BIFI: a Taverna plugin for a simplified and user-friendly workflow platform.

BACKGROUND: Heterogeneity in the features, input-output behaviour and user interface for available bioinformatics tools and services is still a bottleneck for both expert and non-expert users. Advancement in providing common interfaces over such tools and services are gaining interest among researchers. However, the lack of (meta-) information about input-output data and parameter prevents to provide automated and standardized solutions, which can assist users in setting the appropriate parameters. These limitations must be resolved especially in the workflow-based solution in order to ease the integration of software. FINDINGS: We report a Taverna Workbench plugin: the XworX BIFI (Beautiful Interfaces for Inputs) implemented as a solution for the aforementioned issues. BIFI provides a Graphical User Interface (GUI) definition language used to layout the user interface and to define parameter options for Taverna workflows. BIFI is also able to submit GUI Definition Files (GDF) directly or discover appropriate instances from a configured repository. In the absence of a GDF, BIFI generates a default interface. CONCLUSION: The Taverna Workbench is an open source software providing the ability to combine various services within a workflow. Nevertheless, users can supply input data to the workflow via a simple user interface providing only a text area to enter the input in text form. The workflow may contain meta-information in human readable form such as description text for the port and an example value. However, not all workflow ports are documented so well or have all the required information.BIFI uses custom user interface components for ports which give users feedback on the parameter data type or structure to be used for service execution and enables client-side data validations. Moreover, BIFI offers user interfaces that allow users to interactively construct workflow views and share them with the community, thus significantly increasing usability of heterogeneous, distributed service consumption.

Monitoring of technical variation in quantitative high-throughput datasets.

High-dimensional datasets can be confounded by variation from technical sources, such as batches. Undetected batch effects can have severe consequences for the validity of a study's conclusion(s). We evaluate high-throughput RNAseq and miRNAseq as well as DNA methylation and gene expression microarray datasets, mainly from the Cancer Genome Atlas (TCGA) project, in respect to technical and biological annotations. We observe technical bias in these datasets and discuss corrective interventions. We then suggest a general procedure to control study design, detect technical bias using linear regression of principal components, correct for batch effects, and re-evaluate principal components. This procedure is implemented in the R package swamp, and as graphical user interface software. In conclusion, high-throughput platforms that generate continuous measurements are sensitive to various forms of technical bias. For such data, monitoring of technical variation is an important analysis step.

RGG: a general GUI Framework for R scripts.

BACKGROUND: R is the leading open source statistics software with a vast number of biostatistical and bioinformatical analysis packages. To exploit the advantages of R, extensive scripting/programming skills are required. RESULTS: We have developed a software tool called R GUI Generator (RGG) which enables the easy generation of Graphical User Interfaces (GUIs) for the programming language R by adding a few Extensible Markup Language (XML) - tags. RGG consists of an XML-based GUI definition language and a Java-based GUI engine. GUIs are generated in runtime from defined GUI tags that are embedded into the R script. User-GUI input is returned to the R code and replaces the XML-tags. RGG files can be developed using any text editor. The current version of RGG is available as a stand-alone software (RGGRunner) and as a plug-in for JGR. CONCLUSION: RGG is a general GUI framework for R that has the potential to introduce R statistics (R packages, built-in functions and scripts) to users with limited programming skills and helps to bridge the gap between R developers and GUI-dependent users. RGG aims to abstract the GUI development from individual GUI toolkits by using an XML-based GUI definition language. Thus RGG can be easily integrated in any software. The RGG project further includes the development of a web-based repository for RGG-GUIs. RGG is an open source project licensed under the Lesser General Public License (LGPL) and can be downloaded freely at (http://rgg.r-forge.r-project.org).

Consensus genes of the literature to predict breast cancer recurrence.

BACKGROUND: Extensive efforts have been undertaken to discover genes relevant for breast cancer prognosis. Yet, in current opinion, with little overlap in findings. We aimed to reanalyze molecular prediction of breast cancer recurrence. METHODS: From 44 published gene lists relevant for breast cancer prognosis, we extracted 374 genes, which, besides other quality criteria, are recorded at least twice. From eight published microarray datasets, a single dataset of 1,067 breast cancer patients was created, using transformation to 'probability of expression' scale. For recurrence analysis, the Cox proportional hazards model was applied. RESULTS: The 374 genes, termed '374 Gene Set', are highly enriched in cell cycle genes. The '374 Gene Set' is significantly associated with breast cancer recurrence (p = 2 x 10(-12), log-rank test) in the meta set of 1,067 patients, showing an estimated Hazard Ratio of recurrence for the 'poor' prognosis group compared to the 'good' prognosis group of 2.03 (95% confidence interval, 1.66-2.48). Notably, the '374 Gene Set' is significantly associated with recurrence in untreated patients. In multivariate analysis, including the standard histopathological parameters, only tumor size and the '374 Gene Set' remain independent predictors of recurrence. External validation further confirmed the prognostic relevance of the gene set (253 patients, p = 0.001, log-rank test). CONCLUSIONS: The '374 Gene Set' comprises a molecular basis of metastatic breast cancer progression. Starting from this gene set it might be possible to construct a clinically relevant classifier, which then again needs to be validated.