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      Comparative ribosome profiling reveals extensive translational complexity in different Trypanosoma brucei life cycle stages

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          Abstract

          While gene expression is a fundamental and tightly controlled cellular process that is regulated at multiple steps, the exact contribution of each step remains unknown in any organism. The absence of transcription initiation regulation for RNA polymerase II in the protozoan parasite Trypanosoma brucei greatly simplifies the task of elucidating the contribution of translation to global gene expression. Therefore, we have sequenced ribosome-protected mRNA fragments in T. brucei, permitting the genome-wide analysis of RNA translation and translational efficiency. We find that the latter varies greatly between life cycle stages of the parasite and ∼100-fold between genes, thus contributing to gene expression to a similar extent as RNA stability. The ability to map ribosome positions at sub-codon resolution revealed extensive translation from upstream open reading frames located within 5′ UTRs and enabled the identification of hundreds of previously un-annotated putative coding sequences (CDSs). Evaluation of existing proteomics and genome-wide RNAi data confirmed the translation of previously un-annotated CDSs and suggested an important role for >200 of those CDSs in parasite survival, especially in the form that is infective to mammals. Overall our data show that translational control plays a prevalent and important role in different parasite life cycle stages of T. brucei.

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          Most cited references 99

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          Fast gapped-read alignment with Bowtie 2.

          As the rate of sequencing increases, greater throughput is demanded from read aligners. The full-text minute index is often used to make alignment very fast and memory-efficient, but the approach is ill-suited to finding longer, gapped alignments. Bowtie 2 combines the strengths of the full-text minute index with the flexibility and speed of hardware-accelerated dynamic programming algorithms to achieve a combination of high speed, sensitivity and accuracy.
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            edgeR: a Bioconductor package for differential expression analysis of digital gene expression data

            Summary: It is expected that emerging digital gene expression (DGE) technologies will overtake microarray technologies in the near future for many functional genomics applications. One of the fundamental data analysis tasks, especially for gene expression studies, involves determining whether there is evidence that counts for a transcript or exon are significantly different across experimental conditions. edgeR is a Bioconductor software package for examining differential expression of replicated count data. An overdispersed Poisson model is used to account for both biological and technical variability. Empirical Bayes methods are used to moderate the degree of overdispersion across transcripts, improving the reliability of inference. The methodology can be used even with the most minimal levels of replication, provided at least one phenotype or experimental condition is replicated. The software may have other applications beyond sequencing data, such as proteome peptide count data. Availability: The package is freely available under the LGPL licence from the Bioconductor web site (http://bioconductor.org). Contact: mrobinson@wehi.edu.au
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              Differential expression analysis for sequence count data

              High-throughput sequencing assays such as RNA-Seq, ChIP-Seq or barcode counting provide quantitative readouts in the form of count data. To infer differential signal in such data correctly and with good statistical power, estimation of data variability throughout the dynamic range and a suitable error model are required. We propose a method based on the negative binomial distribution, with variance and mean linked by local regression and present an implementation, DESeq, as an R/Bioconductor package.
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                Author and article information

                Journal
                Nucleic Acids Res
                Nucleic Acids Res
                nar
                nar
                Nucleic Acids Research
                Oxford University Press
                0305-1048
                1362-4962
                April 2014
                17 January 2014
                17 January 2014
                : 42
                : 6
                : 3623-3637
                Affiliations
                1Research Center for Infectious Diseases, University of Wuerzburg, Wuerzburg 97080, Germany, 2Département Biologie cellulaire et infection, Institut Pasteur, Unité Biologie Cellulaire du Parasitisme, Paris 75015, France, 3INSERM U786, Paris 75015, France and 4Rudolf Virchow Center, University of Wuerzburg, Wuerzburg 97080, Germany
                Author notes
                *To whom correspondence should be addressed. Tel: +49 931 31 88 499; Fax: +49 931 318 25 78; Email: nicolai.siegel@ 123456uni-wuerzburg.de
                Article
                gkt1386
                10.1093/nar/gkt1386
                3973304
                24442674
                © The Author(s) 2014. Published by Oxford University Press.

                This is an Open Access article distributed under the terms of the Creative Commons Attribution License ( http://creativecommons.org/licenses/by/3.0/), which permits unrestricted reuse, distribution, and reproduction in any medium, provided the original work is properly cited.

                Counts
                Pages: 15
                Categories
                Gene Regulation, Chromatin and Epigenetics

                Genetics

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