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a toolkit for evaluation of the lenght of k-mer in a given genome dataset for alignment-free phylogenimic analysis

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KITSUNE is a toolkit for evaluation of the lenght of k-mer in a given genome dataset for alignment-free phylogenimic analysis.

K-mer based approach is simple and fast yet has been widely used in many applications including biological sequence comparison. However, selection of an appropriate k-mer length to obtain a good information content for comparison is normally overlooked. Therefore, we have developed KITSUNE to aid k-mer length selection process based on a three steps aproach described in Viral Phylogenomics Using an Alignment-Free Method: A Three-Step Approach to Determine Optimal Length of k-mer.

KITSUNE uses Jellyfish software for k-mer counting. Thanks to Jellyfish developer. Citation

KITSUNE will calculte the three matrices across considered k-emer range :

  1. Cumulative Relative Entropy (CRE)
  2. Averrage number of Common Feature (ACF)
  3. Obserbed Common Feature (OCF)

Moreverver, KITSUNE also provides various genomic distance calculations from the k-mer frequnce vectors that can be used for species identifiction or phylogenomic tree construction.

If you use KITSUNE in your research, please cite: Reference


Install throught pip:

pip install kitsune


Calculate CRE, ACF, and OFC value for specific kmer

Kitsune provides three commands to calculate an appropiate k-mer using CRE, ACF, and OCF:

kitsune cre genome_fasta/* -kf 5 -ke 10
kitsune acf genome_fasta/* -k 5
kitsune ofc genome_fasta/* -k 5

Calculate genomic distance at specific k-mer from kmer frequency vectors of two of genomes

Kitsune provides a commands to calculate genomic distance using different distance estimation method.

distance option name
braycurtis Bray-Curtis distance
canberra Canberra distance
chebyshev Chebyshev distance
cityblock City Block (Manhattan) distance
correlation Correlation distance
cosine Cosine distance
euclidean Euclidean distance
jensenshannon Jensen-Shannon distance
sqeuclidean Squared Euclidean distance
dice Dice dissimilarity
hamming Hamming distance
jaccard Jaccard-Needham dissimilarity
kulsinski Kulsinski dissimilarity
rogerstanimoto Rogers-Tanimoto dissimilarity
russellrao Russell-Rao dissimilarity
sokalmichener Sokal-Michener dissimilarity
sokalsneath Sokal-Sneath dissimilarity
yule Yule dissimilarity
mash MASH distance
jsmash MASH Jensen-Shannon distance
jaccarddistp Jaccard-Needham dissimilarity Probability

Example of choosing distance option:

kitsune dmatrix genome1.fna genome2.fna -k 17 -d jaccard --canonical --fast -o output.txt
kitsune dmatrix genome1.fna genome2.fna -k 17 -d hensenshannon --canonical --fast -o output.txt

Find optimum k-mer from a given set of genome

Kitsune provides a comand to find optimum k-mer length in agiven set of genome.

First download the example files.Download

Then use kitsune kopt command

-i : path to list of genome files

-kl: The largest kmer-length to consider

-o: output file

**Please be aware that this comand will use big computational resources when large number of genomes and/or large genome size are used as the input.

kitsune kopt -i genome_list -kl 15 --canonical --fast -o output.txt

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