SemanticGO

SemanticGO performs quantitative functional similarity analysis of genes and pathways in Arabidopsis thaliana and rice by applying latent semantic analysis (LSA) to Gene Ontology (GO) data.

Key Features:

• Latent Semantic Analysis (LSA): Applies latent semantic analysis to Gene Ontology (GO) annotations (described for Arabidopsis thaliana) to derive semantic representations.
• 200-dimensional feature vectors: Generates 200-dimensional feature vectors for each gene that capture functional characteristics in a high-dimensional space.
• Pathway representation: Represents pathways by aggregating (summing) the feature vectors of their member genes.
• Correlation with external data: Correlates gene feature vectors with gene expression profiles and gene network connectivity data to extend functional associations beyond GO annotations.
• Decoding of vectors: Provides a mechanism for decoding feature vectors to interpret their biological significance.
• Species scope: Supports analyses for Arabidopsis thaliana and rice.
• Quantification of sparse similarities: Quantifies sparsely described and qualitative similarities between genes and pathways.

Scientific Applications:

• Cross-species functional similarity: Identifying functionally similar genes between Arabidopsis thaliana and rice.
• Pathway organization analysis: Understanding the functional organization of pathways via comparison of aggregated pathway vectors.
• Inference of interactions and co-regulation: Discovering potential gene interactions or co-regulations based on shared functional vector signatures.

Methodology:

Generate 200-dimensional feature vectors for each gene using latent semantic analysis of Arabidopsis thaliana GO data; represent pathways by summing member gene vectors; correlate gene feature vectors with gene expression profiles and gene network connectivity data; decode feature vectors to interpret biological significance.