Revolutionizing bioinformatics by connecting biological data across distributed sources using Semantic Web technologies
Imagine trying to find a single, specific sentence in a library containing every book ever written, while simultaneously figuring out how the ideas in different books connect to tell a completely new story.
This is the monumental challenge facing biologists today. The field of bioinformatics is experiencing a data deluge of unprecedented scale, with high-throughput technologies generating petabytes of molecular information about everything from human DNA to deadly viruses 4 .
For decades, biological discovery followed a straightforward path. Today, a single sequencing machine can generate massive genomic datasets in a day 1 .
This embarrassment of riches created a new problem: data silos. Critical information became scattered across countless specialized databases and web services.
At its core, SHARE functions as a specialized query engine for bioinformatics that understands both data and meaning. Think of it as a super-powered search engine that doesn't just find keywords but comprehends biological concepts and relationships.
SHARE builds upon Semantic Web standards, which are designed to make online information machine-readable 1 7 .
To understand SHARE's power in action, consider a pressing biological question: how do viruses mimic human proteins to hijack our cellular machinery? This molecular mimicry allows pathogens to evade our immune systems and manipulate our cells for their replication .
Researchers employed a systematic bioinformatics approach to identify what they termed the "share-ome"—the complete set of sequences shared between hosts and pathogens .
| Metric | Count | Biological Significance |
|---|---|---|
| Shared Nonamers | 2,430 | Molecular mimicry candidates |
| Mapped Flaviviridae Proteins | 16,946 | Viral proteins involved in sharing |
| Mapped Human Proteins | 7,506 | Human pathways potentially targeted |
| Flaviviridae Species Affected | 125 | Breadth of mimicry across virus types |
These shared sequences were not evenly distributed across viral species. The majority (~68%) mapped to Hepatitis C virus, with significant representation from West Nile, Dengue, and Zika viruses .
Further analysis provided insights into the structural and functional implications of these shared sequences, supporting the hypothesis that molecular mimicry represents an evolutionary adaptation by viruses.
Research in the age of big data biology requires both computational infrastructure and specialized analytical frameworks.
Query Engine for distributed querying across biological data sources
Web Service Standard that enables automated workflow assembly
Programming Interface for retrieval of molecular data from public databases
Computational Tool for removal of redundant sequences from large datasets
Query Language for semantic queries across linked data sources
Knowledge Representation for standardized biological concepts and relationships
The implications of SHARE and similar semantic technologies extend far beyond individual experiments.
By making data integration automated and reliable, these systems accelerate the entire scientific discovery process. What previously took months of custom programming and data wrestling can now be accomplished through thoughtful query design.
Identifying unintended cross-reactions between drugs and human proteins by finding shared sequences
Pinpointing viral regions that are unlikely to mutate for more stable vaccine targets
Tracking the evolution of pathogens by monitoring changes in shared sequences across outbreaks
Understanding how individual genetic variations affect susceptibility to different pathogens
SHARE represents more than just a technical solution to a data management problem—it embodies a fundamental shift in how we conduct biological research. By creating a "bioinformatics nation" where data flows freely and meaning is machine-interpretable, SHARE and similar Semantic Web technologies are breaking down the barriers between discrete biological datasets.
The framework demonstrates that the true power of bioinformatics lies not just in accumulating data, but in connecting disparate information to reveal biological truths that remain invisible when examining datasets in isolation 1 .