title: Genesis: cluster analysis of microarray data.
abstract: A versatile, platform independent and easy to use Java suite for large-scale gene expression analysis was developed. Genesis integrates various tools for microarray data analysis such as filters, normalization and visualization tools, distance measures as well as common clustering algorithms including hierarchical clustering, self-organizing maps, k-means, principal component analysis, and support vector machines. The results of the clustering are transparent across all implemented methods and enable the analysis of the outcome of different algorithms and parameters. Additionally, mapping of gene expression data onto chromosomal sequences was implemented to enhance promoter analysis and investigation of transcriptional control mechanisms.
authors.affiliation: Institute of Biomedical Engineering, Graz University of Technology, Krenngasse 37, 8010 Graz, Austria.
authors.forename: Alexander
authors.initials: A
authors.lastname: Sturn
authors.forename: John
authors.initials: J
authors.lastname: Quackenbush
authors.forename: Zlatko
authors.initials: Z
authors.lastname: Trajanoski
mesh_terms: D000465:Algorithms; D016000:Cluster Analysis; D019295:Computational Biology; D020869:Gene Expression Profiling / Q000706:statistics & numerical data*; D020411:Oligonucleotide Array Sequence Analysis / Q000706:statistics & numerical data*; D025341:Principal Component Analysis; D011381:Programming Languages; D011401:Promoter Regions, Genetic; D012984:Software; D014158:Transcription, Genetic