基于线性混合模型的基序发现Gibbs采样算法

Q2 Medicine

In Silico Biology Pub Date : 2010-02-15 DOI:10.1145/1722024.1722053

Daming Lu

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引用次数: 1

摘要

基因启动子中基序的鉴定是描述生物体遗传调控框架的关键步骤。本文提出了一种新的线性混合模型。该模型结合了传统的位置权重矩阵(PWM)模型和一种新的互信息(MI)模型。PWM可以包含单个位置频率，而MI可以反映位置之间的成对关系。通过一个训练阶段来确定每个模型的权重。然后，将这个训练好的模型嵌入到吉布斯采样算法中进行基序发现。利用该程序对一组DNA序列进行分析后，得到假定的基序，并与实验验证的基序以及其他流行的基序查找软件进行比较。结果表明，该混合模型在一定程度上提高了基序发现的精度。

本文章由计算机程序翻译，如有差异，请以英文原文为准。

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A Gibbs sampling algorithm for motif discovery using a linear mixed model

The identification of motifs in the gene promoters is a critical step in the delineation of the genetic regulatory framework of an organism. In this paper, a new linear mixed model is introduced. This model is a combination of the conventional Position Weight Matrix (PWM) model and a novel Mutual Information (MI) model. PWM can contain individual position frequencies whereas MI can reflect pair wise relation between positions. A training stage is carried out to determine the weight of each model. After that this trained model is embedded into a Gibbs sampling algorithm for motif discovery. After analyzing a set of DNA sequences using this program, putative motifs are gained and compared with experimental verified motifs as well as other popular motif finding software. Results show that this new mixed model can improve motif discovery accuracy to some extent.

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来源期刊

In Silico Biology Computer Science-Computational Theory and Mathematics

CiteScore

2.20

自引率

0.00%

发文量

期刊介绍： The considerable "algorithmic complexity" of biological systems requires a huge amount of detailed information for their complete description. Although far from being complete, the overwhelming quantity of small pieces of information gathered for all kind of biological systems at the molecular and cellular level requires computational tools to be adequately stored and interpreted. Interpretation of data means to abstract them as much as allowed to provide a systematic, an integrative view of biology. Most of the presently available scientific journals focus either on accumulating more data from elaborate experimental approaches, or on presenting new algorithms for the interpretation of these data. Both approaches are meritorious.