隐马尔可夫模型在基因序列分类中的应用--对布氏锥虫基因组中VSG基因的分类
Hidden Markov Models for Gene Sequence Classification: Classifying the VSG genes in the Trypanosoma brucei Genome
摘要：
本文提出了一种隐马尔可夫模型（HMM）的模式识别基因组序列的应用。应用隐马尔可夫模型对布氏锥虫基因组中编码变异表面糖蛋白（VSG）的基因进行了识别。brucei）和其他非洲锥虫。它们是寄生原生动物，可引起家畜和野生动物的昏睡病和几种疾病。这些寄生虫有一种独特的策略来逃避宿主的免疫系统，包括周期性地改变其主要的细胞表面蛋白（VSG）。使用模式识别方法而不是传统的基于同源性的方法来鉴定这些基因的动机在于，这些基因之间的序列同一性（氨基酸和DNA序列）的水平通常低于这些方法中被认为可靠的水平。在模式识别方法中，隐马尔可夫模型特别适合解决这个问题，因为它们可以更自然地处理基因边缘的确定。我们使用马尔可夫模型中不同数量的状态以及几个性能指标来评估模型的性能。该模型使用公共基因组数据进行应用。我们的实验结果表明，T。使用HMM可以安全地鉴定布鲁氏菌（高灵敏度和低假阳性率）。
英文摘要：
The article presents an application of Hidden Markov Models (HMMs) for pattern recognition on genome sequences. We apply HMM for identifying genes encoding the Variant Surface Glycoprotein (VSG) in the genomes of Trypanosoma brucei (T. brucei) and other African trypanosomes. These are parasitic protozoa causative agents of sleeping sickness and several diseases in domestic and wild animals. These parasites have a peculiar strategy to evade the host's immune system that consists in periodically changing their predominant cellular surface protein (VSG). The motivation for using patterns recognition methods to identify these genes, instead of traditional homology based ones, is that the levels of sequence identity (amino acid and DNA sequence) amongst these genes is often below of what is considered reliable in these methods. Among pattern recognition approaches, HMM are particularly suitable to tackle this problem because they can handle more naturally the determination of gene edges. We evaluate the performance of the model using different number of states in the Markov model, as well as several performance metrics. The model is applied using public genomic data. Our empirical results show that the VSG genes on T. brucei can be safely identified (high sensitivity and low rate of false positives) using HMM.
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。
