Department of Computer Science, Johns Hopkins University, Baltimore, MD 21205, USA.
Department of Oncology, Johns Hopkins School of Medicine, Baltimore, MD 21205, USA.
Bioinformatics. 2021 Nov 18;37(22):4272-4274. doi: 10.1093/bioinformatics/btab401.
Gene alternative splicing plays an important role in development, tissue specialization and disease and differences in splicing patterns can reveal important factors for phenotypic differentiation. While multiple computational methods exist to determine splicing differences, there is a need for user-friendly visualizations that present an intuitive view of the data and work across methods.
We developed a toolkit, Jutils, for visualizing differential splicing events at the intron (splice junction) level. Jutils is method-agnostic, converting individual tools' output into a unified representation and using it to create visualizations. Jutils creates three types of visualizations, namely heatmaps of absolute and Z-score normalized splice ratios, sashimi plots and Venn diagrams of results from multiple comparisons. Jutils is lightweight, relying solely on the unified data file for visualizations.
Jutils is implemented in Python and is available from https://github.com/Splicebox/Jutils.
基因选择性剪接在发育、组织特化和疾病中起着重要作用，剪接模式的差异可以揭示表型分化的重要因素。虽然存在多种用于确定剪接差异的计算方法，但需要用户友好的可视化工具，直观地显示数据并适用于各种方法。
我们开发了一个名为 Jutils 的工具包，用于在 intron（剪接接头）水平可视化差异剪接事件。Jutils 是一种与方法无关的工具，它将各个工具的输出转换为统一的表示形式，并使用它来创建可视化效果。Jutils 创建了三种类型的可视化效果，即绝对和 Z 分数标准化剪接比的热图、sashimi 图以及来自多个比较的结果的 Venn 图。Jutils 很轻量级，仅依赖于统一的数据文件进行可视化。
Jutils 是用 Python 实现的，可以从 https://github.com/Splicebox/Jutils 获得。
