Explaining a Weighted DAG with Few Paths for Solving Genome-Guided Multi-Assembly

RNA-Seq technology offers new high-throughput ways for transcript identification and quantification based on short reads, and has recently attracted great interest. This is achieved by constructing a weighted DAG whose vertices stand for exons, and whose arcs stand for split alignments of the RNA-Seq reads to the exons. The task consists of finding a number of paths, together with their expression levels, which optimally explain the weights of the graph under various fitting functions, such as least sum of squared residuals. In (Tomescu et al. BMC Bioinformatics, 2013) we studied this genome-guided multi-assembly problem when the number of allowed solution paths was linear in the number of arcs. In this paper, we further refine this problem by asking for a bounded number $k$ of solution paths, which is the setting of most practical interest. We formulate this problem in very broad terms, and show that for many choices of the fitting function it becomes NP-hard. Nevertheless, we identify a natural graph parameter of a DAG $G$ , which we call arc-width and denote $langle Grangle$ , and give a dynamic programming algorithm running in time $O(W^klangle Grangle ^k(langle Grangle + k)n)$ , where $n$ is the number of vertices and $W$ is the maximum weight of $G$ . This implies that the problem is fixed-parameter tractable (FPT) in the parameters $W$ , $langle Grangle$ , and $k$ . We also show that the arc-width of DAGs constructed from simulated and real RNA-Seq reads is small in practice. Finally, we study the approximability of this problem, and, in particular, give a fully polynomial-time approximation scheme (FPTAS) for the case when the fitting function penalizes the maximum ratio between the weights of the arcs and their predicted coverage.