RESEARCH ARTICLE ClusTrack: Feature Extraction and Similarity Measures for Clustering of Genome-Wide Data Sets

Clustering is a popular technique for explorative analysis of data, as it can reveal subgroup-ings and similarities between data in an unsupervised manner. While clustering is routinely applied to gene expression data, there is a lack of appropriate general methodology for clus-tering of sequence-level genomic and epigenomic data, e.g. ChIP-based data. We here in-troduce a general methodology for clustering data sets of coordinates relative to a genome assembly, i.e. genomic tracks. By defining appropriate feature extraction approaches and similarity measures, we allow biologically meaningful clustering to be performed for geno-mic tracks using standard clustering algorithms. An implementation of the methodology is provided through a tool, ClusTrack, which allows fine-tuned clustering analyses to be speci-fied through a web-based interface. We apply our methods to the clustering of occupancy of the H3K4me1 histone modification in samples from a range of different cell types. The ma-jority of samples form meaningful subclusters, confirming that the definitions of features and similarity capture biological, rather than technical, variation between the genomic tracks. Input data and results are available, and can be reproduced, through a Galaxy Pages docu-ment a