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统计框架,以确定内部长度分布的统计框架.

Elya Wygoda1, Gil Loewenthal1, Asher Moshe1

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概括
此摘要是机器生成的。

对进化研究来说,准确地推断内长分布对于进化研究至关重要. 一个Zipf分布最适合大多数对齐,优于传统的差距计数方法.

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科学领域:

  • 分子进化分子进化
  • 生物信息学是一种生物信息学.
  • 计算生物学 计算生物学

背景情况:

  • 插入和删除 (indels) 是经常发生的分子进化事件,影响宏观进化.
  • 对于进化应用,特别是对齐软件,准确的内长分布推断至关重要.
  • 使用差距计数的先前方法在统计上是有限的,并且有对齐偏见.

研究的目的:

  • 严格确定最适合对齐的内接长度分布.
  • 开发统计学上可靠的方法来进行内部模型选择.
  • 评估当前的indel模型在代表进化过程中的充分性.

主要方法:

  • 使用机器学习算法减少了对齐偏差.
  • 应用近似贝叶斯计算 (ABC) 用于严格的模型选择.
  • 开发了一种新的方法来测试现有的indel模型的合适性.

主要成果:

  • 最优的内角长度分布模型在不同的对齐中不同.
  • 一个Zipf长度分布被发现是最适合大多数对齐分析.
  • 开发的方法为indel模型选择提供了统计学上严格的方法.

结论:

  • 齐普夫分布是一个优秀的模型,用于表征许多对齐中的内角长度.
  • 这项工作为分析某些进化过程提供了更好的统计学严谨性.
  • 这些发现对开发更准确的生物信息学调整软件有重大影响.