Department of Biochemistry, McGill University, Montreal, Quebec, Canada.
Bioinformatics. 2011 May 15;27(10):1440-1. doi: 10.1093/bioinformatics/btr146. Epub 2011 Mar 21.
Translational control of gene expression has emerged as a major mechanism that regulates many biological processes and shows dysregulation in human diseases including cancer. When studying differential translation, levels of both actively translating mRNAs and total cytosolic mRNAs are obtained where the latter is used to correct for a possible contribution of differential cytosolic mRNA levels to the observed differential levels of actively translated mRNAs. We have recently shown that analysis of partial variance (APV) corrects for cytosolic mRNA levels more effectively than the commonly applied log ratio approach. APV provides a high degree of specificity and sensitivity for detecting biologically meaningful translation changes, especially when combined with a variance shrinkage method for estimating random error. Here we describe the anota (analysis of translational activity) R-package which implements APV, allows scrutiny of associated statistical assumptions and provides biologically motivated filters for analysis of genome wide datasets. Although the package was developed for analysis of differential translation in polysome microarray or ribosome-profiling datasets, any high-dimensional data that result in paired controls, such as RNP immunoprecipitation-microarray (RIP-CHIP) datasets, can be successfully analyzed with anota.
The anota Bioconductor package, www.bioconductor.org.
基因表达的翻译调控已成为调节许多生物过程的主要机制,并在包括癌症在内的人类疾病中出现失调。在研究差异翻译时,会获得活跃翻译的 mRNA 和总胞质 mRNA 的水平,其中后者用于校正差异胞质 mRNA 水平对观察到的活跃翻译的 mRNA 水平的差异可能产生的影响。我们最近表明,部分方差(APV)分析比常用的对数比方法更有效地校正胞质 mRNA 水平。APV 为检测具有生物学意义的翻译变化提供了高度的特异性和敏感性,特别是当与用于估计随机误差的方差收缩方法结合使用时。在这里,我们描述了 anota(翻译活性分析)R 包,它实现了 APV,允许仔细检查相关的统计假设,并为分析全基因组数据集提供了基于生物学的筛选。尽管该软件包是为分析多核糖体微阵列或核糖体分析数据集的差异翻译而开发的,但任何产生配对对照的高维数据,如 RNP 免疫沉淀微阵列(RIP-CHIP)数据集,都可以成功地使用 anota 进行分析。
anota Bioconductor 包,www.bioconductor.org。