BackgroundThe 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.ResultsChipster (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.ConclusionsChipster 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.

Background: A growing amount of scientific evidence suggests that microbes are involved in the aetiology of irritable bowel syndrome (IBS), and the gastrointestinal (GI) microbiota of individuals suffering from diarrhoea-predominant IBS (IBS-D) is distinguishable from other IBS-subtypes. In our study, the GI microbiota of IBS-D patients was evaluated and compared with healthy controls (HC) by using a high-resolution sequencing method. The method allowed microbial community analysis on all levels of microbial genomic guanine plus cytosine (G+C) content, including high G+C bacteria.

The human intestine is colonised with highly diverse and individually defined microbiota, which likely has an impact on the host well-being. Drivers of the individual variation in the microbiota compositions are multifactorial and include environmental, host and dietary factors. We studied the impact of the host secretor status, encoded by fucosyltransferase 2 (FUT2) -gene, on the intestinal microbiota composition. Secretor status determines the expression of the ABH and Lewis histo-blood group antigens in the intestinal mucosa. The study population was comprised of 14 non-secretor (FUT2 rs601338 genotype AA) and 57 secretor (genotypes GG and AG) adult individuals of western European descent. Intestinal microbiota was analyzed by PCR-DGGE and for a subset of 12 non-secretor subjects and 12 secretor subjects additionally by the 16S rRNA gene pyrosequencing and the HITChip phylogenetic microarray analysis. All three methods showed distinct clustering of the intestinal microbiota and significant differences in abundances of several taxa representing dominant microbiota between the non-secretors and the secretors as well as between the FUT2 genotypes. In addition, the non-secretors had lower species richness than the secretors. The soft clustering of microbiota into enterotypes (ET) 1 and 3 showed that the non-secretors had a higher probability of belonging to ET1 and the secretors to ET3. Our study shows that secretor status and FUT2 polymorphism are associated with the composition of human intestinal microbiota, and appears thus to be one of the key drivers affecting the individual variation of human intestinal microbiota.