Zipper plot: visualizing transcriptional activity of genomic regions

BMC Bioinformatics

Methodology Article

Celine Everaert¹  Jasper Anckaert¹  Pieter-Jan Volders¹  Dries Rombaut¹  Francisco Avila Cobos¹  Pieter Mestdagh¹  Jo Vandesompele¹  Katleen De Preter¹ 

[1] Center for Medical Genetics, Ghent University, De Pintelaan 185, Ghent, Belgium;Cancer Research Institute Ghent, De Pintelaan 185, Ghent, Belgium;Bioinformatics Institute Ghent from Nucleotides to Networks, De Pintelaan 185, Ghent, Belgium;

关键词: Transcription Start Site;    Genomic Feature;    Transcript Model;    lncRNA Transcript;    Close Peak;   

DOI  :  10.1186/s12859-017-1651-7

 received in 2017-02-04, accepted in 2017-04-25,  发布年份 2017

来源: Springer

【 摘 要 】

BackgroundReconstructing 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.ResultsTo 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.ConclusionUsing 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.

【 授权许可】

CC BY   
© The Author(s). 2017