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We interpret G with each edge \(e \in E\) colored by read mappings as a flow network, considering the read volume assigned to every (super-) edge as a flux created by the expression of the underlying supporting transcripts T.Consequently, given an edge \(e=(tail,head,exonic,T)\) the contribution of the supporting transcripts \(\{t_1,\ldots,t_n\}\in T\) to the flux \(X_e\) observed along e can be described by a linear equation

\(\sum_{t_i\in T_e}(f_i t_i) \pm \Delta_e = X_e\)

(Equation 1)

where fi represents a factor that expresses the fraction of the respective transcript expression ti observed between taile and heade. In the trivial case, fi corresponds to the proportion of the interval [taile; heade] in comparison to the entire length of the processed transcript. The correction factor \(\Delta_e\) in Eq.1 is to compensate for divergence from the expectation created by stochastical sampling intrinsic to RNA-Seq experiments.

The crux of the flux is that an RNA-Seq experiment provides a series of observations on the underlying expression level ti along the transcript body. Following tradition in transportation problems, we model all of these observations as a system of linear equations by inferring Equation 1 on all \(e\in E\). Subsequently, the linear equations spanned by a locus are resolved by the objective function

\(min(\sum_e \Delta_e)\)

(Equation 2)

Solving the linear program (Eq.2) imposed by a locus intrinsically provides an estimate for the expression level ti of all alternative transcripts that are annotated.




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