A pangenome graph can serve as a better reference for genomic studies because it allows a compact representation of multiple genomes within a species. Aligning sequences to a graph is critical for pangenome-based resequencing. The seed-chain-extend heuristic works by finding short exact matches between a sequence and a graph. In this heuristic, colinear chaining helps identify a good cluster of exact matches that can be combined to form an alignment. Colinear chaining algorithms have been extensively studied for aligning two sequences with various gap costs, including linear, concave, and convex cost functions. However, extending these algorithms for sequence-to-graph alignment presents significant challenges. Recently, Makinen et al. introduced a sparse dynamic programming framework that exploits the small path cover property of acyclic pangenome graphs, enabling efficient chaining. However, this framework does not consider gap costs, limiting its practical effectiveness. We address this limitation by developing novel problem formulations and provably good chaining algorithms that support a variety of gap cost functions. These functions are carefully designed to enable fast chaining algorithms whose time requirements are parameterized in terms of the size of the minimum path cover. Through an empirical evaluation, we demonstrate the superior performance of our algorithm compared with existing aligners. When mapping simulated long reads to a pangenome graph comprising 95 human haplotypes, we achieved 98.7% precision while leaving <2% of reads unmapped.

中文翻译：

---

非循环泛基因组图的间隙敏感共线链接算法。

全基因组图可以作为基因组研究的更好参考，因为它可以紧凑地表示一个物种内的多个基因组。将序列与图表对齐对于基于泛基因组的重测序至关重要。种子链扩展启发式的工作原理是在序列和图形之间查找短的精确匹配。在这种启发式中，共线链接有助于识别一组良好的精确匹配，这些匹配可以组合起来形成对齐。共线链接算法已被广泛研究，用于以各种间隙成本（包括线性、凹和凸成本函数）对齐两个序列。然而，扩展这些算法以进行序列到图的对齐提出了重大挑战。最近，Makinen 等人。引入了稀疏动态编程框架，该框架利用非循环泛基因组图的小路径覆盖特性，实现高效的链接。然而，该框架没有考虑缺口成本，限制了其实际有效性。我们通过开发新颖的问题公式和可证明良好的支持各种间隙成本函数的链接算法来解决这一限制。这些函数经过精心设计，可实现快速链接算法，其时间要求根据最小路径覆盖的大小进行参数化。通过实证评估，我们证明了我们的算法与现有对准器相比的优越性能。当将模拟的长读长映射到包含 95 个人类单倍型的泛基因组图时，我们实现了 98.7% 的精度，同时留下 <2% 的读长未映射。