Discover hidden splicing variations by mapping personal transcriptomes to personal genomes.
Discover hidden splicing variations by mapping personal transcriptomes to personal genomes.
复制标题
通过将个人转录组映射到个人基因组来发现隐藏的剪接变化。
DOI：
10.1093/nar/gkv1099
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发表时间：
2015-12-15
影响因子：
14.9
通讯作者：
Xing Y
中科院分区：
文献类型：
--
作者：
Stein S;Lu ZX;Bahrami-Samani E;Park JW;Xing Y
RNA-seq已成为研究前体mRNA选择性剪接遗传变异的热门技术。通常使用的RNA-seq比对器依赖于共有剪接位点二核苷酸基序来映射跨剪接点的读段。因此，产生新的剪接位点二核苷酸的基因组变体可以产生不能映射到参考基因组的剪接点RNA-seq读数。我们开发并评估了一种方法，通过将个人RNA-seq数据映射到个人基因组来识别个人转录组中的“隐藏”剪接变异。计算分析和实验验证表明，这种方法识别个人特定的剪接点在一个低的假阳性率。将这种方法应用于75个个体的RNA-seq数据集，我们确定了506个个人特异性剪接点，其中437个是目前人类转录本注释中未记录的新型剪接点。94个剪接点具有与人类性状和疾病的GWAS信号相关的剪接位点SNP。这些研究涉及剪接变异与疾病有关的基因（如OAS 1），以及选择性剪接与疾病之间的新关联（如ICA 1）。总的来说，我们的工作表明，RNA-seq读取比对的个人基因组方法能够发现人类群体中大量但以前未知的剪接变异目录。
RNA-seq has become a popular technology for studying genetic variation of pre-mRNA alternative splicing. Commonly used RNA-seq aligners rely on the consensus splice site dinucleotide motifs to map reads across splice junctions. Consequently, genomic variants that create novel splice site dinucleotides may produce splice junction RNA-seq reads that cannot be mapped to the reference genome. We developed and evaluated an approach to identify ‘hidden’ splicing variations in personal transcriptomes, by mapping personal RNA-seq data to personal genomes. Computational analysis and experimental validation indicate that this approach identifies personal specific splice junctions at a low false positive rate. Applying this approach to an RNA-seq data set of 75 individuals, we identified 506 personal specific splice junctions, among which 437 were novel splice junctions not documented in current human transcript annotations. 94 splice junctions had splice site SNPs associated with GWAS signals of human traits and diseases. These involve genes whose splicing variations have been implicated in diseases (such as OAS1), as well as novel associations between alternative splicing and diseases (such as ICA1). Collectively, our work demonstrates that the personal genome approach to RNA-seq read alignment enables the discovery of a large but previously unknown catalog of splicing variations in human populations.