OperomeDB: A Database of Condition-Specific Transcription Units in Prokaryotic Genomes

• Published in: BioMed research international 2015-01, Vol.2015 (2015), p.1-10
• Main Authors: Chetal, Kashish, Janga, Sarath Chandra
• Format: Article
• Language: English
• Subjects: Algorithms; Bacteria - genetics; Campylobacter jejuni - genetics; Clostridium beijerinckii - genetics; Clostridium difficile - genetics; Computational Biology; Databases, Genetic; Datasets; E coli; Escherichia coli K12 - genetics; Gene Expression Profiling; Genes; Genetic transcription; Genome, Bacterial; Genomes; Genomics; Genomics - methods; Informatics; Medical research; Mycobacterium tuberculosis - genetics; Observations; Operon; Organisms; Phylogenetics; Prokaryotic Cells; Reproducibility of Results; RNA sequencing; Salmonella enterica - genetics; Sequence Analysis, RNA; Sinorhizobium meliloti - genetics; Software; Synechococcus - genetics; Technology application; Transcriptome; Visualization
• ISSN: 2314-6133; 2314-6141
• DOI: 10.1155/2015/318217

Background. In prokaryotic organisms, a substantial fraction of adjacent genes are organized into operons—codirectionally organized genes in prokaryotic genomes with the presence of a common promoter and terminator. Although several available operon databases provide information with varying levels of reliability, very few resources provide experimentally supported results. Therefore, we believe that the biological community could benefit from having a new operon prediction database with operons predicted using next-generation RNA-seq datasets. Description. We present operomeDB, a database which provides an ensemble of all the predicted operons for bacterial genomes using available RNA-sequencing datasets across a wide range of experimental conditions. Although several studies have recently confirmed that prokaryotic operon structure is dynamic with significant alterations across environmental and experimental conditions, there are no comprehensive databases for studying such variations across prokaryotic transcriptomes. Currently our database contains nine bacterial organisms and 168 transcriptomes for which we predicted operons. User interface is simple and easy to use, in terms of visualization, downloading, and querying of data. In addition, because of its ability to load custom datasets, users can also compare their datasets with publicly available transcriptomic data of an organism. Conclusion. OperomeDB as a database should not only aid experimental groups working on transcriptome analysis of specific organisms but also enable studies related to computational and comparative operomics.