Jupyter and Galaxy: Easing entry barriers into complex data analyses for biomedical researchers

What does it take to convert a heap of sequencing data into a publishable result? First, common tools are employed to reduce primary data (sequencing reads) to a form suitable for further analyses (i.e., the list of variable sites). The subsequent exploratory stage is much more ad hoc and requires the development of custom scripts and pipelines, making it problematic for biomedical researchers. Here, we describe a hybrid platform combining common analysis pathways with the ability to explore data interactively. It aims to fully encompass and simplify the "raw data-to-publication" pathway and make it reproducible.

如何将海量测序数据转化为可发表的研究成果？首先，需借助通用工具将原始数据（测序读段，sequencing reads）处理为适配后续分析的形式，即可变位点列表。后续的探索性分析阶段更具临时性与定制化属性，需要开发专属脚本与分析流程，这给生物医学研究者造成了一定的操作困境。本研究介绍一款混合式分析平台，其整合了通用分析路径与交互式数据探索功能，旨在完整覆盖并简化"原始数据到发表成果"的全流程，同时保障分析的可重复性。