Model-based detection of alternative splicing signals
Model-based detection of alternative splicing signals
复制标题
DOI：
10.1093/bioinformatics/btq200
复制
发表时间：
2010-06-15
期刊：
影响因子：
5.8
通讯作者：
Frey, Brendan J.
中科院分区：
文献类型：
--
作者：
Barash, Yoseph;Blencowe, Benjamin J.;Frey, Brendan J.
动机：人类多外显子基因中约95%的转录本都经历了选择性剪接（AS）。AS对转录组和蛋白质组复杂性的突出贡献以及异常AS在许多疾病中的作用推动了对AS的日益增长的兴趣。最近的技术进步使成千上万的外显子在不同的细胞类型和细胞条件下同时进行分析，但需要准确识别条件特异性剪接变化。有必要准确地识别这种剪接的变化，以阐明潜在的监管程序或链接的剪接变化，以特定的diseases.Results：我们提出了一个概率模型定制的高通量AS数据，观察到的异构体水平被解释为条件特异性AS信号的组合。根据我们的公式，给定一个AS数据集，我们的任务是检测数据中的共同信号，并识别与每个信号相关的外显子。我们的模型可以结合有关潜在AS信号，测量质量和基因表达水平影响的先验知识。使用大规模的多组织AS数据集，我们证明了我们的方法比标准的替代方法的优势。此外，我们还描述了新发现的组织特异性AS信号，这些信号经过了实验验证，并讨论了相关的监管特征。联系方式：yoseph@psi.utoronto.ca; frey@psi.utoronto. ca补充信息：补充数据可在生物信息学在线获得。
Motivation: Transcripts from similar to 95% of human multi-exon genes are subject to alternative splicing (AS). The growing interest in AS is propelled by its prominent contribution to transcriptome and proteome complexity and the role of aberrant AS in numerous diseases. Recent technological advances enable thousands of exons to be simultaneously profiled across diverse cell types and cellular conditions, but require accurate identification of condition-specific splicing changes. It is necessary to accurately identify such splicing changes to elucidate the underlying regulatory programs or link the splicing changes to specific diseases.Results: We present a probabilistic model tailored for high-throughput AS data, where observed isoform levels are explained as combinations of condition-specific AS signals. According to our formulation, given an AS dataset our tasks are to detect common signals in the data and identify the exons relevant to each signal. Our model can incorporate prior knowledge about underlying AS signals, measurement quality and gene expression level effects. Using a large-scale multi-tissue AS dataset, we demonstrate the advantage of our method over standard alternative approaches. In addition, we describe newly found tissue-specific AS signals which were verified experimentally, and discuss associated regulatory features.Contact: yoseph@psi.utoronto.ca; frey@psi.utoronto.caSupplementary information: Supplementary data are available at Bioinformatics online.