MOTIVATION: The field of toxicogenomics (the application of '-omics' technologies to risk assessment of compound toxicities) has expanded in the last decade, partly driven by new legislation, aimed at reducing animal testing in chemical risk assessment but mainly as a result of a paradigm change in toxicology towards the use and integration of genome wide data. Many research groups worldwide have generated large amounts of such toxicogenomics data. However, there is no centralized repository for archiving and making these data and associated tools for their analysis easily available. RESULTS: The Data Infrastructure for Chemical Safety Assessment (diXa) is a robust and sustainable infrastructure storing toxicogenomics data. A central data warehouse is connected to a portal with links to chemical information and molecular and phenotype data. diXa is publicly available through a user-friendly web interface. New data can be readily deposited into diXa using guidelines and templates available online. Analysis descriptions and tools for interrogating the data are available via the diXa portal. AVAILABILITY AND IMPLEMENTATION: http://www.dixa-fp7.eu CONTACT: [email protected]; [email protected] SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.

MOTIVATION: Comparing time courses of gene expression with time courses of phenotypic data may provide new insights in cellular mechanisms. In this study, we compared the performance of five pattern recognition methods with respect to their ability to relate genes and phenotypic data: one classical method (k-means) and four methods especially developed for time series [Short Time-series Expression Miner (STEM), Linear Mixed Model mixtures, Dynamic Time Warping for -Omics and linear modeling with R/Bioconductor limma package]. The methods were evaluated using data available from toxicological studies that had the aim to relate gene expression with phenotypic endpoints (i.e. to develop biomarkers for adverse outcomes). Additionally, technical aspects (influence of noise, number of time points and number of replicates) were evaluated on simulated data. RESULTS: None of the methods outperforms the others in terms of biology. Linear modeling with limma is mostly influenced by noise. STEM is mostly influenced by the number of biological replicates in the dataset, whereas k-means and linear modeling with limma are mostly influenced by the number of time points. In most cases, the results of the methods complement each other. We therefore provide recommendations to integrate the five methods. AVAILABILITY: The Matlab code for the simulations performed in this research is available in the Supplementary Data (Word file). The microarray data analysed in this paper are available at ArrayExpress (E-TOXM-22 and E-TOXM-23) and Gene Expression Omnibus (GSE39291). The phenotypic data are available in the Supplementary Data (Excel file). Links to the pattern recognition tools compared in this paper are provided in the main text. CONTACT: [email protected] SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.