PANDA: pathway and annotation explorer for visualizing and interpreting gene-centric data
- Published
- Accepted
- Subject Areas
- Computational Biology, Genetics, Genomics, Computational Science
- Keywords
- Pathway, visualization, genomics, user interface, data integration, variant interpretation, annotation and pathway visualization
- Copyright
- © 2015 Hart et al.
- Licence
- This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, reproduction and adaptation in any medium and for any purpose provided that it is properly attributed. For attribution, the original author(s), title, publication source (PeerJ PrePrints) and either DOI or URL of the article must be cited.
- Cite this article
- 2015. PANDA: pathway and annotation explorer for visualizing and interpreting gene-centric data. PeerJ PrePrints 3:e1021v1 https://doi.org/10.7287/peerj.preprints.1021v1
Abstract
Objective: Bringing together genomics, transcriptomics, proteomics, and other –omics technologies is an important step towards developing highly personalized medicine. However, instrumentation has advances far beyond expectations and now we are able to generate data faster than it can be interpreted. Materials and Methods: We have developed PANDA ( P athway AND A nnotation) Explorer, a visualization tool that integrates gene-level annotation in the context of biological pathways to help interpret complex data from disparate sources. PANDA is a web-based application that displays data in the context of well-studied pathways like KEGG, BioCarta, and PharmGKB. PANDA represents data/annotations as icons in the graph while maintaining the other data elements (i.e. other columns for the table of annotations). Custom pathways from underrepresented diseases can be imported when existing data sources are inadequate. PANDA also allows sharing annotations among collaborators. Results : In our first use case, we show how easy it is to view supplemental data from a manuscript in the context of a user’s own data. Another use-case is provided describing how PANDA was leveraged to design a treatment strategy from the somatic variants found in the tumor of a patient with metastatic sarcomatoid renal cell carcinoma. Conclusion : PANDA facilitates the interpretation of gene-centric annotations by visually integrating this information with context of biological pathways. The application can be downloaded or used directly from our website http://bioinformaticstools.mayo.edu/research/panda-viewer/.
Author Comment
This is a submission to PeerJ for review.