IGLOSS：迭代无间隙局部相似性搜索
IGLOSS: iterative gapless local similarity search
摘要：
寻找局部序列模式是生物信息学的基本任务之一。序列模式可能具有结构，功能或其他相关性，并且已经开发了许多方法来检测和分析它们。这些方法往往依赖于已经收集的大量信息。新的可用序列的数量爆炸要求新的方法来探索局部序列相似性。我们已经开发了一个高灵敏度的基于Web的迭代本地相似性扫描仪，发现序列模式类似于提交的查询。
英文摘要：
Searching for local sequence patterns is one of the basic tasks in bioinformatics. Sequence patterns might have structural, functional or some other relevance, and numerous methods have been developed to detect and analyze them. These methods often depend on the wealth of information already collected. The explosion in the number of newly available sequences calls for novel methods to explore local sequence similarity. We have developed a high sensitivity web-based iterative local similarity scanner, that finds sequence patterns similar to a submitted query.
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。
