Zipper plot: visualizing transcriptional activity of genomic regions

• Published in: BMC bioinformatics Vol. 18; no. 1; p. 231
• Main Authors: Avila Cobos, Francisco, Anckaert, Jasper, Volders, Pieter-Jan, Everaert, Celine, Rombaut, Dries, Vandesompele, Jo, De Preter, Katleen, Mestdagh, Pieter
• Format: Journal Article
• Language: English
• Published: England BioMed Central 02.05.2017; BMC
• Subjects: Chromatin Immunoprecipitation; Close Peak; Genomic Feature; Genomics - methods; Humans; lncRNA Transcript; Methodology; RNA, Long Noncoding - genetics; Sequence Analysis, RNA - methods; Software; Transcript Model; Transcription Initiation Site; Transcription Start Site
• ISSN: 1471-2105; 1471-2105
• DOI: 10.1186/s12859-017-1651-7

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. 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. 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.