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Biological Sequence Analysis

机译:生物序列分析

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摘要

This talk will review a little over a decade's research on applying certain stochastic models to biological sequence analysis. The models themselves have a longer history, going back over 30 years, although many novel variants have arisen since that time.The function of the models in biological sequence analysis is to summarize the information concerning what is known as a motif or a domain in bioinformatics, and to provide a tool for discovering instances of that motif or domain in a separate sequence segment. We will introduce the motif models in stages, beginning from very simple, non-stochastic versions, progressively becoming more complex, until we reach modern profile HMMs for motifs. A second example will come from gene finding using sequence data from one or two species, where generalized HMMs or generalized pair HMMs have proved to be very effective.
机译:这次谈判将在十年内审查关于将某些随机模型应用于生物序列分析的研究。模型本身具有更长的历史,返回超过30年的历史,尽管从那时起出现了许多新的变种。生物序列分析中的模型的功能总结了关于在生物信息学中所谓的基序或域名的信息,并提供用于在单独的序列段中发现该图案或域的实例的工具。我们将以阶段介绍主题模型,从非常简单,非随机版本开始,逐渐变得更加复杂,直到我们到达Motifs的现代简介HMMS。第二个例子将来自使用来自一个或两个物种的序列数据的基因发现,其中总而言之的HMM或广义对HMMS已经证明非常有效。

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