The probability of sequencing a set of RNA-seq reads can be directly modeled using the abundances of splice junctions in splice graphs instead of the abundances of a list of transcripts. We call this model graph quantification, which was first proposed by Bernard et al. (Bioinformatics 30:2447–55, 2014). The model can be viewed as a generalization of transcript expression quantification where every full path in the splice graph is a possible transcript. However, the previous graph quantification model assumes the length of single-end reads or paired-end fragments is fixed. We provide an improvement of this model to handle variable-length reads or fragments and incorporate bias correction. We prove that our model is equivalent to running a transcript quantifier with exactly the set of all compatible transcripts. The key to our method is constructing an extension of the splice graph based on Aho-Corasick automata. The proof of equivalence is based on a novel reparameterization of the read generation model of a state-of-art transcript quantification method. We propose a new approach for graph quantification, which is useful for modeling scenarios where reference transcriptome is incomplete or not available and can be further used in transcriptome assembly or alternative splicing analysis.

中文翻译：

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拼接图上的精确转录量化

可以使用剪接图中剪接点的丰度而不是转录物列表的丰度来直接模拟对一组 RNA-seq 读数进行测序的概率。我们将这种模型称为图量化，这是由 Bernard 等人首先提出的。(生物信息学 30:2447–55, 2014)。该模型可以被视为转录表达量化的概括，其中拼接图中的每个完整路径都是可能的转录本。然而，之前的图量化模型假设单端读取或双端片段的长度是固定的。我们对该模型进行了改进，以处理可变长度的读取或片段并结合偏差校正。我们证明我们的模型等价于运行一个带有所有兼容转录本集的转录本量词。我们方法的关键是构建基于 Aho-Corasick 自动机的拼接图的扩展。等效性证明基于对最先进的转录本量化方法的读取生成模型的新的重新参数化。我们提出了一种用于图量化的新方法，该方法可用于对参考转录组不完整或不可用的情况进行建模，并可进一步用于转录组组装或可变剪接分析。