Safety and Completeness in Flow Decompositions for RNA Assembly

TL;DR

This paper introduces a local characterization and practical algorithm for identifying safe paths in flow decompositions of DAGs, significantly improving accuracy and efficiency in RNA transcript analysis.

Contribution

It provides the first local characterization of safe paths in flow decompositions, enabling a practical algorithm that outperforms existing methods in accuracy and computational efficiency.

Findings

Our algorithm reports approximately 50% more coverage than trivial safe algorithms.

It maintains perfect precision while significantly increasing coverage.

The algorithm is 3-5 times faster and uses less space than the greedy-width heuristic.

Abstract

Decomposing a network flow into weighted paths has numerous applications. Some applications require any decomposition that is optimal w.r.t. some property such as number of paths, robustness, or length. Many bioinformatic applications require a specific decomposition where the paths correspond to some underlying data that generated the flow. For real inputs, no optimization criteria guarantees to uniquely identify the correct decomposition. Therefore, we propose to report safe paths, i.e., subpaths of at least one path in every flow decomposition. Ma, Zheng, and Kingsford [WABI 2020] addressed the existence of multiple optimal solutions in a probabilistic framework, i.e., non-identifiability. Later [RECOMB 2021], they gave a quadratic-time algorithm based on a global criterion for solving a problem called AND-Quant, which generalizes the problem of reporting whether a given path is safe. We give the first local characterization of safe paths for flow decompositions in directed acyclic graphs (DAGs), leading to a practical algorithm for finding the complete set of safe paths. We evaluated our algorithms against the trivial safe algorithms (unitigs, extended unitigs) and the popularly used heuristic (greedy-width) for flow decomposition on RNA transcripts datasets. Despite maintaining perfect precision our algorithm reports significantly higher coverage ($\approx 50\%$ more) than trivial safe algorithms. The greedy-width algorithm though reporting a better coverage, has significantly lower precision on complex graphs. Overall, our algorithm outperforms (by $\approx 20\%$) greedy-width on a unified metric (F-Score) when the dataset has significant number of complex graphs. Moreover, it has superior time ($3-5\times$) and space efficiency ($1.2-2.2\times$), resulting in a better and more practical approach for bioinformatics applications of flow decomposition.