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SummaryThe rapid increase of ~omics datasets generated by microarray, mass spectrometry and next generation sequencing technologies requires an integrated platform that can combine results from different ~omics datasets to provide novel insights in the understanding of biological systems. MADMAX is designed to provide a solution for storage and analysis of complex ~omics datasets. In addition, analysis results (such as lists of genes) can be merged to reveal candidate genes supported by all datasets. The system constitutes an ISA-Tab compliant LIMS part, which is linked to the different analysis pipelines. A pilot study of different type of ~omics data in Brassica rapa demonstrates the possible use of MADMAX. The web-based user interface provides easy access to data and analysis tools on top of the database. IntroductionTo better understand how phenotypes emerge, increasingly series of ~omics technologies (genomics, transcriptomics, proteomics, metabolomics) rather than individual measurements are necessarily used within a single study. Such efforts boost the demands of both data storage and data analysis of different high-throughput approaches. However, in the past it was hardly possible to store metadata from different ~omics technologies in the same repository. To accommodate this demand the ISA-Tab [1] format was proposed to build up a common structured representation of the metadata of studies from a combination of technologies. This also triggered attempts to develop data processing tools tailored to the needs of biologists. Unfortunately most of these tools have high demands on hardware requirements, or contain non-intuitive command line-based interfaces.* To whom correspondence should be addressed: jack.leunissen@wur.nl Here we present MADMAX, a multi-purpose database for the management and analysis of data from multiple ~omics experiments. It includes an ISA-Tab compliant backend database and a series of analysis pipelines for transcriptomics, metabolomics and genomics datasets; these pipelines are connected to the database through webservices such that other pipelines can be easily integrated into the current system ( Figure 1 Through the web interface, the user can store a complete experiment with all fields required in ISA-Tab format, sufficient to allow for subsequent analysis or even repeating the experiment later. Another section on the website is the central access to different analysis pipelines. Both individual analysis results and combined gene lists can be retrieved in the system for download. Centrally stored experiments and analysis results can only be accessed by the creator by default and will be accessible for other users only if the creator desires to share the data. The system is on an automatic backup schedule.MADMAX can be reached at http://madmax2.bioinformatics.nl/ and is available upon request by sending an email to madmax.request@bioinformatics.nl. ImplementationMADMAX is built upon an Oracle relational database on a Linux server and a computational analysis engine for different...

Various models and datasets related to aflatoxins in the maize and dairy production chain have been developed and used but they have not yet been linked with each other. This study aimed to investigate the impacts of climate change on aflatoxin B 1 production in maize and its consequences on aflatoxin M 1 contamination in dairy cow’s milk, using a full chain modelling approach. To this end, available models and input data were chained together in a modelling framework. As a case study, we focused on maize grown in Eastern Europe and imported to the Netherlands to be fed–as part of dairy cows’ compound feed–to dairy cows in the Netherlands. Three different climate models, one aflatoxin B 1 prediction model and five different carryover models were used. For this particular case study of East European maize, most of the calculations suggest an increase (up to 50%) of maximum mean aflatoxin M 1 in milk by 2030, except for one climate (DMI) model suggesting a decrease. Results from all combinations of carryover and climate models suggest a similar or slight increase (up to 0.6%) of the chance of finding aflatoxin M 1 in milk above the EC limit of 0.05 μg/kg by 2030. Results varied mainly with the climate model data and carryover model considered. The model framework infrastructure is flexible so that forecasting models for other mycotoxins or other food safety hazards as well as other production chains, together with necessary input databases, can easily be included as well. This modelling framework for the first time links datasets and models related to aflatoxin B 1 in maize and related aflatoxin M 1 the dairy production chain to obtain a unique predictive methodology based on Monte Carlo simulation. Such an integrated approach with scenario analysis provides possibilities for policy makers and risk managers to study the effects of changes in the beginning of the chain on the end product.

Data in the life sciences are extremely diverse and are stored in a broad spectrum of repositories ranging from those designed for particular data types (such as KEGG for pathway data or UniProt for protein data) to those that are general-purpose (such as FigShare, Zenodo, Dataverse or EUDAT). These data have widely different levels of sensitivity and security considerations. For example, clinical observations about genetic mutations in patients are highly sensitive, while observations of species diversity are generally not. The lack of uniformity in data models from one repository to another, and in the richness and availability of metadata descriptions, makes integration and analysis of these data a manual, time-consuming task with no scalability. Here we explore a set of resource-oriented Web design patterns for data discovery, accessibility, transformation, and integration that can be implemented by any general-or specialpurpose repository as a means to assist users in finding and reusing their data holdings. We show that by using off-the-shelf technologies, interoperability can be achieved atthe level of an individual spreadsheet cell. We note that the behaviours of this architecture compare favourably to the desiderata defined by the FAIR Data Principles, and can therefore represent an exemplar implementation of those principles. The proposed interoperability design patterns may be used to improve discovery and integration of both new and legacy data, maximizing the utility of all scholarly outputs.

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