DIEGO: detection of differential alternative splicing using Aitchison's geometry
DIEGO: detection of differential alternative splicing using Aitchison's geometry
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DOI：
10.1093/bioinformatics/btx690
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发表时间：
2018-03-15
期刊：
影响因子：
5.8
通讯作者：
Hoffmann, Steve
中科院分区：
文献类型：
--
作者：
Doose, Gero;Bernhart, Stephan H.;Hoffmann, Steve
动机：选择性剪接是大多数真核生物中至关重要的生物过程。它在细胞分化和基因调控中发挥着关键作用，并与许多不同的疾病有关。 RNA 测序能力的广泛应用允许对差异表达同工型进行更仔细的研究。然而，大多数差异选择性剪接 (DAS) 分析工具并未考虑拆分读数，即剪接事件的最直接证据。在这里，我们提出了 DIEGO，一种成分数据分析方法，能够基于分割读段检测两组 RNA-Seq 样本之间的 DAS。结果：Python 工具 DIEGO 无需异构体注释即可工作，速度足够快，可以分析大型实验，同时稳健且准确。我们为常见格式提供 python 和 perl 解析器。
Motivation: Alternative splicing is a biological process of fundamental importance in most eukaryotes. It plays a pivotal role in cell differentiation and gene regulation and has been associated with a number of different diseases. The widespread availability of RNA-Sequencing capacities allows an ever closer investigation of differentially expressed isoforms. However, most tools for differential alternative splicing ( DAS) analysis do not take split reads, i.e. the most direct evidence for a splice event, into account. Here, we present DIEGO, a compositional data analysis method able to detect DAS between two sets of RNA-Seq samples based on split reads.Results: The python tool DIEGO works without isoform annotations and is fast enough to analyze large experiments while being robust and accurate. We provide python and perl parsers for common formats.