Fast, Flexible, and Exact Minimum Flow Decompositions via ILP

TL;DR

This paper introduces the first practical ILP-based method for exact minimum flow decomposition, enabling fast solutions for large instances and adaptable to various practical bioinformatics variants.

Contribution

It presents a novel ILP formulation for MFD that encodes all solution paths with quadratic variables, achieving scalability and adaptability for practical applications.

Findings

Solves large instances in under 13 seconds

Easily adapts to practical variants like longer reads

Provides exact solutions for complex flow graphs

Abstract

Minimum flow decomposition (MFD) (the problem of finding a minimum set of paths that perfectly decomposes a flow) is a classical problem in Computer Science, and variants of it are powerful models in multiassembly problems in Bioinformatics (e.g. RNA assembly). However, because this problem and its variants are NP-hard, practical multiassembly tools either use heuristics or solve simpler, polynomial-time solvable versions of the problem, which may yield solutions that are not mini-mal or do not perfectly decompose the flow. Many RNA assemblers also use integer linear programming(ILP) formulations of such practical variants, having the major limitation they need to encode all the potentially exponentially many solution paths. Moreover, the only exact solver for MFD does not scale to large instances and cannot be efficiently generalized to practical MFD variants. In this work, we provide the first practical ILP formulation for MFD (and thus the first fast and exact solver for MFD), based on encoding all of the exponentially many solution paths using only a quadratic number of variables. On both simulated and real flow graphs, our approach solves any instance in under 13 seconds. We also show that our ILP formulation can be easily and efficiently adapted for many practical variants, such as incorporating longer or paired-end reads or minimizing flow errors. We hope that our results can remove the current tradeoff between the complexity of a multi assembly model and its tractability and can lie at the core of future practical RNA assembly tools.