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:
-
- Jilin Univ. Jilin (China); Univ. of Georgia, Athens, GA (United States); BioEnergy Science Center, Washington, D.C. (United States); Tianjin Univ., Tianjin (China)
- Broad Institute of MIT and Harvard Univ., Cambridge, MA (United States)
- South Dakota State Univ., Brookings, SD (United States)
- 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)
- OSTI Identifier:
- 1355909
- Grant/Contract Number:
- SC0013632
- Resource Type:
- 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. https://doi.org/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. https://doi.org/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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Works referenced in this record:
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journal, September 2014
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journal, October 2007
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- Nucleic Acids Research, Vol. 36, Issue suppl_1
DOOR: a database for prokaryotic operons
journal, November 2008
- Mao, Fenglou; Dam, Phuongan; Chou, Jacky
- Nucleic Acids Research, Vol. 37, Issue suppl_1
OperonDB: a comprehensive database of predicted operons in microbial genomes
journal, January 2009
- Pertea, M.; Ayanbule, K.; Smedinghoff, M.
- Nucleic Acids Research, Vol. 37, Issue Database
DOOR 2.0: presenting operons and their functions through dynamic and integrated views
journal, November 2013
- Mao, Xizeng; Ma, Qin; Zhou, Chuan
- Nucleic Acids Research, Vol. 42, Issue D1
Computational analysis of bacterial RNA-Seq data
journal, May 2013
- McClure, Ryan; Balasubramanian, Divya; Sun, Yan
- Nucleic Acids Research, Vol. 41, Issue 14
Analysis of strand-specific RNA-seq data using machine learning reveals the structures of transcription units in Clostridium thermocellum
journal, March 2015
- Chou, Wen-Chi; Ma, Qin; Yang, Shihui
- Nucleic Acids Research, Vol. 43, Issue 10
De novo assembly of bacterial transcriptomes from RNA-seq data
journal, January 2015
- Tjaden, Brian
- Genome Biology, Vol. 16, Issue 1
Minimal metabolic pathway structure is consistent with associated biomolecular interactions
journal, July 2014
- Bordbar, Aarash; Nagarajan, Harish; Lewis, Nathan E.
- Molecular Systems Biology, Vol. 10, Issue 7
Works referencing / citing this record:
DOOR: a prokaryotic operon database for genome analyses and functional inference
journal, July 2017
- Cao, Huansheng; Ma, Qin; Chen, Xin
- Briefings in Bioinformatics, Vol. 20, Issue 4
A New Machine Learning-Based Framework for Mapping Uncertainty Analysis in RNA-Seq Read Alignment and Gene Expression Estimation
journal, August 2018
- McDermaid, Adam; Chen, Xin; Zhang, Yiran
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Single-Cell RNA Sequencing of Plant-Associated Bacterial Communities
journal, October 2019
- Ma, Qin; Bücking, Heike; Gonzalez Hernandez, Jose L.
- Frontiers in Microbiology, Vol. 10
AtbPpred: A Robust Sequence-Based Prediction of Anti-Tubercular Peptides Using Extremely Randomized Trees
journal, January 2019
- Manavalan, Balachandran; Basith, Shaherin; Shin, Tae Hwan
- Computational and Structural Biotechnology Journal, Vol. 17
Single-Cell RNA Sequencing of Plant-Associated Bacterial Communities
journal, October 2019
- Ma, Qin; Bücking, Heike; Gonzalez Hernandez, Jose L.
- Frontiers in Microbiology, Vol. 10