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Dissect: detection and characterization of novel structural alterations in transcribed sequences

机译:剖析:检测和表征转录序列中的新结构改变

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Motivation: Computational identification of genomic structural variants via high-throughput sequencing is an important problem for which a number of highly sophisticated solutions have been recently developed. With the advent of high-throughput transcriptome sequencing (RNA-Seq), the problem of identifying structural alterations in the transcriptome is now attracting significant attention. In this article, we introduce two novel algorithmic formulations for identifying transcriptomic structural variants through aligning transcripts to the reference genome under the consideration of such variation. The first formulation is based on a nucleotide-level alignment model; a second, potentially faster formulation is based on chaining fragments shared between each transcript and the reference genome. Based on these formulations, we introduce a novel transcriptome-to-genome alignment tool, Dissect (DIScovery of Structural Alteration Event Containing Transcripts), which can identify and characterize transcriptomic events such as duplications, inversions, rearrangements and fusions. Dissect is suitable for whole transcriptome structural variation discovery problems involving sufficiently long reads or accurately assembled contigs. In this article, we introduce two novel algorithmic formulations for identifying transcriptomic structural variants through aligning transcripts to the reference genome under the consideration of such variation. The first formulation is based on a nucleotide-level alignment model; a second, potentially faster formulation is based on chaining fragments shared between each transcript and the reference genome. Based on these formulations, we introduce a novel transcriptome-to-genome alignment tool, Dissect (DIScovery of Structural Alteration Event Containing Transcripts), which can identify and characterize transcriptomic events such as duplications, inversions, rearrangements and fusions. Dissect is suitable for whole transcriptome structural variation discovery problems involving sufficiently long reads or accurately assembled contigs. Results: We tested Dissect on simulated transcripts altered via structural events, as well as assembled RNA-Seq contigs from human prostate cancer cell line C4-2. Our results indicate that Dissect has high sensitivity and specificity in identifying structural alteration events in simulated transcripts as well as uncovering novel structural alterations in cancer transcriptomes.
机译:动机:通过高通量测序对基因组结构变异进行计算鉴定是一个重要的问题,最近已开发出许多高度复杂的解决方案。随着高通量转录组测序(RNA-Seq)的出现,鉴定转录组中结构改变的问题现在引起了广泛关注。在本文中,我们介绍了两种新颖的算法公式,通过在考虑这种变异的情况下将转录物与参考基因组比对来鉴定转录组结构变异。第一种配方基于核苷酸水平的比对模型;第二种可能更快的配方是基于每个转录物和参考基因组之间共享的链片段。基于这些公式,我们介绍了一种新颖的转录组与基因组的比对工具,Dissect(发现包含转录本的结构变异事件),可以识别和表征转录组事件,例如重复,倒位,重排和融合。 Dissect适用于涉及足够长的阅读时间或准确组装的重叠群的整个转录组结构变异发现问题。在本文中,我们介绍了两种新颖的算法公式,通过在考虑这种变异的情况下将转录物与参考基因组比对来鉴定转录组结构变异。第一种配方基于核苷酸水平的比对模型;第二种可能更快的配方是基于每个转录物和参考基因组之间共享的链片段。基于这些公式,我们介绍了一种新颖的转录组与基因组的比对工具,Dissect(发现包含转录本的结构变异事件),可以识别和表征转录组事件,例如重复,倒位,重排和融合。 Dissect适用于涉及足够长的阅读时间或准确组装的重叠群的整个转录组结构变异发现问题。结果:我们测试了Dissect在通过结构事件以及人类前列腺癌细胞C4-2组装的RNA-Seq重叠群改变的模拟转录本上的表达。我们的结果表明,Dissect在识别模拟转录本中的结构改变事件以及发现癌症转录组中的新型结构改变方面具有很高的敏感性和特异性。

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