VizStruct for visualization of genome-wide SNP analyses.
BACKGROUND
The size, dimensionality and the limited range of the data values make visualization of single nucleotide polymorphism (SNP) datasets challenging. The purpose of this study is to evaluate the usefulness of 3D VizStruct, a novel multi-dimensional data visualization technique for analyzing patterns in SNP datasets.


RESULTS
VizStruct is an interactive visualization technique that reduces multi-dimensional data to two dimensions using the complex-valued harmonics of the discrete Fourier transform (DFT). In the 3D VizStruct extension, the multi-dimensional SNP data vectors are reduced to three dimensions using a combination of the DFT and the Kullback-Leibler divergence. The performance of 3D VizStruct was challenged with several biologically relevant published datasets that included human Chromosome 21, the human lipoprotein lipase (LPL) gene locus and the multi-locus genotypes of coral populations. In every case, the 3D VizStruct mapping provided an intuitive visual description of the key characteristics of the underlying multi-dimensional genotype.
Bhasi
Kavitha
K
Department of Pharmaceutical Sciences, State University of New York Buffalo, NY 14260, USA.
Zhang
Li
L
Brazeau
Daniel
D
Zhang
Aidong
A
Ramanathan
Murali
M
D000818:Animals; D037421:Anthozoa; D002891:Chromosomes, Human, Pair 21 / Q000235:genetics; D019295:Computational Biology / Q000379:methods*; D005583:Fourier Analysis; D016678:Genome; D005838:Genotype; D006239:Haplotypes; D006801:Humans; D021621:Imaging, Three-Dimensional; D008071:Lipoprotein Lipase / Q000235:genetics*; D015233:Models, Statistical; D020641:Polymorphism, Single Nucleotide; D012984:Software
D008071:Lipoprotein Lipase