A Search Engine for Identifying Pathway Genes
Author Information
Author(s): Chen Chunnuan, Weirauch Matthew T, Powell Corey C, Zambon Alexander C, Stuart Joshua M
Primary Institution: University of California, Santa Cruz
Hypothesis
Integrating search results obtained on different organisms would improve our ability to identify pathway members.
Conclusion
The search engine can scan large collections of gene expression data for new genes that are significantly coregulated with a pathway of interest.
Supporting Evidence
- The MSGR achieved its highest accuracy for many human pathways when searches are combined across species.
- Twelve of the 39 GenMAPP pathways tested had significant coexpression scores.
- The MSGR identified a hit list with a precision at the 50% recall rate of 50% for the Calcium Channels pathway.
Takeaway
The study created a tool that helps find genes that work together in biological pathways by looking at data from many different organisms.
Methodology
The MSGR combines search orderings from multiple organisms to identify genes coregulated with a query set of genes.
Potential Biases
The accuracy of the search depends on the quality of orthology predictions.
Limitations
The MSGR relies solely on gene expression data and may miss other types of genomic information.
Statistical Information
P-Value
p<0.001
Statistical Significance
p<0.001
Digital Object Identifier (DOI)
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