Abstract
Over the past decade, a number of biocomputational tools have been developed to predict small RNA (sRNA) genes in bacterial genomes. In this study, several of the leading biocomputational tools, which use different methodologies, were investigated. The performance of the tools, both individually and in combination, was evaluated on ten sets of benchmark data, including data from a novel RNA-seq experiment conducted in this study. The results of this study offer insight into the utility as well as the limitations of the leading biocomputational tools for sRNA identification and provide practical guidance for users of the tools.
Sign-in/Register to access full text options 
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a callDisclaimer: All third-party content on this website/platform is and will remain the property of their respective owners and is provided on "as is" basis without any warranties, express or implied. Use of third-party content does not indicate any affiliation, sponsorship with or endorsement by them. Any references to third-party content is to identify the corresponding services and shall be considered fair use under The CopyrightLaw.
