Question migrated from bioinformatics stackexchange due to lack of interest.
I'm new to bioinformatics and have been reading through a bioinformatics book, and it seems calculating a phylogenetic tree is quite a complicated process.
I'm wondering if generating the minimum spanning tree (MST) from a similarity matrix would provide a decent approximation to the actual phylogenetic tree, i.e. provide some information on the evolutionary ancestry of a collection of organisms? My thinking is that animals diverging from ancient ancestors will always be farther away than animals that diverged recently, so the MST will at least show me what animals are genetically related and which are genetically distant.
I realize this is not quite the same thing as a phylogenetic tree, but it seems to give me some of the same information provided by a phylogenetic tree, namely which animals are closely genetically related and which are distant relatives.
For reference, I'm measuring similarity based on the normalized compression distance (NCD) metric. The metric is defined in "Clustering by Compression" by Cilibrasi and Vitanyi.
Here is an example MST using the dataset from the referenced paper. Some parts make sense from my rudimentary knowledge of biology, like the clustering of primates. Other parts are new to me, and I'm not sure if the relationships are just an accidental feature of the metric, MST, or if real. For instance, cows are more related to whales instead of horses according to the MST, cats and dogs appear to have evolved from seals or visa versa, and pigs are related to a wide variety of animals: ranging from bats to rabbits to whales.
Note, the 'randgen' nodes are randomly generated DNA sequences that I added to the dataset as a sanity check. As expected, they are off on a branch by themselves instead of mixed into the population of real animal DNA sequences. The reason why they are clustered is because I repeat each DNA sequence 40 times to amplify the signal, and repeated short random subsequences become compressible. The random DNA sequences are probably clustered because they tend to share random subsequences, while the mammal DNA sequences are orderly and have fewer random subsequences.
Here is the repo to reproduce the tree. https://github.com/yters/ncd