Integrating sequence composition information into microbial diversity analyses with k-mer frequency counting

K-mer frequency information in biological sequences is used for a wide range of applications, including taxonomy classification, sequence similarity estimation, and supervised learning. However, in spite of its widespread utility, k-mer counting has been largely neglected for diversity estimation. This work examines the application of k-mer counting for alpha and beta diversity as well as supervised classification from microbiome marker-gene sequencing datasets (16S rRNA gene and full-length fungal ITS sequences). Results demonstrate a close correspondence with phylogenetically aware diversity metrics, and advantages for using k-mer-based metrics for measuring microbial biodiversity in microbiome sequencing surveys. K-mer counting appears to be a suitable and efficient strategy for feature processing prior to diversity estimation as well as supervised learning in microbiome surveys. This allows incorporation of subsequence-level information into diversity estimation without the computational cost of pairwise sequence alignment. K-mer counting is proposed as a complementary approach for feature processing prior to diversity estimation and supervised learning analyses, enabling large-scale reference-free profiling of microbiomes in biogeography, ecology, and biomedical data. A method for k-mer counting from marker-gene sequence data is implemented in the QIIME 2 plugin q2-kmerizer (https://github.com/bokulich-lab/q2-kmerizer). ImportanceK-mers are all of the subsequences of length k that comprise a sequence. Comparing the frequency of k-mers in DNA sequences yields valuable information about the composition of these sequences and their similarity. This work demonstrates that k-mer frequencies from marker-gene sequence surveys can be used to inform diversity estimates and machine learning predictions that incorporate sequence composition information. