Abstract
A transcription unit (TU) consists of K ≥ 1consecutive genes on the same strand of a bacterial genome that are transcribed into a single mRNA molecule under certain conditions. Their identification is an essential step in elucidation of transcriptional regulatory networks. We have recently developed a machine-learning method to accurately identify TUs from RNA-seq data, based on two features of the assembled RNA reads: the continuity and stability of RNA-seq coverage across a genomic region. While good performance was achieved by the method on Escherichia coli and Clostridium thermocellum, substantial work is needed to make the program generally applicable to all bacteria, knowing that the program requires organism specific information. A web server, named SeqTU, was developed to automatically identify TUs with given RNA-seq data of any bacterium using a machine-learning approach. The server consists of a number of utility tools, in addition to TU identification, such as data preparation, data quality check and RNA-read mapping. SeqTU provides a user-friendly interface and automated prediction of TUs from given RNA-seq data. The predicted TUs are displayed intuitively using HTML format along with a graphic visualization of the prediction.
Highlights
Some computational studies have been published for identification of transcription unit (TU) based on Tiling array and RNA-seq data[9,10,11], which has offered new information about the complexity of bacterial transcription and regulation
It is noteworthy that we have previously developed an integrative operon database, DOOR2, covering 2,072 bacteria genomes
By integrating the prediction results of SeqTU and DOOR2, a user can see the dynamic changes of expressions under different conditions
Summary
Some computational studies have been published for identification of TUs based on Tiling array and RNA-seq data[9,10,11], which has offered new information about the complexity of bacterial transcription and regulation. Compared to operons that have numerous public databases and prediction servers, currently there are no prediction services for TU identification. The back-end programs, implemented using PERL and R, along with test examples and documents are freely available online at http://csbl.bmb.uga.edu/ SeqTU_dev/index.php. It is noteworthy that we have previously developed an integrative operon database, DOOR2 By integrating the prediction results of SeqTU and DOOR2, a user can see the dynamic changes of expressions under different conditions
Sign-in/Register to view highlights & summary 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.
