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Zipper plot : visualizing transcriptional activity of genomic regions

Francisco Avila Cobos UGent, Jasper Anckaert UGent, Pieter-Jan Volders UGent, Celine Everaert UGent, Dries Rombaut UGent, Jo Vandesompele UGent, Katleen De Preter UGent and Pieter Mestdagh UGent (2017) BMC BIOINFORMATICS. 18.
abstract
Background: Reconstructing transcript models from RNA-sequencing (RNA-seq) data and establishing these as independent transcriptional units can be a challenging task. Current state-of-the-art tools for long non-coding RNA (lncRNA) annotation are mainly based on evolutionary constraints, which may result in false negatives due to the overall limited conservation of lncRNAs. Results: To tackle this problem we have developed the Zipper plot, a novel visualization and analysis method that enables users to simultaneously interrogate thousands of human putative transcription start sites (TSSs) in relation to various features that are indicative for transcriptional activity. These include publicly available CAGE-sequencing, ChIP-sequencing and DNase-sequencing datasets. Our method only requires three tab-separated fields (chromosome, genomic coordinate of the TSS and strand) as input and generates a report that includes a detailed summary table, a Zipper plot and several statistics derived from this plot. Conclusion: Using the Zipper plot, we found evidence of transcription for a set of well-characterized lncRNAs and observed that fewer mono-exonic lncRNAs have CAGE peaks overlapping with their TSSs compared to multi-exonic lncRNAs. Using publicly available RNA-seq data, we found more than one hundred cases where junction reads connected protein-coding gene exons with a downstream mono-exonic lncRNA, revealing the need for a careful evaluation of lncRNA 5′-boundaries. Our method is implemented using the statistical programming language R and is freely available as a webtool.
Please use this url to cite or link to this publication:
author
organization
year
type
journalArticle (original)
publication status
published
subject
keyword
LONG NONCODING RNAS, INTEGRATIVE ANALYSIS, EVOLUTION, LANDSCAPE, UPDATE
journal title
BMC BIOINFORMATICS
BMC Bioinformatics
volume
18
article number
231
pages
9 pages
Web of Science type
Article
Web of Science id
000400615500004
ISSN
1471-2105
DOI
10.1186/s12859-017-1651-7
project
Bioinformatics: from nucleotids to networks (N2N)
language
English
UGent publication?
yes
classification
A1
additional info
the last two authors are equal contributors
copyright statement
Creative Commons Attribution 4.0 International Public License (CC-BY 4.0)
id
8519686
handle
http://hdl.handle.net/1854/LU-8519686
date created
2017-05-05 09:42:23
date last changed
2017-06-14 11:27:15
@article{8519686,
  abstract     = {Background: Reconstructing transcript models from RNA-sequencing (RNA-seq) data and establishing these as independent transcriptional units can be a challenging task. Current state-of-the-art tools for long non-coding RNA (lncRNA) annotation are mainly based on evolutionary constraints, which may result in false negatives due to the overall limited conservation of lncRNAs.
Results: To tackle this problem we have developed the Zipper plot, a novel visualization and analysis method that enables users to simultaneously interrogate thousands of human putative transcription start sites (TSSs) in relation to various features that are indicative for transcriptional activity. These include publicly available CAGE-sequencing, ChIP-sequencing and DNase-sequencing datasets. Our method only requires three tab-separated fields (chromosome, genomic coordinate of the TSS and strand) as input and generates a report that includes a detailed summary table, a Zipper plot and several statistics derived from this plot.
Conclusion: Using the Zipper plot, we found evidence of transcription for a set of well-characterized lncRNAs and observed that fewer mono-exonic lncRNAs have CAGE peaks overlapping with their TSSs compared to multi-exonic lncRNAs. Using publicly available RNA-seq data, we found more than one hundred cases where junction reads connected protein-coding gene exons with a downstream mono-exonic lncRNA, revealing the need for a careful evaluation of lncRNA 5{\textquotesingle}-boundaries. Our method is implemented using the statistical programming language R and is freely available as a webtool.},
  articleno    = {231},
  author       = {Avila Cobos, Francisco and Anckaert, Jasper and Volders, Pieter-Jan and Everaert, Celine and Rombaut, Dries and Vandesompele, Jo and De Preter, Katleen and Mestdagh, Pieter},
  issn         = {1471-2105},
  journal      = {BMC BIOINFORMATICS},
  keyword      = {LONG NONCODING RNAS,INTEGRATIVE ANALYSIS,EVOLUTION,LANDSCAPE,UPDATE},
  language     = {eng},
  pages        = {9},
  title        = {Zipper plot : visualizing transcriptional activity of genomic regions},
  url          = {http://dx.doi.org/10.1186/s12859-017-1651-7},
  volume       = {18},
  year         = {2017},
}

Chicago
Avila Cobos, Francisco, Jasper Anckaert, Pieter-Jan Volders, Celine Everaert, Dries Rombaut, Jo Vandesompele, Katleen De Preter, and Pieter Mestdagh. 2017. “Zipper Plot : Visualizing Transcriptional Activity of Genomic Regions.” Bmc Bioinformatics 18.
APA
Avila Cobos, F., Anckaert, J., Volders, P.-J., Everaert, C., Rombaut, D., Vandesompele, J., De Preter, K., et al. (2017). Zipper plot : visualizing transcriptional activity of genomic regions. BMC BIOINFORMATICS, 18.
Vancouver
1.
Avila Cobos F, Anckaert J, Volders P-J, Everaert C, Rombaut D, Vandesompele J, et al. Zipper plot : visualizing transcriptional activity of genomic regions. BMC BIOINFORMATICS. 2017;18.
MLA
Avila Cobos, Francisco, Jasper Anckaert, Pieter-Jan Volders, et al. “Zipper Plot : Visualizing Transcriptional Activity of Genomic Regions.” BMC BIOINFORMATICS 18 (2017): n. pag. Print.