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SeqTU: A Web Server for Identification of Bacterial Transcription Units
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...
Autores principales: | , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Nature Publishing Group
2017
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5339711/ https://www.ncbi.nlm.nih.gov/pubmed/28266571 http://dx.doi.org/10.1038/srep43925 |
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author | Chen, Xin Chou, Wen-Chi Ma, Qin Xu, Ying |
author_facet | Chen, Xin Chou, Wen-Chi Ma, Qin Xu, Ying |
author_sort | Chen, Xin |
collection | PubMed |
description | 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. |
format | Online Article Text |
id | pubmed-5339711 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2017 |
publisher | Nature Publishing Group |
record_format | MEDLINE/PubMed |
spelling | pubmed-53397112017-03-10 SeqTU: A Web Server for Identification of Bacterial Transcription Units Chen, Xin Chou, Wen-Chi Ma, Qin Xu, Ying Sci Rep Article 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. Nature Publishing Group 2017-03-07 /pmc/articles/PMC5339711/ /pubmed/28266571 http://dx.doi.org/10.1038/srep43925 Text en Copyright © 2017, The Author(s) http://creativecommons.org/licenses/by/4.0/ This work is licensed under a Creative Commons Attribution 4.0 International License. The images or other third party material in this article are included in the article’s Creative Commons license, unless indicated otherwise in the credit line; if the material is not included under the Creative Commons license, users will need to obtain permission from the license holder to reproduce the material. To view a copy of this license, visit http://creativecommons.org/licenses/by/4.0/ |
spellingShingle | Article Chen, Xin Chou, Wen-Chi Ma, Qin Xu, Ying SeqTU: A Web Server for Identification of Bacterial Transcription Units |
title | SeqTU: A Web Server for Identification of Bacterial Transcription Units |
title_full | SeqTU: A Web Server for Identification of Bacterial Transcription Units |
title_fullStr | SeqTU: A Web Server for Identification of Bacterial Transcription Units |
title_full_unstemmed | SeqTU: A Web Server for Identification of Bacterial Transcription Units |
title_short | SeqTU: A Web Server for Identification of Bacterial Transcription Units |
title_sort | seqtu: a web server for identification of bacterial transcription units |
topic | Article |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5339711/ https://www.ncbi.nlm.nih.gov/pubmed/28266571 http://dx.doi.org/10.1038/srep43925 |
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