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Innovations in -omics technologies have driven advances in biomedical research. However, integrating and analysing the large volumes of data generated from different high-throughput -omics technologies remain a significant challenge to basic and clinical scientists without bioinformatics skills or access to bioinformatics support. To address this demand, we have significantly updated our previous O-miner analytical suite, to incorporate several new features and data types to provide an efficient and easy-to-use Web tool for the automated analysis of data from ‘-omics’ technologies. Created from a biologist’s perspective, this tool allows for the automated analysis of large and complex transcriptomic, genomic and methylomic data sets, together with biological/clinical information, to identify significantly altered pathways and prioritize novel biomarkers/targets for biological validation. Our resource can be used to analyse both in-house data and the huge amount of publicly available information from array and sequencing platforms. Multiple data sets can be easily combined, allowing for meta-analyses. Here, we describe the analytical pipelines currently available in O-miner and present examples of use to demonstrate its utility and relevance in maximizing research output. O-miner Web server is free to use and is available at http://www.o-miner.org.

Declines in brain glucose metabolism have been described early in Alzheimer's disease (AD), and there is evidence that a genetic predisposition to AD contributes to accelerate this process. The epsilon 4 (e4) allele of the apolipoprotein E (ApoE) gene has been implicated as a major risk factor in this process. The aim of this FDG-PET study was to assess the ApoE e4 dose related effect on regional cerebral glucose metabolism (METglc) in clinical AD patients, with statistical voxel based methods.Eighty six consecutive mild to moderate AD patients included in the Network for Efficiency and Standardisation of Dementia Diagnosis database underwent FDG-PET scans at rest. PCR was used to determine the ApoE genotype. Patients were grouped as e4 non-carriers (n = 46), e3/e4 (n = 27) and e4/e4 (n = 13) carriers. A voxel-based mapping program was used to compare each AD subgroup with a database of 35 sex and age matched controls (p0.001, corrected for cluster extent) and also to compare between the subgroups (p0.001, uncorrected).No difference was found as to age at examination, age at onset, sex, disease duration, educational level, or severity of dementia between AD subgroups. Compared with controls, all AD subgroups had equivalent METglc reductions in the precuneus, posterior cingulate, parietotemporal, and frontal regions. Direct comparisons between AD subgroups indicated that patients with at least one e4 allele had METglc reductions within additional associative and limbic areas compared with e4 non-carriers.The present FDG-PET study showed different metabolic phenotypes related to the ApoE genotype in clinical AD patients, as revealed with voxel based statistical methods. The results suggest a generalised disorder in e4 carriers impairing metabolism globally, in addition to the more localised changes typical of AD patients.