In transcriptome experiments, the experimental conditions (e.g. mutants and/or treatments) cause transcriptional changes. Identifying experimental conditions that induce similar or opposite transcriptional changes can be useful to identify experimental conditions that affect the same biological process. AtCAST (http://atpbsmd.yokohama-cu.ac.jp) is a web-based tool to analyze the relationship between experimental conditions among transcriptome data. Users can analyze ‘user’s transcriptome data’ of a new mutant or a new chemical compound whose function remains unknown to generate novel biological hypotheses. This tool also allows for mining of related ‘experimental conditions’ from the public microarray data, which are pre-included in AtCAST. This tool extracts a set of genes (i.e. module) that show significant transcriptional changes and generates a network graph to present related transcriptome data. The updated AtCAST now contains data on >7,000 microarrays, including experiments on various stresses, mutants and chemical treatments. Gene ontology term enrichment (GOE) analysis is introduced to assist the characterization of transcriptome data. The new AtCAST supports input from multiple platforms, including the ‘Arabisopsis gene 1.1 ST array’, a new microarray chip from Affymetrix and RNA sequencing (RNA-seq) data obtained using next-generation sequencing (NGS). As a pilot study, we conducted microarray analysis of Arabidopsis under auxin treatment using the new Affymetrix chip, and then analyzed the data in AtCAST. We also analyzed RNA-seq data of the pifq mutant using AtCAST. These new features will facilitate analysis of associations between transcriptome data obtained using different platforms.