So I work in an enzymology lab in a chemistry dept. We do not have much experience with cladistic analysis, however it was suggested by a reviewer of a paper that it would help some readers, particularly physiologists to understand which type of NATs (my enzyme is a transferase) this particular one belongs.
I've since gone from 'phylogeny for dummies' to forming my trees on MEGA using the BLAST results from 30 different insects, but now I just need to get some understanding to what I've actually produced. I have maximum likelihood trees, maximum parsimony trees, UPGMA trees, minimum evolution trees and neighbor joining trees. However, I don't know which one to use! Any suggestions? I would be happy to give more information if necessary.
My next question is: How important are the internal models in constructing the trees? For example, for the max likelihood tree, how do I select between a Poisson model and a Dayhoff model, to name just two?
Next: How important are the log likelihood numbers? Because I was being quoted a value around -6000 for this value and I feel this is a big problem! I can't find any acceptable parameter for this value.
Any other tips for cladistic analyses? I'm a chemist in unfamiliar territory ha!
It sounds like the reviewers would like to know which family or group of N-acetyl transferase enzyme you are working on. Humans, for example, have 18 different genes for different N-acetyl transferase enzymes. Most of the human enzymes are in the GCN5 related N-acetyltransferases family, but some are in other families such as the Arylamine N-acetyltransferase family.
If you do a BLAST search and pull out the same enzyme from 30 different insects and build a tree, this will tell you how closely related your insect is to other insects, but will not tell you which enzyme family your are studying.
https://en.wikipedia.org/wiki/N-acetyltransferase
http://supfam.org/SUPERFAMILY/cgi-bin/scop.cgi?sunid=54047
http://www.genenames.org/cgi-bin/genefamilies/set/1134
First of all you need to find the best substition model for your data. The model will affect your analyze, then you need to decide which character (Distance methods, Parsimony methods, and Likelihood ) you should use. The choice is all depends on your data.
In Minumum Evolution tress; distance measures that correct for multiple hits at the same sites are used, and a topology showing the smallest value of the sum of all branches (S). It takes time because S valuesfor all topologies are evaluated. The number of possible topologies (unrooted trees) rapidly increases with the number of taxa so it becomes very difficult to examine all topologies. In this case, one may use the neighbor-joining method. While the NJ tree is usually the same as the ME tree, when the number of taxa is small the difference between the NJ and ME trees can be substantial. Parsimony trees could be confusing for the beginners because its statistical model little bit different.
For further information you can read:
Swofford DL, Olsen GJ, Waddell PJ & Hillis DM (1996). Phylogenetic Inference. In Hiillis DM, Moritz D, and Mable BK, editors, Molecular Systematics, pp. 407-514.Sinauer Associates, Sunderland, Massachusetts.
Page RDM & Holmes EC (1998) Molecular Evolution: A Phylogenetic Approach. Blackwell Science, Oxford, U.K.
Nei M & Kumar S (2000) Molecular Evolution and Phylogenetics. Oxford University Press, New York.
It sounds like the reviewers would like to know which family or group of N-acetyl transferase enzyme you are working on. Humans, for example, have 18 different genes for different N-acetyl transferase enzymes. Most of the human enzymes are in the GCN5 related N-acetyltransferases family, but some are in other families such as the Arylamine N-acetyltransferase family.
If you do a BLAST search and pull out the same enzyme from 30 different insects and build a tree, this will tell you how closely related your insect is to other insects, but will not tell you which enzyme family your are studying.
https://en.wikipedia.org/wiki/N-acetyltransferase
http://supfam.org/SUPERFAMILY/cgi-bin/scop.cgi?sunid=54047
http://www.genenames.org/cgi-bin/genefamilies/set/1134
Thanks for your advice everyone. Brian, I think you might be correct. If I make a tree consisting of the GNAT superfamily, then split it into the different NATs this will give a much clearer picture as to how it relates to other NATs.
And thanks Dilruba for your statistical advice, it's much needed!!
Thanks all! I'll get on it
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