Detecting tissue-specific regulation of alternative splicing as a qualitative change in microarray data
Detecting tissue-specific regulation of alternative splicing as a qualitative change in microarray data
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DOI：
10.1093/nar/gnh173
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发表时间：
2004-01-01
影响因子：
14.9
通讯作者：
Lee, C
中科院分区：
文献类型：
--
作者：
Le, K;Mitsouras, K;Lee, C
选择性剪接最近已成为人类基因组中的一种主要调控机制，可能存在于40%-60%的人类基因中。因此，原则上，功能调控的微阵列研究不仅可以扩展到检测基因整体表达的变化，还可以检测不同组织之间剪接模式的变化。然而，由于基因总表达的变化及其选择性剪接的变化可以在一组样本中以复杂的方式混合在一起，因此分离这些影响可能很困难，而且对于准确评估它们是必不可少的。我们提出了一种简单而通用的方法来区分选择性剪接的变化和表达的变化，该方法基于检测两个不同样本的对数比率与包含两个样本的池之间的系统反相关性。我们已经在五个人体组织的微阵列数据上测试了这种分析方法，这些数据使用标准的微阵列平台和实验协议生成，之前显示对替代剪接敏感。我们的自动化分析能够检测到各种各样的组织特异性选择性剪接事件，如外显子跳跃、互斥外显子、备选3‘和备选5’剪接、备选起始和备选终止，所有这些都通过独立的逆转录酶PCR实验进行了验证，确认率为70%-85%。我们的分析方法还可以根据基因和样本与其选择性剪接模式的相似程度进行分级聚类，揭示组织特异性调控模式，这些模式不同于从相同微阵列数据中对基因表达进行分级聚类获得的模式。我们的数据和分析源代码可从http://www.bioinformatics.ucla.edu/ASAP.获得
Alternative splicing has recently emerged as a major mechanism of regulation in the human genome, occurring in perhaps 40-60% of human genes. Thus, microarray studies of functional regulation could, in principle, be extended to detect not only the changes in the overall expression of a gene, but also changes in its splicing pattern between different tissues. However, since changes in the total expression of a gene and changes in its alternative splicing can be mixed in complex ways among a set of samples, separating these effects can be difficult, and is essential for their accurate assessment. We present a simple and general approach for distinguishing changes in alternative splicing from changes in expression, based on detecting systematic anti-correlation between the log-ratios of two different samples versus a pool containing both samples. We have tested this analysis method on microarray data for five human tissues, generated using a standard microarray platform and experimental protocols shown previously to be sensitive to alternative splicing. Our automatic analysis was able to detect a wide variety of tissue-specific alternative splicing events, such as exon skipping,mutually exclusive exons, alternative 3' and alternative 5' splicing, alternative initiation and alternative termination, all of which were validated by independent reverse-transcriptase PCR experiments, with validation rates of 70-85%. Our analysis method also enables hierarchical clustering of genes and samples by the level of similarity to their alternative splicing patterns, revealing patterns of tissue-specific regulation that are distinct from those obtained by hierarchical clustering of gene expression from the same microarray data. Our data and analysis source code are available from http://www.bioinformatics.ucla.edu/ASAP.