nocoRNAc: Characterization of noncoding RNAs in prokaryotes. Background: The interest in non-coding RNAs (ncRNAs) constantly rose during the past few years because of the wide spectrum of biological processes in which they are involved. This led to the discovery of numerous ncRNA genes across many species. However, for most organisms the non-coding transcriptome still remains unexplored to a great extent. Various experimental techniques for the identification of ncRNA transcripts are available, but as these methods are costly and time-consuming, there is a need for computational methods that allow the detection of functional RNAs in complete genomes in order to suggest elements for further experiments. Several programs for the genome-wide prediction of functional RNAs have been developed but most of them predict a genomic locus with no indication whether the element is transcribed or not. Results: We present NOCO RNAc, a program for the genome-wide prediction of ncRNA transcripts in bacteria. NOCO RNAc incorporates various procedures for the detection of transcriptional features which are then integrated with functional ncRNA loci to determine the transcript coordinates. We applied RNAz and NOCO RNAc to the genome of Streptomyces coelicolor and detected more than 800 putative ncRNA transcripts most of them located antisense to protein-coding regions. Using a custom design microarray we profiled the expression of about 400 of these elements and found more than 300 to be transcribed, 38 of them are predicted novel ncRNA genes in intergenic regions. The expression patterns of many ncRNAs are similarly complex as those of the protein-coding genes, in particular many antisense ncRNAs show a high expression correlation with their protein-coding partner. Conclusions: We have developed NOCO RNAc, a framework that facilitates the automated characterization of functional ncRNAs. NOCO RNAc increases the confidence of predicted ncRNA loci, especially if they contain transcribed ncRNAs. NOCO RNAc is not restricted to intergenic regions, but it is applicable to the prediction of ncRNA transcripts in whole microbial genomes. The software as well as a user guide and example data is available at

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  1. Kasabov, Nikola (ed.): Springer handbook of bio-/neuro-informatics (2014)
  2. Herbig, Alexander; Nieselt, Kay: Nocornac: characterization of non-coding rnas in prokaryotes (2011) ioport