pyBioPortal: a Python']Python package for simplifying cBioPortal data access in cancer research

被引:0
作者
Valerio, Matteo [1 ]
Inno, Alessandro [1 ]
Gori, Stefania [1 ]
机构
[1] IRCCS Sacro Cuore Don Calabria Hosp, Med Oncol, Via Don A Sempreboni,5, I-37024 Verona, Negrar di Valpo, Italy
关键词
cBioPortal; cancer research; bioinformatics; !text type='Python']Python[!/text;
D O I
10.1093/jamiaopen/ooae146
中图分类号
R19 [保健组织与事业(卫生事业管理)];
学科分类号
摘要
Objectives In recent years, the rise of big data and artificial intelligence has led to an increasing expansion of databases and web services in biomedical research. cBioPortal is one of the most widely used platforms for accessing cancer genomic and clinical data. The primary objective of this study was to develop a tool that simplifies programmatic interaction with cBioPortal's web service.Materials and Methods We developed the pyBioPortal Python package, which leverages the cBioPortal REST API to access genomic and clinical data. The retrieved data is returned as a Pandas DataFrame, a format widely used for data analysis in Python.Results pyBioPortal offers an efficient interface between the user and the cBioPortal database. The data is provided in formats conducive to further analysis and visualization, promoting workflows and improving reproducibility.Discussion The development of pyBioPortal addresses the challenge of accessing and processing large volumes of biomedical data. By simplifying the interaction with the cBioPortal API and providing data in Pandas DataFrame format, pyBioPortal allows users to focus more on the analytical aspects rather than data extraction.Conclusion This tool facilitates the retrieval of heterogeneous biological and clinical data in a standardized format, making it more accessible for analysis and enhancing the reproducibility of results in cancer informatics. Distributed as an open-source project, pyBioPortal is available to the broader bioinformatics community, promoting collaboration and advancing research in cancer genomics. The advent of big data and artificial intelligence has revolutionized cancer research, making large amounts of data available for analysis. However, accessing this data can be challenging, especially for researchers without specific programming skills or those who prefer to focus on data analysis and interpretation rather than on the technical aspects of data extraction. pyBioPortal is a newly developed Python tool designed to simplify the process of retrieving data from cBioPortal, a widely used platforms for accessing cancer genomic and clinical data. By making data easier to access and analyze, pyBioPortal enables researchers to focus on uncovering new insights that drive advancements in cancer research. The tool is freely available as an open-source project, promoting widespread use and collaboration within the bioinformatics community.
引用
收藏
页数:5
相关论文
共 13 条
  • [1] Luo J, Wu M, Gopukumar D, Et al., Big data application in biomedical research and health care: a literature review, Biomed Inform Insights, 8, pp. 1-10, (2016)
  • [2] Cerami E, Gao J, Dogrusoz U, Et al., The cBio cancer genomics portal: an open platform for exploring multidimensional cancer genomics data, Cancer Discov, 2, pp. 401-404, (2012)
  • [3] Gao J, Aksoy BA, Dogrusoz U, Et al., Integrative analysis of complex cancer genomics and clinical profiles using the cBioPortal, Sci Signal, 6, (2013)
  • [4] de Bruijn I, Kundra R, Mastrogiacomo B, Et al., Analysis and visualization of longitudinal genomic and clinical data from the AACR project GENIE biopharma collaborative in cBioPortal, Cancer Res, 83, pp. 3861-3867, (2023)
  • [5] Fielding RT., Representational state transfer (REST), Architectural Styles and the Design of Network-Based Software Architectures [Dissertation], pp. 76-106, (2000)
  • [6] Van Rossum G, Python Language Reference Release 3.11.3, (2023)
  • [7] –Requests 2.32.3 documentation
  • [8] McKinney W., Data structures for statistical computing in Python, Proceedings of the 9th Python in Science Conference, pp. 56-61, (2010)
  • [9] Fielding RT, Reschke J., Hypertext Transfer Protocol (HTTP/1.1): semantics and content, Internet Engineering Task Force, (2014)
  • [10] Bray T., The JavaScript object notation (JSON) data interchange format, Internet Engineering Task Force, (2017)