SeqTU: A web server for identification of bacterial transcription units

Chen, Xin; Chou, Wen -Chi; Ma, Qin; Xu, Ying

doi:10.1038/srep43925

SeqTU: A web server for identification of bacterial transcription units

Journal Article · Tue Mar 07 00:00:00 EST 2017 · Scientific Reports

DOI:https://doi.org/10.1038/srep43925· OSTI ID:1355909

Chen, Xin ^[1]; Chou, Wen -Chi ^[2]; Ma, Qin ^[3]; Xu, Ying ^[4]

Jilin Univ. Jilin (China); Univ. of Georgia, Athens, GA (United States); BioEnergy Science Center, Washington, D.C. (United States); Tianjin Univ., Tianjin (China); South Dakota State University
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)

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.

Research Organization:: South Dakota State Univ., Brookings, SD (United States)

Sponsoring Organization:: USDOE Office of Science (SC), Biological and Environmental Research (BER) (SC-23)

Grant/Contract Number:: SC0013632

OSTI ID:: 1355909

Journal Information:: Scientific Reports, Journal Name: Scientific Reports Vol. 7; ISSN 2045-2322

Publisher:: Nature Publishing GroupCopyright Statement

Country of Publication:: United States

Language:: English

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Single-Cell RNA Sequencing of Plant-Associated Bacterial Communities Ma, Qin; Bücking, Heike; Gonzalez Hernandez, Jose L. Frontiers in Microbiology, Vol. 10 https://doi.org/10.3389/fmicb.2019.02452	journal	October 2019
DOOR: a prokaryotic operon database for genome analyses and functional inference Cao, Huansheng; Ma, Qin; Chen, Xin Briefings in Bioinformatics, Vol. 20, Issue 4 https://doi.org/10.1093/bib/bbx088	journal	July 2017
A New Machine Learning-Based Framework for Mapping Uncertainty Analysis in RNA-Seq Read Alignment and Gene Expression Estimation McDermaid, Adam; Chen, Xin; Zhang, Yiran Frontiers in Genetics, Vol. 9 https://doi.org/10.3389/fgene.2018.00313	journal	August 2018

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59 BASIC BIOLOGICAL SCIENCES
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SeqTU: A web server for identification of bacterial transcription units

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