SPELL

SPELL performs context-sensitive searches of Saccharomyces cerevisiae gene expression microarray compendia to identify co-expressed genes and weight datasets relevant to a user-provided query gene set.

Key Features:

• Context-Sensitive Search Algorithm: Accepts a small set of query genes to establish a biological search context and assess dataset relevance.
• Relevance Weighting and Co-expression Identification: Weights datasets by relevance and identifies genes whose expression profiles are most similar to the query set within the weighted datasets.
• Compendium Coverage: Operates on a compendium of approximately 2400 experimental conditions derived from Saccharomyces cerevisiae gene expression microarray data.
• Enhanced Accuracy: Reports an average 273% increase in accuracy compared with previous mega-clustering approaches when recapitulating known biological responses.
• Novel Biological Predictions: Generates novel biological predictions that can be validated experimentally.

Scientific Applications:

• Exploration of Microarray Compendia: Enables targeted exploration of large Saccharomyces cerevisiae gene expression compendia based on user-defined gene queries.
• Hypothesis Formulation: Supports hypothesis generation by identifying co-expressed genes and dataset contexts relevant to the query.
• Discovery of Novel Interactions: Facilitates prediction of novel gene co-expression relationships and interactions for experimental follow-up.
• Genomics and Systems Biology: Aids studies in genomics and systems biology that require context-aware analysis of microarray expression data.

Methodology:

SPELL collects approximately 2400 experimental conditions from Saccharomyces cerevisiae gene expression microarray data, analyzes the functional coverage of the compendium, and applies a context-sensitive search algorithm to weight datasets by relevance to the input query and identify co-expressed genes.