Genesis is a versatile, platform-independent Java suite for large-scale gene expression analysis. It integrates various tools for microarray data analysis, including filters, normalization, visualization, distance measures, and common clustering algorithms such as hierarchical clustering, self-organizing maps, k-means, principal component analysis, and support vector machines. The clustering results are transparent across all implemented methods, enabling the analysis of outcomes from different algorithms and parameters. Additionally, gene expression data can be mapped onto chromosomal sequences to enhance promoter analysis and investigation of transcriptional control mechanisms. Genesis allows the visualization and analysis of a whole set of gene expression experiments. After reading data from tab-delimited flat files, several graphical representations of measured signal intensities can be generated, showing a matrix of genes (rows) and experiments (columns). Various filters can be applied to extract genes of interest. Fluorescence ratios can be normalized in several ways to choose the appropriate representation for further analysis. Eleven different similarity distance measurements have been implemented, ranging from simple Pearson correlation or Euclidean distance to more sophisticated approaches like mutual information or Spearman's rank correlation coefficients. Common clustering algorithms have been implemented: hierarchical clustering, k-means, self-organizing maps, principal component analysis, and support vector machines. The tool provides transparency in clustering analysis across all implemented methods. The outcome of clustering can differ substantially due to underlying assumptions in each clustering technique and the necessity to adjust various parameters. Therefore, it is imperative to apply several clustering techniques and parameter values on the same dataset to illuminate different relationships between the data. Genesis also provides the ability to map gene expression data onto chromosomal sequences to enhance the investigation of regulatory mechanisms. The software was extensively tested on various platforms, including Windows 2000, Linux, Thru64 Unix, Solaris, and Irix. This work was supported by the Austrian Ministry for Transport, Innovation and Technology, the Jubiläumsfonds der Österreichischen Nationalbank, and the Austrian Science Fund.Genesis is a versatile, platform-independent Java suite for large-scale gene expression analysis. It integrates various tools for microarray data analysis, including filters, normalization, visualization, distance measures, and common clustering algorithms such as hierarchical clustering, self-organizing maps, k-means, principal component analysis, and support vector machines. The clustering results are transparent across all implemented methods, enabling the analysis of outcomes from different algorithms and parameters. Additionally, gene expression data can be mapped onto chromosomal sequences to enhance promoter analysis and investigation of transcriptional control mechanisms. Genesis allows the visualization and analysis of a whole set of gene expression experiments. After reading data from tab-delimited flat files, several graphical representations of measured signal intensities can be generated, showing a matrix of genes (rows) and experiments (columns). Various filters can be applied to extract genes of interest. Fluorescence ratios can be normalized in several ways to choose the appropriate representation for further analysis. Eleven different similarity distance measurements have been implemented, ranging from simple Pearson correlation or Euclidean distance to more sophisticated approaches like mutual information or Spearman's rank correlation coefficients. Common clustering algorithms have been implemented: hierarchical clustering, k-means, self-organizing maps, principal component analysis, and support vector machines. The tool provides transparency in clustering analysis across all implemented methods. The outcome of clustering can differ substantially due to underlying assumptions in each clustering technique and the necessity to adjust various parameters. Therefore, it is imperative to apply several clustering techniques and parameter values on the same dataset to illuminate different relationships between the data. Genesis also provides the ability to map gene expression data onto chromosomal sequences to enhance the investigation of regulatory mechanisms. The software was extensively tested on various platforms, including Windows 2000, Linux, Thru64 Unix, Solaris, and Irix. This work was supported by the Austrian Ministry for Transport, Innovation and Technology, the Jubiläumsfonds der Österreichischen Nationalbank, and the Austrian Science Fund.