AbstractSummaryThe (m,n)-mer is a simple alternative classification feature based on conditional probability distributions. In this application note, we comparedk-mer and (m,n)-mer frequency features in 11 distinct datasets used for binary, multiclass and clustering classifications. Our findings show that the (m,n)-mer frequency features are related to the highest performance metrics and often statistically outperformed thek-mers. Here, the (m,n)-mer frequencies improved performance for classifying smaller sequence lengths (as short as 300 bp) and yielded higher metrics when using short values ofk(ranging from 2 to 4). Therefore, we present the (m,n)-mers frequencies to the scientific community as a feature that seems to be quite effective in identifying complex discriminatory patterns and classifying polyphyletic sequence groups.Availability and implementationThe (m,n)-mer algorithm is released as an R package within the CRAN project (https://cran.r-project.org/web/packages/mnmer) and is also available athttps://github.com/labinfo-lncc/mnmer.Supplementary informationSupplementary dataare available atBioinformatics Advancesonline.