SUMMARY: Biomine Explorer is a web application that enables interactive exploration of large heterogeneous biological networks constructed from selected publicly available biological knowledge sources. It is built on top of Biomine, a system which integrates cross-references from several biological databases into a large heterogeneous probabilistic network. Biomine Explorer offers user-friendly interfaces for search, visualization, exploration and manipulation as well as public and private storage of discovered subnetworks with permanent links suitable for inclusion into scientific publications. A JSON-based web API for network search queries is also available for advanced users. AVAILABILITY AND IMPLEMENTATION: Biomine Explorer is implemented as a web application, which is publicly available at https://biomine.ijs.si. Registration is not required but registered users can benefit from additional features such as private network repositories.

BACKGROUND: Quantitative molecular biology remains a challenge for researchers due to inconsistent approaches for control of errors in the final results. Due to several factors that can influence the final result, quantitative analysis and interpretation of qPCR data are still not trivial. Together with the development of high-throughput qPCR platforms, there is a need for a tool allowing for robust, reliable and fast nucleic acid quantification. RESULTS: We have developed "quantGenius" ( http://quantgenius.nib.si ), an open-access web application for a reliable qPCR-based quantification of nucleic acids. The quantGenius workflow interactively guides the user through data import, quality control (QC) and calculation steps. The input is machine- and chemistry-independent. Quantification is performed using the standard curve approach, with normalization to one or several reference genes. The special feature of the application is the implementation of user-guided QC-based decision support system, based on qPCR standards, that takes into account pipetting errors, assay amplification efficiencies, limits of detection and quantification of the assays as well as the control of PCR inhibition in individual samples. The intermediate calculations and final results are exportable in a data matrix suitable for further statistical analysis or visualization. We additionally compare the most important features of quantGenius with similar advanced software tools and illustrate the importance of proper QC system in the analysis of qPCR data in two use cases. CONCLUSIONS: To our knowledge, quantGenius is the only qPCR data analysis tool that integrates QC-based decision support and will help scientists to obtain reliable results which are the basis for biologically meaningful data interpretation.

BACKGROUND: With the increasing pace of new Genetically Modified Organisms (GMOs) authorized or in pipeline for commercialization worldwide, the task of the laboratories in charge to test the compliance of food, feed or seed samples with their relevant regulations became difficult and costly. Many of them have already adopted the so called "matrix approach" to rationalize the resources and efforts used to increase their efficiency within a limited budget. Most of the time, the "matrix approach" is implemented using limited information and some proprietary (if any) computational tool to efficiently use the available data. RESULTS: The developed GMOseek software is designed to support decision making in all the phases of routine GMO laboratory testing, including the interpretation of wet-lab results. The tool makes use of a tabulated matrix of GM events and their genetic elements, of the laboratory analysis history and the available information about the sample at hand. The tool uses an optimization approach to suggest the most suited screening assays for the given sample. The practical GMOseek user interface allows the user to customize the search for a cost-efficient combination of screening assays to be employed on a given sample. It further guides the user to select appropriate analyses to determine the presence of individual GM events in the analyzed sample, and it helps taking a final decision regarding the GMO composition in the sample. GMOseek can also be used to evaluate new, previously unused GMO screening targets and to estimate the profitability of developing new GMO screening methods. CONCLUSION: The presented freely available software tool offers the GMO testing laboratories the possibility to select combinations of assays (e.g. quantitative real-time PCR tests) needed for their task, by allowing the expert to express his/her preferences in terms of multiplexing and cost. The utility of GMOseek is exemplified by analyzing selected food, feed and seed samples from a national reference laboratory for GMO testing and by comparing its performance to existing tools which use the matrix approach. GMOseek proves superior when tested on real samples in terms of GMO coverage and cost efficiency of its screening strategies, including its capacity of simple interpretation of the testing results.

