Ideas or advise for data visualization that shows genotype and phenotpye relationship

Question

[kevinjspring]

I thought biostars may be a good place to ask this question.

I have a large database of similar protein sequences with one to multiple amino acid substitutions and a phenotypic value for each of these protein sequences. What would be a good way to visualize the relationship between the single or multiple amino acid substitutions and its effect on phenotypic change? Currently I have a graph such as below but it does not capture the protein sequence.

Answer 1

[Katie D'Aco]

What about creating a phylogeny of the protein sequences and color coding the branches by phenotype?

Something like this:

[Istvan Albert]

neat visualization!

[Devon Ryan]

I'm totally going to have to find an excuse to make a dendrogram like that. What program did you use?

[Katie D'Aco]

ummm...I used google image search ;)

I should have credited the source in my answer, here is the blog post it is from: http://blog.phytools.org/2012/11/mapping-reconstructed-value-of.html

The author uses a package called phytools

[kevinjspring]

Thanks Katie. It is written in R too.

Answer 2

[Alex Reynolds]

Draw two graphs, one of top of the other, at the same width:

1. A sequence logo showing the amino acid sequence; substitutions will be visualized as lower information or higher entropy residues.
2. A bar or violin plot showing measured phenotypic change at each residue, aligned to the residues in the sequence logo.

Repeat for each amino acid sequence; tile the figures if they can be put into a group.

[kevinjspring]

This doesn't seem to work with phenotypic measurements on proteins with multiple mutations.

Answer 3

[joshualevipayne]

You may also be able to use the "genotype network" formalism, creating a separate network for each phenotype. In a genotype network, a vertex represents a sequence and edges connect vertices if their corresponding sequences are separated by a single, small mutation. For more information on this formalism, see Wagner (2008) Nature Reviews Genetics, 9:965-974. For the foundations of this formalism, see Schuster et al. (1994) Proc. R. Soc. Lond. B 255:279-284.