多序列中的次线性时间模体发现
Sublinear Time Motif Discovery from Multiple Sequences
摘要：
一个自然的概率模型的基序发现已被用来实验测试的质量的基序发现程序。在该模型中，存在$k$背景序列，并且背景序列中的每个字符是来自字母表$Σ$的随机字符。基元$G=g_1g_2...g_m$是一串$m$字符。每个背景序列植入概率产生的$G$的近似拷贝。对于概率生成的$G$的近似副本$b_1b_2...b_m$，概率生成每个字符$b_i$，使得$b_i\neq g_i$的概率至多为$α$。我们开发了三种算法，在概率模型下，通过计算时间和突变概率之间的权衡，可以找到高概率的植入基序。本文提出的方法已在软件实现中得到应用。我们观察到一些令人鼓舞的结果，显示与其他软件相比，提高了性能的主题检测。
英文摘要：
A natural probabilistic model for motif discovery has been used to experimentally test the quality of motif discovery programs. In this model, there are $k$ background sequences, and each character in a background sequence is a random character from an alphabet $Σ$. A motif $G=g_1g_2...g_m$ is a string of $m$ characters. Each background sequence is implanted a probabilistically generated approximate copy of $G$. For a probabilistically generated approximate copy $b_1b_2...b_m$ of $G$, every character $b_i$ is probabilistically generated such that the probability for $b_i\neq g_i$ is at most $α$. We develop three algorithms that under the probabilistic model can find the implanted motif with high probability via a tradeoff between computational time and the probability of mutation. The methods developed in this paper have been used in the software implementation. We observed some encouraging results that show improved performance for motif detection compared with other softwares.
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。
