Molecular Biology Institute, Center for Computational Biology, Institute for Genomics and Proteomics, Department of Chemistry and Biochemistry, University of California Los Angeles, Los Angeles, California, USA.
PLoS Comput Biol. 2009 Dec;5(12):e1000608. doi: 10.1371/journal.pcbi.1000608. Epub 2009 Dec 18.
High-throughput methods such as EST sequencing, microarrays and deep sequencing have identified large numbers of alternative splicing (AS) events, but studies have shown that only a subset of these may be functional. Here we report a sensitive bioinformatics approach that identifies exons with evidence of a strong RNA selection pressure ratio (RSPR)--i.e., evolutionary selection against mutations that change only the mRNA sequence while leaving the protein sequence unchanged--measured across an entire evolutionary family, which greatly amplifies its predictive power. Using the UCSC 28 vertebrate genome alignment, this approach correctly predicted half to three-quarters of AS exons that are known binding targets of the NOVA splicing regulatory factor, and predicted 345 strongly selected alternative splicing events in human, and 262 in mouse. These predictions were strongly validated by several experimental criteria of functional AS such as independent detection of the same AS event in other species, reading frame-preservation, and experimental evidence of tissue-specific regulation: 75% (15/20) of a sample of high-RSPR exons displayed tissue specific regulation in a panel of ten tissues, vs. only 20% (4/20) among a sample of low-RSPR exons. These data suggest that RSPR can identify exons with functionally important splicing regulation, and provides biologists with a dataset of over 600 such exons. We present several case studies, including both well-studied examples (GRIN1) and novel examples (EXOC7). These data also show that RSPR strongly outperforms other approaches such as standard sequence conservation (which fails to distinguish amino acid selection pressure from RNA selection pressure), or pairwise genome comparison (which lacks adequate statistical power for predicting individual exons).
高通量方法，如 EST 测序、微阵列和深度测序，已经鉴定出大量的可变剪接（AS）事件，但研究表明，这些事件中只有一部分可能具有功能。在这里，我们报告了一种敏感的生物信息学方法，该方法可以识别出具有强烈 RNA 选择压力比（RSPR）证据的外显子，即跨整个进化家族测量时，对仅改变 mRNA 序列而不改变蛋白质序列的突变具有进化选择的外显子——这极大地增强了其预测能力。使用 UCSC 28 个脊椎动物基因组比对，这种方法正确预测了已知是 NOVA 剪接调节因子结合靶标的一半到四分之三的 AS 外显子，并且预测了 345 个人类和 262 个老鼠中强烈选择的可变剪接事件。这些预测得到了几个功能 AS 的实验标准的强烈验证，例如在其他物种中独立检测到相同的 AS 事件、阅读框保存和组织特异性调节的实验证据：在十个组织的面板中，75%（15/20）的高 RSPR 外显子显示出组织特异性调节，而在低 RSPR 外显子样本中，只有 20%（4/20）。这些数据表明，RSPR 可以识别具有功能重要剪接调节的外显子，并为生物学家提供了超过 600 个这样的外显子的数据集。我们展示了几个案例研究，包括研究得很好的例子（GRIN1）和新的例子（EXOC7）。这些数据还表明，RSPR 明显优于其他方法，如标准序列保守性（无法区分氨基酸选择压力和 RNA 选择压力）或成对基因组比较（缺乏预测单个外显子的足够统计能力）。
