Using Sequence Similarity Networks to Visualize Protein Relationships
Author Information
Author(s): Holly J. Atkinson, John H. Morris, Thomas E. Ferrin, Patricia C. Babbitt
Primary Institution: University of California San Francisco
Hypothesis
Can sequence similarity networks effectively visualize functional trends across diverse protein superfamilies?
Conclusion
Sequence similarity networks are effective tools for visualizing relationships among proteins and can reveal functional themes.
Supporting Evidence
- Sequence similarity networks provide a good representation of group-wise sequence similarity relationships.
- Networks correlate well with known functional relationships.
- Visualized networks enable the perception of trends from the context of sequence similarity.
Takeaway
This study shows that we can use special networks to see how different proteins are related to each other, helping scientists understand their functions better.
Methodology
The study used sequence similarity networks to analyze and visualize relationships among large sets of homologous proteins.
Potential Biases
Potential biases may arise from the selection of sequences and the thresholds used for defining similarity.
Limitations
The networks may not accurately reflect evolutionary history due to their lack of an explicit evolutionary model.
Statistical Information
P-Value
1.95×10−23
Statistical Significance
p<0.05
Digital Object Identifier (DOI)
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