SAPP: functional genome annotation and analysis through a semantic framework using FAIR principles.

Area of Science:

• Genomics
• Bioinformatics
• Data Science

Background:

• Genome data analysis requires robust annotation and provenance tracking.
• Interoperability and reusability of genomic annotations are critical for advancing research.
• Current methods often lack comprehensive provenance and semantic integration.

Purpose of the Study:

• To develop a Semantic Annotation Platform with Provenance (SAPP) to enhance genome data interoperability and reusability.
• To support FAIR (Findable, Accessible, Interoperable, Reusable) data principles in computational genomics.
• To automate the prediction, tracking, and storage of genomic annotations and their provenance.

Main Methods:

• SAPP automatically predicts, tracks, and stores structural and functional annotations.
• Provenance data is captured at both dataset and element levels.
• Linked Data formats and Semantic Web technologies are employed for data representation and retrieval.

Main Results:

• SAPP enables efficient information mining and retrieval of genome annotations.
• The platform significantly reduces the administrative burden of managing analysis tools and versions.
• Facilitates multi-level, large-scale comparative analyses of genomic data.

Conclusions:

• SAPP provides a robust infrastructure for FAIR de novo computational genomics.
• The platform enhances the value and usability of existing genome annotations.
• Enables advanced data analysis through semantic integration and provenance tracking.