REEF: searching REgionally Enriched Features in genomes
REEF is a tool that uses a genome scan method in order to find chromosomal regions in which a specific features class is enriched. A feature class may be based on whatever sequence characteristics. For instance, it may consist of a class of genes, defined by analysis of gene expression or by involvement of gene products in specific pathways or interaction groups.
REEF is a software written in the Python programming language and uses the wxPython graphical toolkit in order to provide an easy to use graphical interface. It can be freely downloaded. Multi-platform source code package as well as a binary packages for Windows are available, see the documentation page for detailed installation and usage instructions.
REEF is aimed at identifying regions of a genome enriched in specific features, as compared with a reference landscape of features density. It takes as input a list of reference features (RF, e.g human genes) mapped on a genome sequence, a list of selected features (SF) among the RF (e.g. human genes specifically expressed in a given tissue) with their genomic positions and the number and the length of the chromosomes in the genome under consideration. It scans the genome using a sliding window approach, and calculates the statistical significance of each windows using the Hypergeometric Distribution and the False Discovery Rate. Consecutive significant windows form a cluster of regional enriched features. Results can be viewed as plots (see the screenshots page) or dumped to text file for further analysis. The program also allows the user to display the results using the Custom Annotation Tracks facility from the UCSC Genome Browser.
Citation
Coppe A., Danieli G.A. and Bortoluzzi S. REEF: searching REgionally Enriched Features in genomes. BMC Bioinformatics 2006, 7:453
Credits
REEF was conceived by Alessandro Coppe and Stefania Bortoluzzi and coded by Alessandro Coppe at the Bioinformatic Group of the Human Genetics Laboratory, Biology Department, Padova University. For suggestions and comments please email: ale@telethon.bio.unipd.it