SplicingCompass: differential splicing detection using RNA-Seq data
SplicingCompass: differential splicing detection using RNA-Seq data
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DOI：
10.1093/bioinformatics/btt101
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发表时间：
2013-05-01
期刊：
影响因子：
5.8
通讯作者：
Koenig, Rainer
中科院分区：
文献类型：
--
作者：
Aschoff, Moritz;Hotz-Wagenblatt, Agnes;Koenig, Rainer
动机：选择性剪接是细胞过程的中心，并大大增加了转录组和蛋白质组的多样性。异常剪接事件通常具有病理后果，并与各种疾病和癌症类型有关。下一代RNA测序（RNA-seq）的出现为大规模分析选择性剪接提供了一种令人兴奋的新技术。然而，能够从短读测序中分析备选剪接的算法尚未完全建立，对于各种数据分析任务仍然没有标准的解决方案。结果：我们提出了一种利用RNA-seq数据预测两种不同条件下差异剪接的基因的新方法和软件。我们的方法使用了外显子读取计数的高维向量之间的几何角度。通过这种方法，即使剪接事件具有较高的复杂性，并且涉及以前未知的剪接模式，也可以检测到差异剪接。我们将我们的方法应用于两个病例研究，包括神经母细胞瘤的有利和不利的临床病程数据。我们展示了我们的预测的有效性，以及我们的方法在患者聚类的背景下的适用性。我们通过几种方法验证了我们的预测，包括模拟实验和互补的硅分析。我们发现大量的外显子具有预测基因的特定调节剪接因子基序，并且大量的出版物将这些基因与选择性剪接联系起来。此外，我们可以成功地利用剪接信息来聚类组织和患者。最后，我们发现了在标准化读取覆盖图和跨外显子-外显子连接的读取中许多预测基因剪接多样性的额外证据。
Motivation: Alternative splicing is central for cellular processes and substantially increases transcriptome and proteome diversity. Aberrant splicing events often have pathological consequences and are associated with various diseases and cancer types. The emergence of next-generation RNA sequencing (RNA-seq) provides an exciting new technology to analyse alternative splicing on a large scale. However, algorithms that enable the analysis of alternative splicing from short-read sequencing are not fully established yet and there are still no standard solutions available for a variety of data analysis tasks.Results: We present a new method and software to predict genes that are differentially spliced between two different conditions using RNA-seq data. Our method uses geometric angles between the high dimensional vectors of exon read counts. With this, differential splicing can be detected even if the splicing events are composed of higher complexity and involve previously unknown splicing patterns. We applied our approach to two case studies including neuroblastoma tumour data with favourable and unfavourable clinical courses. We show the validity of our predictions as well as the applicability of our method in the context of patient clustering. We verified our predictions by several methods including simulated experiments and complementary in silico analyses. We found a significant number of exons with specific regulatory splicing factor motifs for predicted genes and a substantial number of publications linking those genes to alternative splicing. Furthermore, we could successfully exploit splicing information to cluster tissues and patients. Finally, we found additional evidence of splicing diversity for many predicted genes in normalized read coverage plots and in reads that span exon-exon junctions.