SGFSC: speeding the gene functional similarity calculation based on hash tables.

Area of Science:

• Bioinformatics
• Computational Biology
• Genomics

Background:

• Gene functional similarity measures are crucial for biological research.
• Existing methods, especially pairwise approaches, suffer from significant computational time consumption.
• Efficiently calculating gene functional similarity for large datasets remains a challenge.

Purpose of the Study:

• To develop a computationally efficient strategy for calculating gene functional similarity.
• To address the time-consuming nature of existing gene similarity algorithms.
• To improve the scalability of gene functional similarity analysis.

Main Methods:

• A novel two-step computing strategy was proposed.
• Hash tables were established for each method to store Gene Ontology (GO) graph information.
• Gene functional similarity was calculated using these hash tables, avoiding repeated GO graph traversals.

Main Results:

• The proposed strategy significantly improves computational efficiency.
• A tool named Speeding Gene Functional Similarity Calculation (SGFSC) was implemented, incorporating seven typical measures.
• SGFSC demonstrated a significant advantage in measuring gene functional similarity on a whole-genomic scale.

Conclusions:

• The strategy successfully accelerates gene functional similarity calculation methods.
• SGFSC is an efficient tool freely available for researchers.
• The tool facilitates large-scale genomic analysis by providing faster similarity computations.