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:
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.
