GAP Enhancing Semantic Interoperability of Genomic Datasets and Provenance Through Nanopublications

TL;DR

This paper introduces GAP, a novel data model that enhances semantic interoperability and provenance tracking of genomic datasets using nanopublications, aiming to improve data reuse and reproducibility in biological repositories.

Contribution

GAP integrates data provenance, FAIR principles, and nanopublications into a three-level model for genomic data, improving semantic clarity and interoperability.

Findings

Prototype successfully scrapes and traces genomic data to nanopublications.

Three-level nanopub model effectively organizes genomic and related scientific data.

Enhanced data flexibility and interoperability demonstrated in experiments.

Abstract

While the publication of datasets in scientific repositories has become broadly recognised, the repositories tend to have increasing semantic-related problems. For instance, they present various data reuse obstacles for machine-actionable processes, especially in biological repositories, hampering the reproducibility of scientific experiments. An example of these shortcomings is the GenBank database. We propose GAP, an innovative data model to enhance the semantic data meaning to address these issues. The model focuses on converging related approaches like data provenance, semantic interoperability, FAIR principles, and nanopublications. Our experiments include a prototype to scrape genomic data and trace them to nanopublications as a proof of concept. For this, (meta)data are stored in a three-level nanopub data model. The first level is related to a target organism, specifying data in terms of biological taxonomy. The second level focuses on the biological strains of the target, the central part of our contribution. The strains express information related to deciphered (meta)data of the genetic variations of the genomic material. The third level stores related scientific papers (meta)data. We expect it will offer higher data storage flexibility and more extensive interoperability with other data sources by incorporating and adopting associated approaches to store genomic data in the proposed model.