Contains tools for rapidly computing distance matrices 
    and clustering large sequence datasets using fast alignment-free 
    k-mer counting and recursive k-means partitioning. 
    See Vinga and Almeida (2003) <doi:10.1093/bioinformatics/btg005> 
    for a review of k-mer counting methods and applications for 
    biological sequence analysis.

Similarity Score Matrix and HeatMap for nucleic and amino acid k-mers. Similarity score is evaluated by Point Accepted Mutation (PAM) and BLOcks SUbstitution Matrix (BLOSUM). The 30, 40, 70, 120, 250 and 62, 45, 50, 62, 80, 100 matrix versions are available for PAM and BLOSUM, respectively. Alignment is evaluated by local and global alignment.

A multi-purpose and flexible k-meric enrichment analysis software. 
    'kmeRtone' measures the enrichment of k-mers by comparing the population of 
    k-mers in the case loci with a carefully devised internal negative control 
    group, consisting of k-mers from regions close to, yet sufficiently distant 
    from, the case loci to mitigate any potential sequencing bias. This method 
    effectively captures both the local sequencing variations and broader 
    sequence influences, while also correcting for potential biases, thereby 
    ensuring more accurate analysis. The core functionality of 'kmeRtone' is 
    the SCORE() function, which calculates the susceptibility scores for k-mers in 
    case and control regions. Case regions are defined by the genomic coordinates 
    provided in a file by the user and the control regions can be constructed 
    relative to the case regions or provided directly. The k-meric susceptibility 
    scores are calculated by using a one-proportion z-statistic. 'kmeRtone' is 
    highly flexible by allowing users to also specify their target k-mer patterns
    and quantify the corresponding k-mer enrichment scores in the context of 
    these patterns, allowing for a more comprehensive approach to understanding 
    the functional implications of specific DNA sequences on a genomic scale
    (e.g., CT motifs upon UV radiation damage).
    Adib A. Abdullah, Patrick Pflughaupt, Claudia Feng, Aleksandr B. Sahakyan (2024) Bioinformatics (submitted).