It is rare that actual data adheres to the concept of perfect phylogeny.
Using a character-based approach employs character states across species as an input in an attempt to find the most "perfect" phylogenetic tree.
Therefore, it is often the case that researchers are forced to compromise by developing trees that simply try to minimize homoplasy, finding a maximum-cardinality set of compatible characters, or constructing phylogenies that match as closely as possible to the partitions implied by the characters.
Both of these data sets illustrate examples of character state matrices.
In contrast, when observing matrix M'2, one can see that there is no way to set up the phylogenetic tree such that each character labels only one edge length.
This concept involves utilizing perfect phylogenies with real, and therefore incomplete and imperfect, datasets.
These Short Interspersed Elements are present across many genomes and can be identified by their flanking sequences.
By utilizing algorithms derived from perfect phylogeny data we are able to attempt to reconstruct a phylogenetic tree in spite of these limitations.
By utilizing the concepts and algorithms described in perfect phylogeny one can determine information regarding missing and unavailable haplotype data.
[11] By assuming that the set of haplotypes that result from genotype mapping corresponds and adheres to the concept of perfect phylogeny (as well as other assumptions such as perfect Mendelian inheritance and the fact that there is only one mutation per SNP), one is able to infer missing haplotype data.
[12][13][14] [15] Inferring a phylogeny from noisy VAF data under the PPM is a hard problem.
Examples of tools that infer phylogenies from noisy VAF data include AncesTree, Canopy, CITUP, EXACT, and PhyloWGS.