Department of Physiology, College of Oriental Medicine, KyungHee University, #1 Hoegi-Dong, Dongdaemun-gu, Seoul 130-701, South Korea.
BMC Bioinformatics. 2009 Dec 16;10:424. doi: 10.1186/1471-2105-10-424.
Genomic alterations frequently occur in many cancer patients and play important mechanistic roles in the pathogenesis of cancer. Furthermore, they can modify the expression level of genes due to altered copy number in the corresponding region of the chromosome. An accumulating body of evidence supports the possibility that strong genome-wide correlation exists between DNA content and gene expression. Therefore, more comprehensive analysis is needed to quantify the relationship between genomic alteration and gene expression. A well-designed bioinformatics tool is essential to perform this kind of integrative analysis. A few programs have already been introduced for integrative analysis. However, there are many limitations in their performance of comprehensive integrated analysis using published software because of limitations in implemented algorithms and visualization modules.
To address this issue, we have implemented the Java-based program CHESS to allow integrative analysis of two experimental data sets: genomic alteration and genome-wide expression profile. CHESS is composed of a genomic alteration analysis module and an integrative analysis module. The genomic alteration analysis module detects genomic alteration by applying a threshold based method or SW-ARRAY algorithm and investigates whether the detected alteration is phenotype specific or not. On the other hand, the integrative analysis module measures the genomic alteration's influence on gene expression. It is divided into two separate parts. The first part calculates overall correlation between comparative genomic hybridization ratio and gene expression level by applying following three statistical methods: simple linear regression, Spearman rank correlation and Pearson's correlation. In the second part, CHESS detects the genes that are differentially expressed according to the genomic alteration pattern with three alternative statistical approaches: Student's t-test, Fisher's exact test and Chi square test. By successive operations of two modules, users can clarify how gene expression levels are affected by the phenotype specific genomic alterations. As CHESS was developed in both Java application and web environments, it can be run on a web browser or a local machine. It also supports all experimental platforms if a properly formatted text file is provided to include the chromosomal position of probes and their gene identifiers.
CHESS is a user-friendly tool for investigating disease specific genomic alterations and quantitative relationships between those genomic alterations and genome-wide gene expression profiling.
基因组改变在许多癌症患者中经常发生，在癌症的发病机制中起着重要的机制作用。此外，由于染色体相应区域的拷贝数改变，它们可以改变基因的表达水平。越来越多的证据支持这样一种可能性，即 DNA 含量和基因表达之间存在强大的全基因组相关性。因此，需要更全面的分析来量化基因组改变与基因表达之间的关系。一个精心设计的生物信息学工具对于进行这种综合分析是必不可少的。已经介绍了一些用于综合分析的程序。然而，由于所实现的算法和可视化模块的限制，使用已发布的软件进行全面综合分析时，它们的性能存在许多限制。
为了解决这个问题，我们实现了基于 Java 的程序 CHESS，以允许对两个实验数据集进行综合分析：基因组改变和全基因组表达谱。CHESS 由基因组改变分析模块和综合分析模块组成。基因组改变分析模块通过应用基于阈值的方法或 SW-ARRAY 算法来检测基因组改变，并研究检测到的改变是否是表型特异性的。另一方面，综合分析模块测量基因组改变对基因表达的影响。它分为两个独立的部分。第一部分通过应用以下三种统计方法：简单线性回归、Spearman 秩相关和 Pearson 相关，计算比较基因组杂交比与基因表达水平之间的总体相关性。在第二部分，CHESS 用三种替代的统计方法根据基因组改变模式检测差异表达的基因：学生 t 检验、Fisher 精确检验和卡方检验。通过两个模块的连续操作，用户可以阐明基因表达水平如何受到表型特异性基因组改变的影响。由于 CHESS 是在 Java 应用程序和网络环境中开发的，因此它可以在网络浏览器或本地机器上运行。如果提供了格式正确的文本文件来包含探针的染色体位置及其基因标识符，它也支持所有实验平台。
CHESS 是一种用于研究疾病特异性基因组改变以及这些基因组改变与全基因组基因表达谱之间定量关系的用户友好工具。
