ArticleBioinformatics (Oxford, England)2014
tRanslatome: an R/Bioconductor package to portray translational control.
Article in Bioinformatics (Oxford, England), 2014. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 11 papers.
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
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Who cites it
11 citing papers in PubMed.
- EasyMultiProfiler: an efficient multi-omics data integration and analysis workflow for microbiome research.Science China. Life sciences · 2026Article
- Article
- Introduction to Bioinformatics Resources for Post-transcriptional Regulation of Gene Expression.Methods in molecular biology (Clifton, N.J.) · 2022Article
- RiboDiPA: a novel tool for differential pattern analysis in Ribo-seq data.Nucleic acids research · 2020Article
- RIVET: comprehensive graphic user interface for analysis and exploration of genome-wide translatomics data.BMC genomics · 2018Article
- The LIN28B-IMP1 post-transcriptional regulon has opposing effects on oncogenic signaling in the intestine.Genes & development · 2018Article
- Following Ribosome Footprints to Understand Translation at a Genome Wide Level.Computational and structural biotechnology journal · 2018Review
- In Vivo Translatome Profiling in Spinal Muscular Atrophy Reveals a Role for SMN Protein in Ribosome Biology.Cell reports · 2017Article
- p53-directed translational control can shape and expand the universe of p53 target genes.Cell death and differentiation · 2014Article
- Translational control of immune responses: from transcripts to translatomes.Nature immunology · 2014Review
- Diarylheptanoids from Alpinia officinarum Cause Distinct but Overlapping Effects on the Translatome of B Lymphoblastoid Cells.Evidence-based complementary and alternative medicine : eCAM · 2014Article
Corrections and comments
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Authors and funding
5 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
unlabelledHigh-throughput technologies have led to an explosion of genomic data available for automated analysis. The consequent possibility to simultaneously sample multiple layers of variation along the gene expression flow requires computational methods integrating raw information from different '-omics'. It has been recently demonstrated that translational control is a widespread phenomenon, with profound and still underestimated regulation capabilities. Although detecting changes in the levels of total messenger RNAs (mRNAs; the transcriptome), of polysomally loaded mRNAs (the translatome) and of proteins (the proteome) is experimentally feasible in a high-throughput way, the integration of these levels is still far from being robustly approached. Here we introduce tRanslatome, a new R/Bioconductor package, which is a complete platform for the simultaneous pairwise analysis of transcriptome, translatome and proteome data. The package 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. Notably, it also enables the prediction of translational regulatory elements on mRNA sequences. The utility of this tool is demonstrated with two case studies. AVAILABILITY AND IMPLEMENTATION: tRanslatome is available in Bioconductor.
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.