Mehlmann Martin, Dawson Erica D, Townsend Michael B, Smagala James A, Moore Chad L, Smith Catherine B, Cox Nancy J, Kuchta Robert D, Rowlen Kathy L
Department of Chemistry and Biochemistry, University of Colorado, UCB215, Boulder, CO 80303, USA.
J Clin Microbiol. 2006 Aug;44(8):2857-62. doi: 10.1128/JCM.00135-06.
DNA microarrays have proven to be powerful tools for gene expression analyses and are becoming increasingly attractive for diagnostic applications, e.g., for virus identification and subtyping. The selection of appropriate sequences for use on a microarray poses a challenge, particularly for highly mutable organisms such as influenza viruses, human immunodeficiency viruses, and hepatitis C viruses. The goal of this work was to develop an efficient method for mining large databases in order to identify regions of conservation in the influenza virus genome. From these regions of conservation, capture and label sequences capable of discriminating between different viral types and subtypes were selected. The salient features of the method were the use of phylogenetic trees for data reduction and the selection of a relatively small number of capture and label sequences capable of identifying a broad spectrum of influenza viruses. A detailed experimental evaluation of the selected sequences is described in a companion paper. The software is freely available under the General Public License at http://www.colorado.edu/chemistry/RGHP/software/.
DNA微阵列已被证明是用于基因表达分析的强大工具，并且在诊断应用中越来越具有吸引力，例如用于病毒鉴定和亚型分析。选择用于微阵列的合适序列是一项挑战，特别是对于诸如流感病毒、人类免疫缺陷病毒和丙型肝炎病毒等高度可变的生物体。这项工作的目标是开发一种有效的方法来挖掘大型数据库，以便识别流感病毒基因组中的保守区域。从这些保守区域中，选择能够区分不同病毒类型和亚型的捕获和标记序列。该方法的显著特点是使用系统发育树进行数据简化，并选择相对较少数量的能够识别广泛流感病毒谱的捕获和标记序列。在一篇配套论文中描述了对所选序列的详细实验评估。该软件可根据通用公共许可证在http://www.colorado.edu/chemistry/RGHP/software/免费获取。
