↓ Skip to main content

Zipper plot: visualizing transcriptional activity of genomic regions

Overview of attention for article published in BMC Bioinformatics, May 2017
Altmetric Badge

About this Attention Score

  • In the top 25% of all research outputs scored by Altmetric
  • High Attention Score compared to outputs of the same age (85th percentile)
  • High Attention Score compared to outputs of the same age and source (91st percentile)

Mentioned by

twitter
26 tweeters

Citations

dimensions_citation
4 Dimensions

Readers on

mendeley
47 Mendeley
You are seeing a free-to-access but limited selection of the activity Altmetric has collected about this research output. Click here to find out more.
Title
Zipper plot: visualizing transcriptional activity of genomic regions
Published in
BMC Bioinformatics, May 2017
DOI 10.1186/s12859-017-1651-7
Pubmed ID
Authors

Francisco Avila Cobos, Jasper Anckaert, Pieter-Jan Volders, Celine Everaert, Dries Rombaut, Jo Vandesompele, Katleen De Preter, Pieter Mestdagh

Abstract

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.

Twitter Demographics

The data shown below were collected from the profiles of 26 tweeters who shared this research output. Click here to find out more about how the information was compiled.

Mendeley readers

The data shown below were compiled from readership statistics for 47 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
Turkey 1 2%
Denmark 1 2%
Unknown 45 96%

Demographic breakdown

Readers by professional status Count As %
Researcher 12 26%
Student > Ph. D. Student 11 23%
Student > Master 9 19%
Student > Bachelor 4 9%
Student > Doctoral Student 3 6%
Other 3 6%
Unknown 5 11%
Readers by discipline Count As %
Biochemistry, Genetics and Molecular Biology 22 47%
Agricultural and Biological Sciences 11 23%
Computer Science 3 6%
Engineering 3 6%
Medicine and Dentistry 2 4%
Other 0 0%
Unknown 6 13%

Attention Score in Context

This research output has an Altmetric Attention Score of 14. This is our high-level measure of the quality and quantity of online attention that it has received. This Attention Score, as well as the ranking and number of research outputs shown below, was calculated when the research output was last mentioned on 18 November 2017.
All research outputs
#1,301,839
of 14,624,569 outputs
Outputs from BMC Bioinformatics
#428
of 5,457 outputs
Outputs of similar age
#36,974
of 264,000 outputs
Outputs of similar age from BMC Bioinformatics
#1
of 12 outputs
Altmetric has tracked 14,624,569 research outputs across all sources so far. Compared to these this one has done particularly well and is in the 91st percentile: it's in the top 10% of all research outputs ever tracked by Altmetric.
So far Altmetric has tracked 5,457 research outputs from this source. They receive a mean Attention Score of 5.0. This one has done particularly well, scoring higher than 92% of its peers.
Older research outputs will score higher simply because they've had more time to accumulate mentions. To account for age we can compare this Altmetric Attention Score to the 264,000 tracked outputs that were published within six weeks on either side of this one in any source. This one has done well, scoring higher than 85% of its contemporaries.
We're also able to compare this research output to 12 others from the same source and published within six weeks on either side of this one. This one has done particularly well, scoring higher than 91% of its contemporaries.