Willows: a memory efficient tree and forest construction package.
BACKGROUND
Existing tree and forest methods are powerful bioinformatics tools to explore high dimensional data including high throughput genomic data. However, they cannot deal with the data generated by recent genotyping platforms for single nucleotide polymorphisms due to the massive size of the data and its excessive memory demand.


RESULTS
Using the recursive partitioning technique, we developed a new software package, Willows, to maximize the utility of the computer memory and make it feasible to analyze massive genotype data. This package includes three tree-based methods -- classification tree, random forest, and deterministic forest, and can efficiently handle the massive amount of SNP data. In addition, this package can easily set different options (e.g., algorithms and specifications) and predict the class of test samples.


CONCLUSIONS
We developed Willows in a user friendly interface with the goal of maximizing the use of memory, which is critical for analysis of genomic data. The Willows package is well documented and publicly available at (http://c2s2.yale.edu/software/Willows).
Department of Epidemiology and Public Health, Yale University School of Medicine, New Haven, CT 06520-8034, USA. heping.zhang@yale.edu
Heping
H
Zhang
Minghui
M
Wang
Xiang
X
Chen
D019295:Computational Biology / Q000379:methods*; D055106:Genome-Wide Association Study; D020641:Polymorphism, Single Nucleotide / Q000235:genetics*; D012984:Software