The paper presents an approach to the holistic analysis of transcriptomic data which integrates two state-of-the-art methodologies into a coherent framework. The aim of the proposed approach is to give insight into the discovered patterns, help explaining the observed phenomena, enable the creation of new research hypotheses and assist in design of new experiments. We have integrated a methodology for semantic analysis of transcriptomic data, a system for automated extraction of biological relations from the literature, and a number of supporting components. The approach is demonstrated and evaluated on a publicly available dataset from a clinical trial in acute lymphoblastic leukaemia and a document corpus of full-text articles from the PubMed Open Access Subset.

BACKGROUND: Since their first commercialization, the diversity of taxa and the genetic composition of transgene sequences in genetically modified plants (GMOs) are constantly increasing. To date, the detection of GMOs and derived products is commonly performed by PCR-based methods targeting specific DNA sequences introduced into the host genome. Information available regarding the GMOs' molecular characterization is dispersed and not appropriately organized. For this reason, GMO testing is very challenging and requires more complex screening strategies and decision making schemes, demanding in return the use of efficient bioinformatics tools relying on reliable information. DESCRIPTION: The GMOseek matrix was built as a comprehensive, online open-access tabulated database which provides a reliable, comprehensive and user-friendly overview of 328 GMO events and 247 different genetic elements (status: 18/07/2013). The GMOseek matrix is aiming to facilitate GMO detection from plant origin at different phases of the analysis. It assists in selecting the targets for a screening analysis, interpreting the screening results, checking the occurrence of a screening element in a group of selected GMOs, identifying gaps in the available pool of GMO detection methods, and designing a decision tree. The GMOseek matrix is an independent database with effective functionalities in a format facilitating transferability to other platforms. Data were collected from all available sources and experimentally tested where detection methods and certified reference materials (CRMs) were available. CONCLUSIONS: The GMOseek matrix is currently a unique and very valuable tool with reliable information on GMOs from plant origin and their present genetic elements that enables further development of appropriate strategies for GMO detection. It is flexible enough to be further updated with new information and integrated in different applications and platforms.

Subgroup discovery methods find interesting subsets of objects of a given class. Motivated by an application in bioinformatics, we first define a generalized subgroup discovery problem. In this setting, a subgroup is interesting if its members are characteristic for their class, even if the classes are not identical. Then we further refine this setting for the case where subsets of objects, for example, subsets of objects that represent different time points or different phenotypes, are contrasted. We show that this allows finding subgroups of objects that could not be found with classical subgroup discovery. To find such subgroups, we propose an approach that consists of two subgroup discovery steps and an intermediate, contrast set definition step. This approach is applicable in various application areas. An example is biology, where interesting subgroups of genes are searched by using gene expression data. We address the problem of finding enriched gene sets that are specific for virus infected samples for a specific time point or a specific phenotype. We report on experimental results on a time series data set for virus infected Solanum tuberosum (potato) plants. The results on S. tuberosum’s response to virus infection revealed new research hypotheses for plant biologists.

BACKGROUND: In experimental data analysis, bioinformatics researchers increasingly rely on tools that enable the composition and reuse of scientific workflows. The utility of current bioinformatics workflow environments can be significantly increased by offering advanced data mining services as workflow components. Such services can support, for instance, knowledge discovery from diverse distributed data and knowledge sources (such as GO, KEGG, PubMed, and experimental databases). Specifically, cutting-edge data analysis approaches, such as semantic data mining, link discovery, and visualization, have not yet been made available to researchers investigating complex biological datasets. RESULTS: We present a new methodology, SegMine, for semantic analysis of microarray data by exploiting general biological knowledge, and a new workflow environment, Orange4WS, with integrated support for web services in which the SegMine methodology is implemented. The SegMine methodology consists of two main steps. First, the semantic subgroup discovery algorithm is used to construct elaborate rules that identify enriched gene sets. Then, a link discovery service is used for the creation and visualization of new biological hypotheses. The utility of SegMine, implemented as a set of workflows in Orange4WS, is demonstrated in two microarray data analysis applications. In the analysis of senescence in human stem cells, the use of SegMine resulted in three novel research hypotheses that could improve understanding of the underlying mechanisms of senescence and identification of candidate marker genes. CONCLUSIONS: Compared to the available data analysis systems, SegMine offers improved hypothesis generation and data interpretation for bioinformatics in an easy-to-use integrated workflow environment.