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Information-optimal genome assembly via sparse read-overlap graphs

机译:通过稀疏读取重叠图实现信息优化的基因组组装

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Motivation: In the context of third-generation long-read sequencing technologies, read-overlap-based approaches are expected to play a central role in the assembly step. A fundamental challenge in assembling from a read-overlap graph is that the true sequence corresponds to a Hamiltonian path on the graph, and, under most formulations, the assembly problem becomes NP-hard, restricting practical approaches to heuristics. In this work, we avoid this seemingly fundamental barrier by first setting the computational complexity issue aside, and seeking an algorithm that targets information limits. In particular, we consider a basic feasibility question: when does the set of reads contain enough information to allow unambiguous reconstruction of the true sequence?
机译:动机:在第三代长读取测序技术的背景下,基于读取重叠的方法有望在组装步骤中发挥核心作用。从读取重叠图进行组装的一个基本挑战是,真实序列与图中的哈密顿路径相对应,并且在大多数形式下,组装问题变得很困难,从而限制了启发式方法的实用方法。在这项工作中,我们首先将计算复杂性问题放在一边,然后寻找针对信息限制的算法,从而避免了看似基本的障碍。特别地,我们考虑一个基本的可行性问题:读取的集合何时包含足够的信息以允许对真实序列进行明确的重建?

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