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PeerJ. 2016 Jul 19;4:e2209. doi: 10.7717/peerj.2209. eCollection 2016.

fluff: exploratory analysis and visualization of high-throughput sequencing data.

Author information

1
Radboud University, Molecular Developmental Biology , Nijmegen , The Netherlands.

Abstract

In this article we describe fluff, a software package that allows for simple exploration, clustering and visualization of high-throughput sequencing data mapped to a reference genome. The package contains three command-line tools to generate publication-quality figures in an uncomplicated manner using sensible defaults. Genome-wide data can be aggregated, clustered and visualized in a heatmap, according to different clustering methods. This includes a predefined setting to identify dynamic clusters between different conditions or developmental stages. Alternatively, clustered data can be visualized in a bandplot. Finally, fluff includes a tool to generate genomic profiles. As command-line tools, the fluff programs can easily be integrated into standard analysis pipelines. The installation is straightforward and documentation is available at http://fluff.readthedocs.org.

AVAILABILITY:

fluff is implemented in Python and runs on Linux. The source code is freely available for download at https://github.com/simonvh/fluff.

KEYWORDS:

ChIP-seq; Clustering; High-throughput sequencing; Next-generation sequencing; Python; Visualization

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