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Title: SeqTU: A web server for identification of bacterial transcription units

Abstract

A transcription unit (TU) consists of K ≥ 1 consecutive 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. Furthermore, the predicted TUs are displayed intuitively using HTML format along with a graphic visualization of the prediction.

Authors:
 [1];  [2];  [3];  [4]
  1. Jilin Univ. Jilin (China); Univ. of Georgia, Athens, GA (United States); BioEnergy Science Center, Washington, D.C. (United States); Tianjin Univ., Tianjin (China)
  2. Broad Institute of MIT and Harvard Univ., Cambridge, MA (United States)
  3. South Dakota State Univ., Brookings, SD (United States)
  4. Jilin Univ., Jilin (China); Univ. of Georgia, Athens, GA (United States); BioEnergy Science Center, Washington, D.C. (United States)
Publication Date:
Research Org.:
South Dakota State Univ., Brookings, SD (United States)
Sponsoring Org.:
USDOE Office of Science (SC), Biological and Environmental Research (BER) (SC-23)
OSTI Identifier:
1355909
Grant/Contract Number:  
SC0013632
Resource Type:
Journal Article: Accepted Manuscript
Journal Name:
Scientific Reports
Additional Journal Information:
Journal Volume: 7; Journal ID: ISSN 2045-2322
Publisher:
Nature Publishing Group
Country of Publication:
United States
Language:
English
Subject:
59 BASIC BIOLOGICAL SCIENCES; bacillus-subtilis; Escherichia coli; database; operons; reveals; door; bioinformatics; computational platforms and environments; software

Citation Formats

Chen, Xin, Chou, Wen -Chi, Ma, Qin, and Xu, Ying. SeqTU: A web server for identification of bacterial transcription units. United States: N. p., 2017. Web. doi:10.1038/srep43925.
Chen, Xin, Chou, Wen -Chi, Ma, Qin, & Xu, Ying. SeqTU: A web server for identification of bacterial transcription units. United States. doi:10.1038/srep43925.
Chen, Xin, Chou, Wen -Chi, Ma, Qin, and Xu, Ying. Tue . "SeqTU: A web server for identification of bacterial transcription units". United States. doi:10.1038/srep43925. https://www.osti.gov/servlets/purl/1355909.
@article{osti_1355909,
title = {SeqTU: A web server for identification of bacterial transcription units},
author = {Chen, Xin and Chou, Wen -Chi and Ma, Qin and Xu, Ying},
abstractNote = {A transcription unit (TU) consists of K ≥ 1 consecutive 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. Furthermore, the predicted TUs are displayed intuitively using HTML format along with a graphic visualization of the prediction.},
doi = {10.1038/srep43925},
journal = {Scientific Reports},
number = ,
volume = 7,
place = {United States},
year = {Tue Mar 07 00:00:00 EST 2017},
month = {Tue Mar 07 00:00:00 EST 2017}
}

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