Abstract
BackgroundThe 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.ResultsWe 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.ConclusionWe 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.
Highlights
The probability of sequencing a set of RNA sequencing (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 prove that optimizing a network flow on the prefix graph is equivalent to optimizing abundances of reference transcripts using the state-of-the-art transcript expression quantification formulation when all full paths of splice graphs are provided as reference transcripts, assuming modeled biases of generating a fragment are determined by the fragment sequence itself regardless of which transcript it is from
The key algorithmic contributions are a provably correct reparameterization process and the introduction of the prefix graph inspired by AhoCorasick automata for inference
Summary
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 previous graph quantification model assumes the length of single-end reads or paired-end fragments is fixed. FlipFlop infers network flow on its extension of splice graphs, called fragment graphs, and uses the model to further assemble transcripts. The proposed fragment graph model only retains its theoretical guarantee when the lengths of single-end reads or paired-end fragments are fixed. Our method is based on flow inference on a different extension of the splice graph
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