https://github.com/cumbof/kitsune
a toolkit for evaluation of the lenght of k-mer in a given genome dataset for alignment-free phylogenimic analysis.
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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: K-mer-length Iterative Selection for UNbiased Ecophylogenomics[](https://badge.fury.io/py/kitsune)  KITSUNE is a toolkit for evaluation of the length 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. The optimum k-mer length is a prerequsite to obtain biological meaningful genomic distance for assesment of phylogenetic relationships. Therefore, we have developed KITSUNE to aid k-mer length selection process in a systematic way, 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](https://doi.org/10.1038/srep40712). KITSUNE will calculte the three matrices across considered k-mer range: 1. Cumulative Relative Entropy (CRE) 1. Averrage number of Common Feature (ACF) 1. Obserbed Common Feature (OCF) Moreover, KITSUNE also provides various genomic distance calculations from the k-mer frequency vectors that can be used for species identification or phylogenomic tree construction. > **Note**: > If you use KITSUNE in your research, please cite: > [KITSUNE: A Tool for Identifying Empirically Optimal K-mer Length for Alignment-free Phylogenomic Analysis](https://doi.org/10.3389/fbioe.2020.556413) ## Installation Kitsune is developed under python version 3 environment. We recommend users use python >= v3.5. Requirement packages: scipy >= 0.18.1, numpy >= 1.1.0, tqdm >= 4.32 Kitsune also requires [Jellyfish](https://doi.org/10.1093/bioinformatics/btr011) for k-mer counting as external software dependency. Thus, you need to install it before running the tool: [https://github.com/gmarcais/Jellyfish](https://github.com/gmarcais/Jellyfish) ### Install with pip ```bash pip install kitsune ``` ### Install from source ```bash # Clone the GitHub repository git clone https://github.com/natapol/kitsune # Move to the kitsune folder cd kitsune/ # Install python setup.py install ``` ## Usage ### Overview of kitsune command for listing help ```bash $ kitsune --help usage: kitsune
[ ] Available commands: acf Compute average number of common feature between signatures cre Compute cumulative relative entropy dmatrix Compute distance matrix kopt Compute recommended choice (optimal) of kmer within a given kmer interval for a set of genomes using the cre, acf and ofc ofc Compute observed feature frequencies Use --help in conjunction with one of the commands above for a list of available options (e.g. kitsune acf --help) ``` ### Calculate CRE, ACF, and OFC value for specific kmer Kitsune provides three commands to calculate an appropiate k-mer using CRE, ACF, and OCF: #### Calculate CRE ```bash $ kitsune cre -h usage: kitsune (cre) [-h] --filename FILENAME [--fast] [--canonical] -ke KEND [-kf KFROM] [-t THREAD] [-o OUTPUT] Calculate k-mer from cumulative relative entropy of all genomes optional arguments: -h, --help show this help message and exit --filename FILENAME A genome file in fasta format (default: None) --fast Jellyfish one-pass calculation (faster) (default: False) --canonical Jellyfish count only canonical mer (default: False) -ke KEND, --kend KEND Last k-mer (default: None) -kf KFROM, --kfrom KFROM Calculate from k-mer (default: 4) -t THREAD, --thread THREAD -o OUTPUT, --output OUTPUT Output filename (default: None) ``` #### Calculate ACF ```bash $ kitsune acf -h usage: kitsune (acf) [-h] --filenames FILENAMES [FILENAMES ...] [--fast] [--canonical] -k KMERS [KMERS ...] [-t THREAD] [-o OUTPUT] Calculate an average number of common feature pairwise between one genome against others optional arguments: -h, --help show this help message and exit --filenames FILENAMES [FILENAMES ...] Genome files in fasta format (default: None) --fast Jellyfish one-pass calculation (faster) (default: False) --canonical Jellyfish count only canonical mer (default: False) -k KMERS [KMERS ...], --kmers KMERS [KMERS ...] Have to state before (default: None) -t THREAD, --thread THREAD -o OUTPUT, --output OUTPUT Output filename (default: None) ``` #### Calculate OFC ```bash $ kitsune ofc -h usage: kitsune (ofc) [-h] --filenames FILENAMES [FILENAMES ...] [--fast] [--canonical] -k KMERS [KMERS ...] [-t THREAD] [-o OUTPUT] Calculate an observe feature frequency optional arguments: -h, --help show this help message and exit --filenames FILENAMES [FILENAMES ...] Genome files in fasta format (default: None) --fast Jellyfish one-pass calculation (faster) (default: False) --canonical Jellyfish count only canonical mer (default: False) -k KMERS [KMERS ...], --kmers KMERS [KMERS ...] -t THREAD, --thread THREAD -o OUTPUT, --output OUTPUT Output filename (default: None) ``` #### General Example ```bash kitsune cre --filenames genome1.fna -kf 5 -ke 10 kitsune acf --filenames genome1.fna genome2.fna -k 5 kitsune ofc --filenames 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. Users can assess the impact of a selected k-mer length on the genomic distnace of choice below. 