Linking big biomedical datasets to modular analysis with Portable Encapsulated Projects. Issue 12 (6th December 2021)

• Record Type: Journal Article
• Authors: Sheffield, Nathan C; Stolarczyk, Michał; Reuter, Vincent P; Rendeiro, André F
• Is Part Of: GigaScience. Volume 10:Issue 12(2021)
• Publication Date: 2021-12-06
• Subjects: sample metadata standard -- interoperability -- sample annotation table -- metadata validation schema; Information storage and retrieval systems -- Research -- Periodicals; Biology -- Research -- Periodicals; Medical sciences -- Research -- Periodicals; Database management -- Periodicals; 570.285
• DOI: 10.1093/gigascience/giab077
• Languages: English
• ISSNs: 2047-217X

Abstract

Abstract: Background: Organizing and annotating biological sample data is critical in data-intensive bioinformatics. Unfortunately, metadata formats from a data provider are often incompatible with requirements of a processing tool. There is no broadly accepted standard to organize metadata across biological projects and bioinformatics tools, restricting the portability and reusability of both annotated datasets and analysis software. Results: To address this, we present the Portable Encapsulated Project (PEP) specification, a formal specification for biological sample metadata structure. The PEP specification accommodates typical features of data-intensive bioinformatics projects with many biological samples. In addition to standardization, the PEP specification provides descriptors and modifiers for project-level and sample-level metadata, which improve portability across both computing environments and data processing tools. PEPs include a schema validator framework, allowing formal definition of required metadata attributes for data analysis broadly. We have implemented packages for reading PEPs in both Python and R to provide a language-agnostic interface for organizing project metadata. Conclusions: The PEP specification is an important step toward unifying data annotation and processing tools in data-intensive biological research projects. Links to tools and documentation are available at http://pep.databio.org/ .