Predicting Protein Interactions in Mice
Author Information
Author(s): Yellaboina Sailu, Dudekula Dawood B, Ko Minoru SH
Primary Institution: National Institute on Aging, National Institutes of Health
Hypothesis
Can phylogenetic profiles help reduce false positives in predicting protein-protein interactions in Mus musculus?
Conclusion
The study developed a protein-protein interaction database for mice, containing 41,109 interologs, and demonstrated that phylogenetic profiles can effectively filter out false positives.
Supporting Evidence
- The filtering method significantly increased the frequency of co-expressed interacting protein pairs.
- Phylogenetic profiles helped reduce the number of false positives in interologs.
- The final dataset contained 65,515 protein-protein interactions in Mus musculus.
- Co-expression frequency and similarity of Gene Ontology terms were significantly different between filtered and unfiltered interologs.
Takeaway
Scientists figured out how to find out which proteins in mice work together by looking at similar proteins in other animals and checking if they really interact.
Methodology
The study used phylogenetic profiles to filter predicted protein-protein interactions based on evolutionary conservation.
Potential Biases
Potential bias may arise from the selection of reference organisms and the quality of the experimental data used.
Limitations
The study may have limitations due to the reliance on existing experimental data, which can contain false positives.
Participant Demographics
The study focused on protein interactions in Mus musculus, a common model organism.
Statistical Information
P-Value
p<2.2e-16
Statistical Significance
p<0.05
Digital Object Identifier (DOI)
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