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

Saulius Lukauskas (Lead / Corresponding author), Roberto Visintainer, Guido Sanguinetti, Gabriele Schweikert

Research output: Contribution to journalArticlepeer-review

6 Citations (Scopus)


Background: 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.
Results: 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.
Conclusions: 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.
Original languageEnglish
Article number447
Pages (from-to)53-63
Number of pages11
JournalBMC Bioinformatics
Issue numberSupl 16
Publication statusPublished - 13 Dec 2016
EventTenth International Workshop on Machine Learning in Systems Biology (MLSB 2016) - The Hague, Netherlands
Duration: 3 Sept 20164 Sept 2016


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