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

PeerJ

article

Georgios Georgiou¹  Simon J. van Heeringen¹ 

[1] Radboud University, Molecular Developmental Biology

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

DOI  :  10.7717/peerj.2209

学科分类：社会科学、人文和艺术（综合）

来源: Inra

【 摘 要 】

Summary. 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.

【 授权许可】

CC BY