| 摘要: |
| 基因的差异化表达由多种因素共同导致,并且与许多疾病的发生和发展有密切联系,对差异化表达的基因进行生物信息学以及生物统计学的分析对于研究细胞调节机制和疾病机理有着重要意义。目前,对差异化表达的基因有以下几种主流的研究方法:DNA微阵列 (DNA microarray), 抑制性消减杂交 (SSH),基因表达连续性分析 (SAGE),代表性差异分析 (RDA),以及mRNA差异显示PCR (mRNA DDRT-PCR)。目前许多基因差异化表达数据是建立在时段(time series)基础上,因此对基于时间变化的基因差异化表达分析变得尤为重要。本文将对差异化表达基因的几种主流方法进行详细阐述,并介绍一种基于傅里叶函数的时段基因差异化表达分析。 |
| 关键词: 生物信息学 基因 差异化表达 时段 |
| DOI：10.3969/j.issn.1672-5565.2014-02.20140210 |
| 分类号:Q344+.13 |
| 基金项目: |
|
| Bioinformatics analysis for gene differential expression |
|
LU Ting
|
|
(Institute of Hydrobiology, Chinese Academy of Sciences, Wuhan 430072, China)
|
| Abstract: |
| Gene differential expression can be caused by multiple factors, and related with genesis and development of many diseases. Bioinformatics and biostatistics analysis for gene differential expression are widely used for studying cellular regulation mechanism and disease mechanism. Currently, there are several main methods for studying gene differential expression, DNA microarray, suppression subtractive hybridization (SSH), serial analysis of gene expression (SAGE), representational difference analysis (RDA), and mRNA differential display PCR (mRNA DDRT-PCR). Nowadays, much gene differential expression data is time series based, therefore the analysis for time series based gene differential expression data is critical. This review will elucidate several main methods for gene differential expression, and introduce a Fourier algorithm-based bioinformatics analysis for time-series gene differential expression. |
| Key words: Bioinformatics Gene Differential expression Time-series |
