TRanslatome: An R/Bioconductor package to portray translational control

Bioinformatics

Volumn 30, Issue 2, 2014, Pages 289-291

  Tebaldi, Toma ^a   Dassi, Erik ^a   Kostoska, Galena ^a   Viero, Gabriella ^b   Quattrone, Alessandro ^a  

^a UNIVERSITY OF TRENTO   (Italy)

^b CNR   (Italy)

Author keywords

[No Author keywords available]

Indexed keywords

MESSENGER RNA; PROTEOME; TRANSCRIPTOME;

ARTICLE; BIOLOGY; CELL DIFFERENTIATION; COMPUTER PROGRAM; CYTOLOGY; FACTUAL DATABASE; GENETICS; GENOMICS; HUMAN; LIVER CELL; METABOLISM; PROTEIN SYNTHESIS; REGULATORY SEQUENCE;

CELL DIFFERENTIATION; COMPUTATIONAL BIOLOGY; DATABASES, FACTUAL; GENOMICS; HEPATOCYTES; HUMANS; PROTEIN BIOSYNTHESIS; PROTEOME; REGULATORY SEQUENCES, NUCLEIC ACID; RNA, MESSENGER; SOFTWARE; TRANSCRIPTOME;

EID: 84892739582     PISSN: 13674803     EISSN: 14602059     Source Type: Journal    
DOI: 10.1093/bioinformatics/btt634     Document Type: Article

References (18)

1. Anders, S. and Huber, W. (2010) Differential expression analysis for sequence count data. Genome Biol., 11, R106.
2. Ashburner, M. et al. (2000) Gene ontology: tool for the unification of biology. The Gene Ontology Consortium. Nat. Genet., 25, 25-29.
3. Breitling, R. et al. (2004) Rank products: a simple, yet powerful, new method to detect differentially regulated genes in replicated microarray experiments. FEBS Lett., 573, 83-92.
4. Colman, H. et al. (2013) Genome-wide analysis of host mRNA translation during hepatitis C virus infection. J. Virol., 87, 6668-6677.
5. Dassi, E. et al. (2012) AURA: Atlas of UTR Regulatory Activity. Bioinformatics, 28, 142-144.
6. Ingolia, N.T. et al. (2011) Ribosome profiling of mouse embryonic stem cells reveals the complexity and dynamics of mammalian proteomes. Cell, 147, 789-802.
7. Larsson, O. et al. (2011) anota: Analysis of differential translation in genome-wide studies. Bioinformatics, 27, 1440-1441.
8. Parent, R. et al. (2007) Mammalian target of rapamycin activation impairs hepato-cytic differentiation and targets genes moderating lipid homeostasis and hepa-tocellular growth. Cancer Res., 67, 4337-4345.
9. Powley, I.R. et al. (2009) Translational reprogramming following UVB irradiation is mediated by DNA-PKcs and allows selective recruitment to the polysomes of mRNAs encoding DNA repair enzymes. Genes Dev., 23, 1207-1220.
10. Robinson, M.D. et al. (2010) edgeR: a Bioconductor package for differential expression analysis of digital gene expression data. Bioinformatics, 26, 139-140.
11. Schwanhausser, B. et al. (2011) Global quantification of mammalian gene expression control. Nature, 473, 337-342.
12. Smyth, G.K. (2004) Linear models and empirical bayes methods for assessing differential expression in microarray experiments. Stat. Appl. Genet. Mol. Biol., 3, Article3.
13. Stevens, S.G. and Brown, C.M. (2013) In silico estimation of translation efficiency in human cell lines: potential evidence for widespread translational control. PLoS One, 8, e57625.
14. Tebaldi, T. et al. (2012) Widespread uncoupling between transcriptome and transla-tome variations after a stimulus in mammalian cells. BMC Genomics, 13, 220.
15. Tusher, V.G. et al. (2001) Significance analysis of microarrays applied to the ionizing radiation response. Proc. Natl. Acad. Sci. USA., 98, 5116-5121.
16. Vogel, C. and Marcotte, E.M. (2012) Insights into the regulation of protein abundance from proteomic and transcriptomic analyses. Nat. Rev. Genet., 13, 227-232.
17. Vogel, C. et al. (2010) Sequence signatures and mRNA concentration can explain two-thirds of protein abundance variation in a human cell line. Mol. Syst. Biol., 6, 400.
18. Wang, J.Z. et al. (2007) A new method to measure the semantic similarity of GO terms. Bioinformatics, 23, 1274-1281.