UNLABELLED: 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. AVAILABILITY: The anota Bioconductor package, www.bioconductor.org.

BACKGROUND: Microarrays enable high throughput detection of transcript expression levels. Different investigators have recently introduced updated probe set definitions to more accurately map probes to our current knowledge of genes and transcripts. RESULTS: We demonstrate that updated probe set definitions provide both better precision and accuracy in probe set estimates compared to the original Affymetrix definitions. We show that the improved precision mainly depends on the increased number of probes that are integrated into each probe set, but we also demonstrate an improvement when the same number of probes is used. CONCLUSION: Updated probe set definitions does not only offer expression levels that are more accurately associated to genes and transcripts but also improvements in the estimated transcript expression levels. These results give support for the use of updated probe set definitions for analysis and meta-analysis of microarray data.

BACKGROUND: Users of microarray technology typically strive to use universally acceptable data analysis strategies to determine significant expression changes in their experiments. One of the most frequently utilised methods for gene expression data analysis is SAM (significance analysis of microarrays). The impact of selection thresholds, on the output from SAM, may critically alter the conclusion of a study, yet this consideration has not been systematically evaluated in any publication. RESULTS: We have examined the effect of discrete data selection criteria (qualification criteria for inclusion) and response thresholds (out-put filtering) on the number of significant genes reported by SAM. The use of a reduced data set by applying arbitrary restrictions vis-à-vis abundance calls (e.g. from D-chip) or application of the fold change (FC) option within SAM (named the FC hurdle hereafter), can substantially alter the significant gene list when running SAM in Microsoft Excel. We determined that for a given final FC criteria (e.g. 1.5 fold change) the FC hurdle applied within Microsoft Excel SAM alters the number of reported genes above the final FC criteria. The reason is that the FC hurdle changes the composition of the control data set, such that a different significance level (q-value) is obtained for any given gene. This effect can be so large that it changes subsequent post hoc analysis interpretation, such as ontology overrepresentation analysis. CONCLUSION: Our results argue for caution when using SAM. All data sets analysed with SAM could be reanalysed taking into account the potential impact of the use of arbitrary thresholds to trim data sets before significance testing.