• Manuals

Browse the manual

• Introduction to the Phylogeny Module
  • Phylogeny
  • Features
• System requirements and installation of the Phylogeny Module
  • System requirements
  • How to install a plug-in
  • How to uninstall a plug-in
• Alignment of sequences
  • Create an alignment
    • Gap costs
    • Fast or accurate alignment algorithm
    • Aligning alignments
    • Fixpoints
  • Join alignments
    • How alignments are joined
  • Pairwise comparison
    • Pairwise comparison on alignment selection
    • Pairwise comparison parameters
    • The pairwise comparison table
• Create Trees
  • K-mer Based Tree Construction
  • Create tree
  • Model Testing
  • Maximum Likelihood Phylogeny
  • Bioinformatics explained
    • Substitution models and distance estimation
    • K-mer based distance estimation
    • Distance based reconstruction methods
    • Maximum likelihood reconstruction methods
    • Bootstrap tests
• Tree Settings
  • Minimap
  • Tree layout
  • Node settings
  • Label settings
  • Background settings
  • Branch layout
  • Bootstrap settings
  • Metadata
  • Node right click menu
• Metadata and Phylogenetic Trees
  • Table Settings and Filtering
  • Add or modify metadata
  • Selection of specific nodes
• Bibliography

K-mer based distance estimation

K-mer based distance estimation is an alternative to estimating evolutionary distance based on multiple alignments. At a high level, the distance between two sequences is defined by first collecting the set of k-mers (subsequences of length k) occuring in the two sequences. From these two sets, the evolutionary distance between the two organisms is now defined by measuring how different the two sets are. The more the two sets look alike, the smaller is the evolutionary distance. The main motivation for estimating evolutionary distance based on k-mers, is that it is computationally much faster than first constructing a multiple alignment. Experiments show that phylogenetic tree reconstruction using k-mer based distances can produce results comparable to the slower multiple alignment based methods [Blaisdell, 1989].

All of the k-mer based distance measures completely ignores the ordering of the k-mers inside the input sequences. Hence, if the selected k value (the length of the sequences) is too small, very distantly related organisms may be assigned a small evolutionary distance (in the extreme case where k is $1$ , two organisms will be treated as being identical if the frequency of each nucleotide/amino-acid is the same in the two corresponding sequences). In the other extreme, the k-mers should have a length (k) that is somewhat below the average distance between mismatches if the input sequences were aligned (in the extreme case of k=the length of the sequences, two organisms have a maximum distance if they are not identical). Thus the selected k value should not be too large and not too small. A general rule of thumb is to only use k-mer based distance estimation for organisms that are not too distantly related.

Formal definition of distance. In the following, we give a more formal definition of the three supported distance measures: Euclidian-squared, Mahalanobis and Fractional common k-mer count. For all three, we first associate a point $p(s)$ to every input sequence $s$ . Each point $p(s)$ has one coordinate for every possible length k sequence (e.g. if $s$ represent nucleotide sequences, then $p(s)$ has $4^k$ coordinates). The coordinate corresponding to a length k sequence $x$ has the value: ``number of times $x$ occurs as a subsequence in $s$ ''. Now for two sequences $s_1$ and $s_2$ , their evolutionary distance is defined as follows:

• Euclidian squared: For this measure, the distance is simply defined as the (squared Euclidian) distance between the two points $p(s_1)$ and $p(s_2)$ , i.e.
  $$\textrm{dist}(s_1,s_2) = \sum_i (p(s_1)_i - p(s_2)_i)^2.$$

• Mahalanobis: This measure is essentially a fine-tuned version of the Euclidian squared distance measure. Here all the counts $p(s_j)_i$ are ``normalized'' by dividing with the standard deviation $\sigma_j$ of the count for the k-mer. The revised formula thus becomes:
  $$\textrm{dist}(s_1,s_2) = \sum_i(p(s_1)_i/\sigma_i - p(s_2)_i/\sigma_i)^2.$$
  Here the standard deviations can be computed directly from a set of equilibrium frequencies for the different bases, see [Gentleman and Mullin, 1989].

• Fractional common k-mer count: For the last measure, the distance is computed based on the minimum count of every k-mer in the two sequences, thus if two sequences are very different, the minimums will all be small. The formula is as follows:
  $$\textrm{dist}(s_1,s_2) = \log(0.1 + \sum_i(\min(p(s_1)_i,p(s_2)_i)/(\min(n,m)-k+1))).$$
  Here $n$ is the length of $s_1$ and $m$ is the length of $s_2$ . This method has been described in [Edgar, 2004].

In experiments performed in [Höhl et al., 2007], the Mahalanobis distance measure seemed to be the best performing of the three supported measures.