PDF(Pubmed)
Abstract：
Chromosomal fusion is a significant form of structural variation, but research into algorithms for its identification has been limited. Most existing methods rely on synteny analysis, which necessitates manual annotations and always involves inefficient sequence alignments. In this paper, we present a novel alignment-free algorithm for chromosomal fusion recognition. Our method transforms the problem into a series of assignment problems using natural vectors and efficiently solves them with the Kuhn-Munkres algorithm. When applied to the human/gorilla and swamp buffalo/river buffalo datasets, our algorithm successfully and efficiently identifies chromosomal fusion events. Notably, our approach offers several advantages, including higher processing speeds by eliminating time-consuming alignments and removing the need for manual annotations. By an alignment-free perspective, our algorithm initially considers entire chromosomes instead of fragments to identify chromosomal structural variations, offering substantial potential to advance research in this field.
摘要：
染色体融合是结构变异的重要形式,但是对其识别算法的研究一直很有限。大多数现有的方法都依赖于同义分析，这需要手动注释，并且总是涉及低效的序列比对。在本文中,我们提出了一种新的染色体融合识别算法。我们的方法使用自然向量将问题转换为一系列分配问题，并使用Kuhn-Munkres算法有效地解决了这些问题。当应用于人类/大猩猩和沼泽水牛/河水牛数据集时，我们的算法成功和有效地识别染色体融合事件。值得注意的是,我们的方法提供了几个优点，通过消除耗时的对齐和消除对手动注释的需要，包括更高的处理速度。通过无对齐的视角，我们的算法最初考虑整个染色体而不是片段来识别染色体结构变异，为推进这一领域的研究提供了巨大的潜力。
