anota

anota performs genome-wide analysis of translational control by identifying differences in actively translated mRNA levels that are independent of cytosolic mRNA concentrations.

Key Features:

• Analysis of Partial Variance (APV): APV corrects for differences in cytosolic mRNA levels and improves detection of translation-specific changes compared with traditional log ratio approaches.
• Random Variance Model: Incorporates a random variance model combined with variance shrinkage methods to estimate random error and refine translational activity estimates.
• Statistical Assumptions and Filters: Provides checks of associated statistical assumptions and applies biologically motivated filters to strengthen robustness of results.
• Versatility Across High-Dimensional Data: Applicable to polysome microarray and ribosome-profiling datasets as well as other high-dimensional paired control data such as RNP immunoprecipitation-microarray (RIP-CHIP).

Scientific Applications:

• Differential Translation Studies: Identifies mRNAs with changes in translation independent of total cytosolic mRNA, facilitating studies of translational control and its dysregulation in diseases such as cancer.
• Genome-Wide Dataset Analysis: Analyzes large-scale, genome-wide datasets to uncover translational regulation patterns across the transcriptome.

Methodology:

Computational methods explicitly include Analysis of Partial Variance (APV), a random variance model with variance shrinkage, statistical-assumption checks, and biologically motivated filters.