PDF(Pubmed)
Abstract：
As genome sequencing technologies advance, the accumulation of sequencing data in public databases necessitates more robust and adaptable data analysis workflows. Here, we present Rocketchip, which aims to offer a solution to this problem by allowing researchers to easily compare and swap out different components of ChIP-seq, CUT&RUN, and CUT&Tag data analysis, thereby facilitating the identification of reliable analysis methodologies. Rocketchip enables researchers to efficiently process large datasets while ensuring reproducibility and allowing for the reanalysis of existing data. By supporting comparative analyses across different datasets and methodologies, Rocketchip contributes to the rigor and reproducibility of scientific findings. Furthermore, Rocketchip serves as a platform for benchmarking algorithms, allowing researchers to identify the most accurate and efficient analytical approaches to be applied to their data. In emphasizing reproducibility and adaptability, Rocketchip represents a significant step towards fostering robust scientific research practices.
摘要：
随着基因组测序技术的进步，在公共数据库中积累测序数据需要更健壮和适应性更强的数据分析工作流程.这里,我们展示了火箭芯片，它旨在通过允许研究人员轻松比较和交换ChIP-seq的不同组件来提供解决此问题的方法，CUT&RUN,以及CUT和标签数据分析，从而有助于确定可靠的分析方法。Rocketchip使研究人员能够有效地处理大型数据集，同时确保可重复性并允许重新分析现有数据。通过支持跨不同数据集和方法的比较分析，Rocketchip有助于科学发现的严谨性和可重复性。此外，Rocketchip作为基准算法的平台，允许研究人员确定最准确和有效的分析方法，以应用于他们的数据。在强调再现性和适应性时，Rocketchip代表了促进强大科学研究实践的重要一步。
