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HilbertVis: Visualization of genomic data with the Hilbert curve


Sub-pages: Gallery of sample images Download stand-alone version Download R packages

Author

Simon Anders, sanders@fs.tum.de
(Huber group, EMBL-EBI)

Overview

This tool allows to display very long data vectors in a space-efficient manner, allowing the user to visually judge the large scale structure and distribution of features simultaenously with the rough shape and intensity of individual features. A typical use case is ChIP-Chip and ChIP-Seq, or basically all the kinds of genomic data, that are conventionally displayed as quantitative track ("wiggle data") in genome browsers such as those provided by Ensemble or UCSC.

Here are some examples of ChIP-Seq data visualized with the Hilbert curve:

Publication

For an explanation on how to interprete these, see the following paper:

S. Anders: “Visualisation of genomic data with the Hilbert curve”,
    Bioinformatics, Vol. 25 (2009) pp. 1231-1235 (open access, i.e., free full text)

If you use HilbertVis for scientific work, please cite this publication.

Gallery

⇒⇒⇒ For more pictures please have a look at the Gallery. ⇐⇐⇐

Graphical user interface

HilbertVis allows to produce and explore such plots with an interactive GUI.

(Click on the picture to the left for a full-size view of the GUI)

Usage options

HilbertVis comes in two variants:
  • as a stand-alone application, that can read in and display data in the file formats GFF, BED, Wiggle, and Maq map (see below for details). This variant is easy to use and meant for the typical user.
  • as a set of two packages for the statistical environment "R", typically (but not necessarily) used in the framework of the Bioconductor project. Instead of loading the data with HilbertVis, you can use the facilities of R and Bioconductor to load, preprocess, and manipulate any kind of data and then hand over the data vectors to HilbertVis for display. The R package is very flexible but only of use for users familiar with R. HilbertVis is split in two packages, one that provides the GUI, and one that provides non-interactive functions for batch production of Hilbert plots.
Both variants are available for Linux, Mac OS X, Microsoft Windows, and as source code.

Download and installation

   • For the stand-alone version (easy to use, for users unfamiliar with R), please click here:
   • For more information on the R packages, please follow this link:

Please, if you have trouble with installation, do not simply give up but send me an e-mail: sanders@fs.tum.de

If you would like to stay informed about new releases of HilbertVis, please subscribe to the (low-volume) HilbertVis announcements mailing list.

Documentation

Stand-alone version

R package

“Visualising very long data vectors with the Hilbert curve — Description of the Bioconductor packages HilbertVis and HilbertVisGUI” (distributed with the HilbertVis package)
“Processing and Visualisation of High-Throughput Sequencing Data with ShortRead and HilbertVis” (distributed with the ShortRead package)

Acknowledgment

The development of this open-source software was funded by the European Union's Marie Curie Research and Training Network Chromatin Plasticity.


Simon Anders, EMBL-EBI, sanders@fs.tum.de; last change: 2009-02-16

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