Cedano Juan, Huerta Mario, Querol Enrique
Departament de Bioquímica i Biología Molecular, Institut de Biotecnologia i Biomedicina, Universitat Autònoma de Barcelona, 08193 Bellaterra, Barcelona, Spain.
Adv Bioinformatics. 2008;2008:789026. doi: 10.1155/2008/789026. Epub 2008 Dec 10.
Background. Microarray technology is so expensive and powerful that it is essential to extract maximum value from microarray data. Our tools allow researchers to test and formulate from a hypothesis to entire models. Results. The objective of the NCRPCOPGene is to study the relationships among gene expressions under different conditions, to classify these conditions, and to study their effect on the different relationships. The web application makes it easier to define the sample classes, grouping the microarray experiments either by using (a) biological, statistical, or any other previous knowledge or (b) their effect on the expression relationship maintained among specific genes of interest. By means of the type (a) class definition, the researcher can add biological information to the gene-expression relationships. The type (b) class definition allows for linking genes correlated neither linearly nor nonlinearly. Conclusions. The PCOPGene tools are especially suitable for microarrays with large sample series. This application helps to identify cellular states and the genes involved in it in a flexible way. The application takes advantage of the ability of our system to relate gene expressions; even when these relationships are noncontinuous and cannot be found using linear or nonlinear analytical methods.
背景。微阵列技术既昂贵又强大,因此从微阵列数据中提取最大价值至关重要。我们的工具使研究人员能够从假设到整个模型进行测试和构建。结果。NCRPCOPGene的目标是研究不同条件下基因表达之间的关系,对这些条件进行分类,并研究它们对不同关系的影响。该网络应用程序使定义样本类别变得更加容易,可通过以下两种方式对微阵列实验进行分组:(a)利用生物学、统计学或任何其他先前的知识,或(b)根据它们对感兴趣的特定基因之间维持的表达关系的影响。通过(a)类定义,研究人员可以将生物学信息添加到基因表达关系中。(b)类定义允许将既不线性相关也不非线性相关的基因联系起来。结论。PCOPGene工具特别适用于具有大量样本系列的微阵列。该应用程序有助于以灵活的方式识别细胞状态及其相关基因。该应用程序利用了我们系统关联基因表达的能力;即使这些关系是不连续的,并且无法使用线性或非线性分析方法找到。