Biology subjects

Auer, F. J.

Publications and source records attributed to Auer, F. J..

3 recordsLinked to original sources

bioRxiv2022

MetaRelSubNetVis: Referenceable network visualizations based on integrated patient data with group-wise comparison

MotivationNetworks are a common data structure to describe relations among biological entities. Enriched with information to specify the entities or their connections, they provide a solid foundation for data-dependent visualization. When such annotations overlap, for example in a protein-protein interaction network that is enriched with patient-specific expressions, visualization is reliant on user interaction. Thereby, effective and reliable exchange of visualization parameters between collaborators is crucial to the communication within workflows. ResultsHere, we introduce MetaRelSubNetVis, a web-based tool that allows users to interactively apply group-wise visualizations to networks augmented with patient data. Our application can visually reflect patient-specific attributes for single patients or in a comparative context. Furthermore, we improved upon the exchange of network visualizations by providing unambiguous links that result in the same visual markup. Our work provides new prospects in interacting with and collaborating on network data, especially with respect to the exchange and integration of network visualizations. Contactflorian.auer@informatik.uni-augsburg.de

bioinformatics↗

bioRxiv2022

Reproducible data integration and visualization of biological networks in R

MotivationCollaborative workflows in network biology not only require the documentation of the performed analysis steps but also of the network data on which the decisions were based. However, replication of the entire workflow or tracking of the intermediate networks used for a particular visualization remains an intricate task. Also, the amount and heterogeneity of the integrated data requires instruments to explore and thus comprehend the results. ResultsHere we demonstrate a collection of software tools and libraries for network data integration, exploration, and visualization to document the different stages of the workflow. The integrative steps are performed in R, and the entire process is accompanied by an interchangeable toolset for data exploration and network visualization. AvailabilityThe source code of the performed workflow is available as R markdown scripts at https://github.com/frankkramer-lab/reproducible-network-visualization. A compiled HTML version is also hosted on Github pages at https://frankkramer-lab.github.io/reproducible-network-visualization. Contactflorian.auer@informatik.uni-augsburg.de

bioinformatics↗

bioRxiv2021

RCX - an R package adapting the Cytoscape Exchange format for biological networks

MotivationThe Cytoscape Exchange (CX) format is a JSON-based data structure designed for the transmission of biological networks using standard web technologies. It was developed by the network data exchange (NDEx), which itself serves as online commons to share and collaborate on biological networks. Furthermore, the Cytoscape software for the analysis and visualization of biological networks contributes structure elements to capture the visual layout within the CX format. However, there is a fundamental difference between data handling in web standards and R. A manual conversion requires detailed knowledge of the CX format to reproduce and work with the networks. ResultsHere we present a software package to create, handle, validate, visualize and convert networks in CX format to standard data types and objects within R. Networks in this format can serve as a source for biological knowledge, and also capture the results of the analysis of those while preserving the visual layout across all platforms. The RCX package connects the R environment for statistical computing with outside platforms for storage and collaboration, as well as further analysis and visualization of biological networks. AvailabilityRCX is a free and open-source R package, available on Bioconductor from release 3.15 (https://bioconductor.org/packages/RCX) and via GitHub (https://github.com/frankkramer-lab/RCX). Contactflorian.auer@informatik.uni-augsburg.de Supplementary informationSupplementary data are available at Bioinformatics Advances online.

bioinformatics↗