种子敏感性的统一框架及其在子集种子中的应用
A unifying framework for seed sensitivity and its application to subset seeds
摘要：
我们提出了一个通用的方法来计算种子的敏感性，可以应用到不同的定义的种子。它分别对待种子敏感性问题的三个组成部分-一组目标对齐，相关的概率分布，和种子模型-由不同的有限自动机指定。然后，该方法被应用到一个新的概念，子集种子，我们提出了一个有效的自动机建设。实验结果表明，敏感子集种子可以有效地使用我们的方法设计，然后可以用于相似性搜索产生更好的结果比普通的间隔种子。
英文摘要：
We propose a general approach to compute the seed sensitivity, that can be applied to different definitions of seeds. It treats separately three components of the seed sensitivity problem -- a set of target alignments, an associated probability distribution, and a seed model -- that are specified by distinct finite automata. The approach is then applied to a new concept of subset seeds for which we propose an efficient automaton construction. Experimental results confirm that sensitive subset seeds can be efficiently designed using our approach, and can then be used in similarity search producing better results than ordinary spaced seeds.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
