Calling fusion genes from RNA-seq data is well established, but other transcriptional variants are difficult to detect using existing approaches. To identify all types of variants in transcriptomes we developed MINTIE, an integrated pipeline for RNA-seq data. We take a reference-free approach, combining de novo assembly of transcripts with differential expression analysis to identify up-regulated novel variants in a case sample. We compare MINTIE with eight other approaches, detecting > 85% of variants while no other method is able to achieve this. We posit that MINTIE will be able to identify new disease variants across a range of disease types.

中文翻译：

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MINTIE：使用 RNA-seq 数据识别转录组中的新结构和剪接变体

从 RNA-seq 数据中调用融合基因已经很成熟，但使用现有方法很难检测到其他转录变体。为了识别转录组中所有类型的变体，我们开发了 MINTIE，这是一个用于 RNA-seq 数据的集成管道。我们采用无参考方法，将转录本的从头组装与差异表达分析相结合，以识别病例样本中上调的新变体。我们将 MINTIE 与其他八种方法进行比较，检测到 > 85% 的变体，而没有其他方法能够实现这一点。我们假设 MINTIE 将能够识别一系列疾病类型的新疾病变体。