RPPApipe: a pipeline for the analysis of reverse-phase protein array data.

BACKGROUND AND SCOPE: Today, web-based data analysis pipelines exist for a wide variety of microarray platforms, such as ordinary gene-centered arrays, exon arrays and SNP arrays. However, most of the available software tools provide only limited support for reverse-phase protein arrays (RPPA), as relevant inherent properties of the corresponding datasets are not taken into account. Thus, we developed the web-based data analysis pipeline RPPApipe, which was specifically tailored to suit the characteristics of the RPPA platform and encompasses various tools for data preprocessing, statistical analysis, clustering and pathway analysis. IMPLEMENTATION AND PERFORMANCE: All tools which are part of the RPPApipe software were implemented using R/Bioconductor. The software was embedded into our web-based ZBIT Bioinformatics Toolbox which is a customized instance of the Galaxy platform. AVAILABILITY: RPPApipe is freely available under GNU Public License from http://webservices.cs.uni-tuebingen.de. A full documentation of the tool can be found on the corresponding website http://www.cogsys.cs.uni-tuebingen.de/software/RPPApipe.

Precise generation of systems biology models from KEGG pathways.

BACKGROUND: The KEGG PATHWAY database provides a plethora of pathways for a diversity of organisms. All pathway components are directly linked to other KEGG databases, such as KEGG COMPOUND or KEGG REACTION. Therefore, the pathways can be extended with an enormous amount of information and provide a foundation for initial structural modeling approaches. As a drawback, KGML-formatted KEGG pathways are primarily designed for visualization purposes and often omit important details for the sake of a clear arrangement of its entries. Thus, a direct conversion into systems biology models would produce incomplete and erroneous models. RESULTS: Here, we present a precise method for processing and converting KEGG pathways into initial metabolic and signaling models encoded in the standardized community pathway formats SBML (Levels 2 and 3) and BioPAX (Levels 2 and 3). This method involves correcting invalid or incomplete KGML content, creating complete and valid stoichiometric reactions, translating relations to signaling models and augmenting the pathway content with various information, such as cross-references to Entrez Gene, OMIM, UniProt ChEBI, and many more.Finally, we compare several existing conversion tools for KEGG pathways and show that the conversion from KEGG to BioPAX does not involve a loss of information, whilst lossless translations to SBML can only be performed using SBML Level 3, including its recently proposed qualitative models and groups extension packages. CONCLUSIONS: Building correct BioPAX and SBML signaling models from the KEGG database is a unique characteristic of the proposed method. Further, there is no other approach that is able to appropriately construct metabolic models from KEGG pathways, including correct reactions with stoichiometry. The resulting initial models, which contain valid and comprehensive SBML or BioPAX code and a multitude of cross-references, lay the foundation to facilitate further modeling steps.