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BMC Genomics. 2013 Dec 17;14:888. doi: 10.1186/1471-2164-14-888.

Comprehensive analysis of the Corynebacterium glutamicum transcriptome using an improved RNAseq technique.

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Microbial Genomics and Biotechnology, Center for Biotechnology, Bielefeld University, Universitätsstraße 27, 33615, Bielefeld, Germany.



The use of RNAseq to resolve the transcriptional organization of an organism was established in recent years and also showed the complexity and dynamics of bacterial transcriptomes. The aim of this study was to comprehensively investigate the transcriptome of the industrially relevant amino acid producer and model organism Corynebacterium glutamicum by RNAseq in order to improve its genome annotation and to describe important features for transcription and translation.


RNAseq data sets were obtained by two methods, one that focuses on 5'-ends of primary transcripts and another that provides the overall transcriptome with an improved resolution of 3'-ends of transcripts. Subsequent data analysis led to the identification of more than 2,000 transcription start sites (TSSs), the definition of 5'-UTRs (untranslated regions) for annotated protein-coding genes, operon structures and many novel transcripts located between or in antisense orientation to protein-coding regions. Interestingly, a high number of mRNAs (33%) is transcribed as leaderless transcripts. From the data, consensus promoter and ribosome binding site (RBS) motifs were identified and it was shown that the majority of genes in C. glutamicum are transcribed monocistronically, but operons containing up to 16 genes are also present.


The comprehensive transcriptome map of C. glutamicum established in this study represents a major step forward towards a complete definition of genetic elements (e.g. promoter regions, gene starts and stops, 5'-UTRs, RBSs, transcript starts and ends) and provides the ideal basis for further analyses on transcriptional regulatory networks in this organism. The methods developed are easily applicable for other bacteria and have the potential to be used also for quantification of transcriptomes, replacing microarrays in the near future.

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