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DGW: an exploratory data analysis tool for clustering and visualisation of epigenomic marks

Overview of attention for article published in BMC Bioinformatics, December 2016
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Title
DGW: an exploratory data analysis tool for clustering and visualisation of epigenomic marks
Published in
BMC Bioinformatics, December 2016
DOI 10.1186/s12859-016-1306-0
Pubmed ID
Authors

Saulius Lukauskas, Roberto Visintainer, Guido Sanguinetti, Gabriele B. Schweikert

Abstract

Functional genomic and epigenomic research relies fundamentally on sequencing based methods like ChIP-seq for the detection of DNA-protein interactions. These techniques return large, high dimensional data sets with visually complex structures, such as multi-modal peaks extended over large genomic regions. Current tools for visualisation and data exploration represent and leverage these complex features only to a limited extent. We present DGW, an open source software package for simultaneous alignment and clustering of multiple epigenomic marks. DGW uses Dynamic Time Warping to adaptively rescale and align genomic distances which allows to group regions of interest with similar shapes, thereby capturing the structure of epigenomic marks. We demonstrate the effectiveness of the approach in a simulation study and on a real epigenomic data set from the ENCODE project. Our results show that DGW automatically recognises and aligns important genomic features such as transcription start sites and splicing sites from histone marks. DGW is available as an open source Python package.

Twitter Demographics

The data shown below were collected from the profile of 1 tweeter 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 29 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
Unknown 29 100%

Demographic breakdown

Readers by professional status Count As %
Student > Ph. D. Student 7 24%
Student > Master 7 24%
Researcher 3 10%
Student > Bachelor 3 10%
Professor > Associate Professor 2 7%
Other 4 14%
Unknown 3 10%
Readers by discipline Count As %
Agricultural and Biological Sciences 9 31%
Computer Science 7 24%
Biochemistry, Genetics and Molecular Biology 6 21%
Engineering 2 7%
Medicine and Dentistry 1 3%
Other 1 3%
Unknown 3 10%

Attention Score in Context

This research output has an Altmetric Attention Score of 1. 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 07 February 2017.
All research outputs
#6,877,319
of 9,025,160 outputs
Outputs from BMC Bioinformatics
#3,216
of 3,869 outputs
Outputs of similar age
#221,883
of 309,744 outputs
Outputs of similar age from BMC Bioinformatics
#100
of 135 outputs
Altmetric has tracked 9,025,160 research outputs across all sources so far. This one is in the 13th percentile – i.e., 13% of other outputs scored the same or lower than it.
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We're also able to compare this research output to 135 others from the same source and published within six weeks on either side of this one. This one is in the 13th percentile – i.e., 13% of its contemporaries scored the same or lower than it.