I am trying to pick the best representative sequence from an amino acid MSA. I do not want to use the consensus, as it generates an artificial one. Here is my approach:
- I calculate a Percentage Identity Matrix (PIM) from the MSA file
- I check the highest value in each column and rank the respective sequences. What I mean by this, for example, the values in the first column would be the Percentage Identity of the first seq compared the rest of the dataset. If I pick the highest value in there, that would give me the sequences that are highly identical to the first seq.
- I do this for the every column and rank the sequences. The top ranked sequence would yield me the best representetive sequence within my MSA data set.
The problem with this approach is that its computationally expensive to calculate the PIM as its doing a character match, comparing each sequence's position with all of the dataset. Is there any easier way to achieve this? Im not sure, if we could achieve something similar by looking at the tree. I think we can find the lowest common ancestor by the tree but I do not think that would yield a representetive sequence.