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Updated: Jun 1, 2026

Navigating MARRVEL, a Web-Based Tool that Integrates Human Genomics and Model Organism Genetics Information

Navigating MARRVEL, a Web-Based Tool that Integrates Human Genomics and Model Organism Genetics Information

Published on: August 15, 2019

Semantic-JSON: a lightweight web service interface for Semantic Web contents integrating multiple life science

Norio Kobayashi¹, Manabu Ishii, Satoshi Takahashi

¹Bioinformatics And Systems Engineering division, RIKEN. 1-7-22 Suehiro, Tsurumi, Yokohama, Kanagawa, 230-0045 Japan.

Nucleic Acids Research

|June 3, 2011

Summary

Bioinformatics databases face integration challenges. A new Semantic-JSON interface securely accesses linked and raw life sciences data, enabling advanced research applications.

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Last Updated: Jun 1, 2026

Navigating MARRVEL, a Web-Based Tool that Integrates Human Genomics and Model Organism Genetics Information

Navigating MARRVEL, a Web-Based Tool that Integrates Human Genomics and Model Organism Genetics Information

Published on: August 15, 2019

Area of Science:

Bioinformatics
Computational Biology
Data Science

Background:

Global bioinformatics research databases are large and heterogeneous, presenting challenges in cross-integration, retrieval, security, and openness.
Existing Semantic Web-based integration frameworks like SciNetS.org manage millions of data records but face limitations in data query capacity and analysis of raw data.

Purpose of the Study:

To develop a novel interface for secure and efficient access to integrated and raw life sciences data.
To overcome the limitations of existing tools like SPARQL for querying and analyzing heterogeneous bioinformatics databases.

Main Methods:

Developed a lightweight Semantic-JSON interface compatible with programming languages like Perl and Ruby.
Integrated 192 mammalian, plant, and protein life sciences databases (8.2 million records) as Linked Open or Private Data (LOD/LPD) using SciNetS.org.
Enabled secure access to both linked and raw data fragments.

Main Results:

The Semantic-JSON interface successfully facilitated access across 28 million semantic relationships.
Enabled diverse biological applications, including genome design, sequence processing, phenotype database inference, and full-text search.
Provided specialized tools for data analysis using raw original data, enhancing research capabilities.

Conclusions:

The Semantic-JSON interface effectively addresses challenges in accessing and analyzing large-scale, heterogeneous bioinformatics data.
It enhances collaborative research by providing secure, flexible access to both linked and raw life sciences data.
The developed system supports a wide range of advanced bioinformatics applications and promotes data-driven biological discovery.