ArticleBMC bioinformatics2016
DGW: an exploratory data analysis tool for clustering and visualisation of epigenomic marks.
Article in BMC bioinformatics, 2016. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 3 papers.
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Who cites it
3 citing papers in PubMed.
- DNA methylation analysis in plants: review of computational tools and future perspectives.Briefings in bioinformatics · 2020Review
- HebbPlot: an intelligent tool for learning and visualizing chromatin mark signatures.BMC bioinformatics · 2018Article
- Selected proceedings of Machine Learning in Systems Biology: MLSB 2016.BMC bioinformatics · 2016Article
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4 authors.
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Abstract
backgroundFunctional 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.
resultsWe 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.
conclusionsOur 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.
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