5.1 Linear Program

We interpret G colored by read mappings to each edge  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, the contribution of each transcript  to the flux X_e observed along an edge  it is supporting can be described by a linear equation

where f_i represents a factor that expresses the fraction of the respective transcript expression t_i observed between tail_e and head_e. In the trivial case, f_i corresponds to the proportion of the interval [tail_e; head_e] in comparison to the entire length of the processed transcript. The correction factor  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 t_i along the transcript body. Following traditions in transportation problems, we model all of these observations as a system of linear equations by inferring Equation 1 on all . Subsequently, the linear equations spanned by a locus are resolved by the objective function

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