VIPR HMM: a hidden Markov model for detecting recombination with microbial detection microarrays.
VIPR HMM: a hidden Markov model for detecting recombination with microbial detection microarrays.
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VIPR HMM：一种隐马尔可夫模型，用于通过微生物检测微阵列检测重组。
DOI：
10.1093/bioinformatics/bts560
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发表时间：
2012
期刊：
影响因子：
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通讯作者：
Wang,David
中科院分区：
文献类型：
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作者：
Allred,AdamF;Renshaw,Hilary;Weaver,Scott;Tesh,RobertB;Wang,David
动机：目前诊断微生物学的方法通常集中在检测候选药物中的单个基因组位点或蛋白质。然后从该分离的结果推断整个微生物的存在。问题是，微生物基因组中重组的存在将无法检测到，除非对其他基因组位点或蛋白质组分进行特异性测定。微阵列很好地适用于检测来自给定微生物的多个基因座;此外，微阵列的固有性质有利于高度平行地询问多种微生物。然而，用于分析诊断微阵列数据的现有方法中没有一种具有特异性鉴定重组微生物的能力。在以前的工作中，我们开发了一种新的算法，VIPR，用于分析诊断微阵列data.Results：我们已经扩大了我们以前的实施VIPR，将隐马尔可夫模型（HMM）检测重组基因组。我们在一组非重组亲本病毒上训练我们的HMM，并将我们的方法应用于与诊断微阵列杂交的11种重组甲病毒和4种重组黄病毒，以评估HMM的性能。VIPR HMM正确识别了验证集中62个种间重组断点的95%，仅预测了两个假阳性断点。这项研究是第一次描述和验证的算法能够检测重组病毒的诊断微阵列杂交patterns.Availability的基础上：VIPR HMM是免费提供的学术用途，可以从http://ibridgenetwork.org/wustl/vipr.Contact:davewang@borcim.wustl.eduSupplementary下载信息：补充数据可在Bioinformaticsonline。
Motivation:Current methods in diagnostic microbiology typically focus on the detection of a single genomic locus or protein in a candidate agent. The presence of the entire microbe is then inferred from this isolated result. Problematically, the presence of recombination in microbial genomes would go undetected unless other genomic loci or protein components were specifically assayed. Microarrays lend themselves well to the detection of multiple loci from a given microbe; furthermore, the inherent nature of microarrays facilitates highly parallel interrogation of multiple microbes. However, none of the existing methods for analyzing diagnostic microarray data has the capacity to specifically identify recombinant microbes. In previous work, we developed a novel algorithm, VIPR, for analyzing diagnostic microarray data.Results:We have expanded upon our previous implementation of VIPR by incorporating a hidden Markov model (HMM) to detect recombinant genomes. We trained our HMM on a set of non-recombinant parental viruses and applied our method to 11 recombinant alphaviruses and 4 recombinant flaviviruses hybridized to a diagnostic microarray in order to evaluate performance of the HMM. VIPR HMM correctly identified 95% of the 62 inter-species recombination breakpoints in the validation set and only two false-positive breakpoints were predicted. This study represents the first description and validation of an algorithm capable of detecting recombinant viruses based on diagnostic microarray hybridization patterns.Availability:VIPR HMM is freely available for academic use and can be downloaded from http://ibridgenetwork.org/wustl/vipr.Contact:davewang@borcim.wustl.eduSupplementary information:Supplementary data are available atBioinformaticsonline.