Inference of alternative splicing from RNA-Seq data with probabilistic splice graphs
Inference of alternative splicing from RNA-Seq data with probabilistic splice graphs
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DOI：
10.1093/bioinformatics/btt396
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发表时间：
2013-09-15
期刊：
影响因子：
5.8
通讯作者：
Dewey, Colin N.
中科院分区：
文献类型：
--
作者：
LeGault, Laura H.;Dewey, Colin N.
动机：在真核细胞中，选择性剪接和其他允许从同一基因衍生出不同转录本的过程是重要的力量。RNA-Seq是一种很有前途的分析替代转录本的技术，因为它不需要事先了解转录本结构或基因组序列。然而，由于效率、可识别性和代表性问题，在存在大量替代转录本的基因中分析RNA-Seq数据目前具有挑战性。结果：我们提出了基于概率剪接图概念的RNA-Seq模型和相关推理算法，缓解了这些问题。我们证明了我们的模型通常是可识别的，并且证明了我们用于量化和差分处理检测的推理方法是有效和准确的。
Motivation: Alternative splicing and other processes that allow for different transcripts to be derived from the same gene are significant forces in the eukaryotic cell. RNA-Seq is a promising technology for analyzing alternative transcripts, as it does not require prior knowledge of transcript structures or genome sequences. However, analysis of RNA-Seq data in the presence of genes with large numbers of alternative transcripts is currently challenging due to efficiency, identifiability and representation issues.Results: We present RNA-Seq models and associated inference algorithms based on the concept of probabilistic splice graphs, which alleviate these issues. We prove that our models are often identifiable and demonstrate that our inference methods for quantification and differential processing detection are efficient and accurate.