title: OperomeDB: A Database of Condition-Specific Transcription Units in Prokaryotic Genomes.
abstract: 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.
authors.affiliation: Department of Biohealth Informatics, School of Informatics and Computing, Indiana University-Purdue University Indianapolis (IUPUI), 719 Indiana Avenue, Suite 319, Walker Plaza Building, Indianapolis, IN 46202, USA.
authors.forename: Kashish
authors.initials: K
authors.lastname: Chetal
authors.affiliation: Department of Biohealth Informatics, School of Informatics and Computing, Indiana University-Purdue University Indianapolis (IUPUI), 719 Indiana Avenue, Suite 319, Walker Plaza Building, Indianapolis, IN 46202, USA ; Center for Computational Biology and Bioinformatics, Indiana University School of Medicine, 5021 Health Information and Translational Sciences (HITS), 410 West 10th Street, Indianapolis, IN 46202, USA ; Department of Medical and Molecular Genetics, Indiana University School of Medicine, Medical Research and Library Building, 975 West Walnut Street, Indianapolis, IN 46202, USA.
authors.forename: Sarath Chandra
authors.initials: SC
authors.lastname: Janga
mesh_terms: D001419:Bacteria / Q000235:genetics*; D016123:Campylobacter jejuni / Q000235:genetics; D016360:Clostridioides difficile / Q000235:genetics; D046970:Clostridium beijerinckii / Q000235:genetics; D019295:Computational Biology; D030541:Databases, Genetic; D048168:Escherichia coli K12 / Q000235:genetics; D020869:Gene Expression Profiling; D016680:Genome, Bacterial; D023281:Genomics / Q000379:methods*; D009169:Mycobacterium tuberculosis / Q000235:genetics; D009876:Operon; D011387:Prokaryotic Cells; D015203:Reproducibility of Results; D019779:Salmonella enterica / Q000235:genetics; D017423:Sequence Analysis, RNA; D016962:Sinorhizobium meliloti / Q000235:genetics; D012984:Software; D046940:Synechococcus / Q000235:genetics; D059467:Transcriptome