Chipster: user-friendly analysis software for microarray and other high-throughput data.

BACKGROUND: The growth of high-throughput technologies such as microarrays and next generation sequencing has been accompanied by active research in data analysis methodology, producing new analysis methods at a rapid pace. While most of the newly developed methods are freely available, their use requires substantial computational skills. In order to enable non-programming biologists to benefit from the method development in a timely manner, we have created the Chipster software. RESULTS: Chipster (http://chipster.csc.fi/) brings a powerful collection of data analysis methods within the reach of bioscientists via its intuitive graphical user interface. Users can analyze and integrate different data types such as gene expression, miRNA and aCGH. The analysis functionality is complemented with rich interactive visualizations, allowing users to select datapoints and create new gene lists based on these selections. Importantly, users can save the performed analysis steps as reusable, automatic workflows, which can also be shared with other users. Being a versatile and easily extendable platform, Chipster can be used for microarray, proteomics and sequencing data. In this article we describe its comprehensive collection of analysis and visualization tools for microarray data using three case studies. CONCLUSIONS: Chipster is a user-friendly analysis software for high-throughput data. Its intuitive graphical user interface enables biologists to access a powerful collection of data analysis and integration tools, and to visualize data interactively. Users can collaborate by sharing analysis sessions and workflows. Chipster is open source, and the server installation package is freely available.

Influence of GSTM1, GSTT1, GSTP1, NAT1, NAT2, EPHX1, MTR and MTHFR polymorphism on chromosomal aberration frequencies in human lymphocytes.

We have studied the influence of genetic polymorphisms in the xenobiotic-metabolizing genes GSTM1, GSTP1, GSTT1, EPHX1, NAT1 and NAT2 and the folate-metabolizing genes MTR and MTHFR on the frequencies of cells with chromosomal aberrations (CAs) in peripheral lymphocytes of Norwegian men. Log-linear Poisson regression models were applied on 357 subjects of whom data on all the polymorphisms examined were available. Total CAs and chromosome-type aberrations (CSAs) were significantly increased by higher age alone, whereas chromatid-type aberrations (CTAs) were elevated by the GSTT1-null genotype and MTHFR codon 222 variant allele and chromatid gaps (CTGs) by EPHX1 high activity genotype and occupational exposure. Stratification by smoking and age (<40 and ≥40 years) showed that the effect of the GSTT1 null and EPHX1 high activity genotypes only concerned (older) smokers, in agreement with the roles of the respective enzymes in detoxification and metabolic activation. The MTHFR codon 222 variant allele was associated with high CTGs in smokers, the MTR codon 919 variant allele with high CTAs in older smokers and the NAT2 fast acetylator genotype with high CTGs in older subjects. Among younger nonsmokers, however, carriers of the MTHFR codon 222 and MTR codon 919 variant alleles showed a decrease in the level of CTGs and total CAs, respectively. In conclusion, polymorphisms of GSTT1, EPHX1, MTHFR, MTR and NAT2 differentially affect the frequency of CTAs, CSAs and CTGs, showing interaction with smoking and age. It appears that CA subtypes rather than total CAs should be considered in this type of studies.