SBGNview: towards data analysis, integration and visualization on all pathways

Author:

Dong Xiaoxi12,Vegesna Kovidh12,Brouwer Cory12,Luo Weijun123ORCID

Affiliation:

1. Department of Bioinformatics and Genomics, College of Computing and Informatics, UNC Charlotte, Charlotte, NC 28223, USA

2. UNC Charlotte Bioinformatics Service Division, North Carolina Research Campus, Kannapolis, NC 28081, USA

3. Novant Health, Charlotte, NC 28207, USA

Abstract

Abstract Summary Pathway analysis is widely used in genomics and omics research, but the data visualization has been highly limited in function, pathway coverage and data format. Here, we develop SBGNview a comprehensive R package to address these needs. By adopting the standard SBGN format, SBGNview greatly extend the coverage of pathway-based analysis and data visualization to essentially all major pathway databases beyond KEGG, including 5200 reference pathways and over 3000 species. In addition, SBGNview substantially extends or exceeds current tools (esp. Pathview) in both design and function, including standard input format (SBGN), high-quality output graphics (SVG format) convenient for both interpretation and further update, and flexible and open-end workflow for iterative editing and interactive visualization (Highlighter module). In addition to pathway analysis and data visualization, SBGNview provides essential infrastructure for SBGN data manipulation and processing. Availability and implementation The data underlying this article are available as part of the SBGNview package is available on both GitHub and Bioconductor: https://github.com/datapplab/SBGNview, https://bioconductor.org/packages/SBGNview. Supplementary information Supplementary data are available at Bioinformatics online.

Funder

National Science Foundation

Publisher

Oxford University Press (OUP)

Subject

Computational Mathematics,Computational Theory and Mathematics,Computer Science Applications,Molecular Biology,Biochemistry,Statistics and Probability

Reference20 articles.

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