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 euclidean_of_frequency | Euclidean distance of Frequency Kitsune provides a choice of distance transformation proposed by [Fan et.al](https://doi.org/10.1186/s12864-015-1647-5). ### Calculate a distance matrix ```bash $ kitsune dmatrix -h usage: kitsune (dmatrix) [-h] [--filenames [FILENAMES [FILENAMES ...]]] [--fast] [--canonical] -k KMER [-i INPUT] [-o OUTPUT] [-t THREAD] [--transformed] [-d {braycurtis,canberra,jsmash,chebyshev,cityblock,correlation,cosine,dice,euclidean,hamming,jaccard,kulsinsk,matching,rogerstanimoto,russellrao,sokalmichener,sokalsneath,sqeuclidean,yule,mash,jaccarddistp}] [-f FORMAT] Calculate a distance matrix optional arguments: -h, --help show this help message and exit --filenames [FILENAMES [FILENAMES ...]] Genome files in fasta format (default: None) --fast Jellyfish one-pass calculation (faster) (default: False) --canonical Jellyfish count only canonical mer (default: False) -k KMER, --kmer KMER -i INPUT, --input INPUT List of genome files in txt (default: None) -o OUTPUT, --output OUTPUT Output filename (default: None) -t THREAD, --thread THREAD --transformed -d {braycurtis,canberra,jsmash,chebyshev,cityblock,correlation,cosine,dice,euclidean,hamming,jaccard,kulsinsk,matching,rogerstanimoto,russellrao,sokalmichener,sokalsneath,sqeuclidean,yule,mash,jaccarddistp}, --distance {braycurtis,canberra,jsmash,chebyshev,cityblock,correlation,cosine,dice,euclidean,hamming,jaccard,kulsinsk,matching,rogerstanimoto,russellrao,sokalmichener,sokalsneath,sqeuclidean,yule,mash,jaccarddistp} -f FORMAT, --format FORMAT ``` Example of choosing distance option: ```bash kitsune dmatrix --filenames genome1.fna genome2.fna -k 11 -d jaccard --canonical --fast -o output.txt kitsune dmatrix --filenames genome1.fna genome2.fna -k 11 -d hensenshannon --canonical --fast -o output.txt ``` ### Find optimum k-mer from a given set of genomes Kitsune provides a wrap-up comand to find optimum k-mer length for a given set of genome within a given kmer interval. ```bash $ kitsune kopt -h usage: kitsune (ofc) [-h] --filenames FILENAMES [--fast] [--canonical] -kl KLARGE [-o OUTPUT] [--closely_related] [-x CRE_CUTOFF] [-y ACF_CUTOFF] [-t THREAD] [-n NPROC] Find the recommended optimal kmer using acf,cre and ofc for a given set of genomes. Example: kitsune kopt genomeList.txt -kl 15 --canonical --fast -t 4 -o out.txt optional arguments: -h, --help show this help message and exit --filenames FILENAMES A file that list the path to all genomes (fasta format) with extension as (.txt, .csv, .tab) or no extension (default: None) --fast Jellyfish one-pass calculation (faster) (default: False) --canonical Jellyfish count only canonical mer (default: False) -kl KLARGE, --klarge KLARGE Largest k-mer length to consider, note: the smallest kmer length is 4 (default: None) -o OUTPUT, --output OUTPUT Output filename (default: None) --closely_related For closely related set of genomes, use this option (default: False) -x CRE_CUTOFF, --cre_cutoff CRE_CUTOFF Cutoff to use in selecting kmers whose cre's are <= (cutoff * max(cre)), Default = 10 percent, ie x=0.1 (default: 0.1) -y ACF_CUTOFF, --acf_cutoff ACF_CUTOFF Cutoff to use in selecting kmers whose acf's are >= (cutoff * max(acf)), Default = 10 percent, ie y=0.1 (default: 0.1) -t THREAD, --thread THREAD Number of threads (default: 1) -n NPROC, --nproc NPROC Number of processes (default: 1) ``` ### Example dataset First download the example files. [Download](examples/example_viral_genomes.zip) ```bash kitsune kopt --filenames genome_list -kl 15 --canonical --fast -t 4 -n 1 -o out.txt ``` > :warning: _Please be aware that this command will use big computational resources when large number of genomes and/or large genome size are used as the input._
Owner
- Name: Fabio Cumbo
- Login: cumbof
- Kind: user
- Location: Cleveland, OH, USA
- Company: Genomic Medicine Institute, Lerner Research Institute, Cleveland Clinic
- Website: https://cumbof.github.io/
- Twitter: cumbofabio
- Repositories: 14
- Profile: https://github.com/cumbof
Ph.D. in Computer Science and Automation Engineering, Postdoctoral Research Fellow @BlankenbergLab, GMI, LRI, Cleveland Clinic, USA