可以提取相关的存档数据集， 具有预期的生物学特性，以一种不依赖于相关生物学特性的准确性的方式， 元数据.最后，我们将努力开发新的基础设施，用于大规模的再分析和索引。 存档数据，最终产生新的“搜索引擎”，用于科学问答。特别是， 我们将扩展我们过去在Rail-RNA、recount 2和Snaptron上的工作，以便我们能够更有效地 分析大量存档数据，将其转换为各种有用的摘要形式， 而不是增加一层索引，以便用户可以在科学的背景下查询摘要， 调查我们还将创建新的目录和机制，使研究人员可以分享他们的 档案辅助研究设计，以便归档数据集的有用组合以及对其中的见解 他们的元数据可能不正确或不完整，可以报告和共享。

PROJECT SUMMARY We will develop methods and maintain software that make it radically easier for biomedical researchers to use and understand sequencing data. The project will support our maintaining and improving our popular “upstream” tools for analyzing sequencing data. These include the Bowtie and Bowtie 2 tools for read alignment, the Kraken 2 tool for metagenomics classification and the Dashing tool for genomic sketching and comparison. We will also develop new systems that allow researchers to use these same core tools (Bowtie, Kraken 2, Dashing) to rapidly discover and vet archived datasets. We will enable researchers to quickly ascertain whether a dataset is of high quality, what species are present, whether contaminants are present, what assay was performed, what datasets are similar to each other, and what datasets are inconsistent with annotated metadata. In this way, researchers can distill relevant archived datasets, those having the expected biological properties, in a way that does not hinge on the accuracy of the associated metadata. Finally, we will work to develop new infrastructure for large-scale reanalysis and indexing of archived data, ultimately yielding new “search engines” for scientific question-answering. In particular, we will extend our past work on the Rail-RNA, recount2 and Snaptron so that we can more effectively analyze huge collections of archived data, converting them into a variety of useful summary forms, and than adding a layer of indexing so that users can query the summaries in the context of a scientific investigation. We will also create new catalogs and mechanisms whereby researchers can share their archive-assisted study designs, so that useful combinations of archived datasets, and insights into where their metadata might be incorrect or incomplete, can be reported and shared.