Differential translation identification software tools | Ribosome profiling data analysis
Global analysis of translation regulation has recently been enabled by the development of Ribosome Profiling, or Ribo-seq, technology. This approach provides maps of ribosome activity for each expressed gene in a given biological sample. Measurements of translation efficiency are generated when Ribo-seq data is analyzed in combination with matched RNA-seq gene expression profiles. Existing computational methods for identifying genes with differential translation across samples are based on sound principles, but require users to choose between accuracy and speed.
Implements analysis of partial variance and allows scrutiny of associated statistical assumptions. ANOTA also provides biologically motivated filters for analysis of genome wide datasets. It 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 (ribonucleoprotein) immunoprecipitation-microarray (RIP-CHIP) datasets.
Allows users to quantify the magnitudes and statistical significances of differential translations at the genome-wide scale with ribosome profiling data. Xtail is able to identify the significant translational dysregulations that make biological sense. This tool provides biological insights into the molecular and cellular responses to mammalian target of rapamycin (mTOR) signaling perturbation in prostate cancer cells.
Detects the protein translational efficiency change from Ribo-Seq (ribosome footprinting) and RNA-Seq data. RiboDiff uses a generalized linear model to detect genes showing difference in translational profile taking mRNA abundance into account. It facilitates us to decipher the translational regulation that behave independently with transcriptional regulation.
An analytical methodology for assessing the significance of changes in translational regulation within cells and between conditions. This approach facilitates the analysis of translation genome-wide while allowing statistically principled gene-level inference. Babel is based on an errors-in-variables regression model that uses the negative binomial distribution and draws inference using a parametric bootstrap approach.
Provides a complete platform for the simultaneous pairwise analysis of transcriptome, translatome and proteome data. tRanslatome includes most of the available statistical methods developed for the analysis of high-throughput data, allowing the parallel comparison of differentially expressed genes and the corresponding differentially enriched biological themes. It will help to study mRNA and protein variations in an exhaustive way, providing specific tools for the comparison of polysomal mRNA with total mRNA or protein data. tRanslatome allows a user-friendly comparison and integration of data generated from two ‘-omics’ measurements, empowering the discovery of regulatory mechanisms underlying the uncoupling processes among the transcriptome, the translatome and the proteome.
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