Short Abstract: Standardizing data from multiple sources is a common problem for clinical researchers who integrate data from the electronic medical record with data generated as part of their studies. Differing formats, labels and notations create barriers to true integration. Ontologies have been widely used to standardize metadata tags and simple biological annotations, and their utility in defining standard data formats for clinical data integration has started to become more common. ClinMynEHR provides a multi-component platform consisting of a relational database, data entry and curation software, cohort discovery tool and annotation report tool providing users the ability to customize reports on single or multiple patients or subjects with a download option. Data can be entered manually or uploaded from clinical data warehouses or other databases. Multiple ontologies and vocabularies are used to provide the framework for data formats and standardize information within the frameworks for components such as: patient information; family history; diagnostics and clinical measurements; surgical treatments; non-surgical treatments; medications; signs, symptoms and phenotypes; and lifestyle. ClinMynEHR provides an ontology-based query wizard for cohort discovery and a reporting system which allows users to customize the return of desired information on identified subjects. Data download options also allow users to further analyze data. Planned expansions include sophisticated data visualizations, timeline generation and data analysis tools. ClinMynEHR provides a complete platform for a variety of research projects and easy customization through its ontology-based design.

Short Abstract: Methods for network reconstruction are often designed with the possibility to integrate prior knowledge about the topology of biological signaling networks. However, the format of prior knowledge required, usually in form of an adjacency matrix, is a strong abstraction of the biological reality. In the past years ontologies have been the tool of choice to represent and allow the sharing of knowledge of this biological reality. BioPAX is a commonly used ontology for the encoding of regulatory pathways.
The R Project for Statistical Computing is the standard environment for statistical analyses of high-dimensional data and network reconstruction methods. Although there are packages available that provide the pathway data of databases like KEGG, the Pathway Interaction Database (Nature/NCI) or Reactome as graphs, there was no software available to parse, merge and manipulate BioPAX ontologies inside of R.
We present a new open-source package called rBiopaxParser that parses BioPAX-Ontologies and represents them in R. Class definitions, properties and restrictions are mapped on a 1:1 basis, with respect to the limitions of object-orientation of R. The user is able to parse arbitrary BioPAX OWL files, for example the exports of popular online pathway databases like PID, Reactome or KEGG. Instances of BioPAX-Classes can be programatically added or removed. Multiple pathways can be merged or transformed into an adjacency matrix suitable as input for network reconstruction algorithms, i.e. reducing a pathway to a graph with edges representing only activations or inhibitions. The software will be made publicaly available on either Bioconductor or CRAN.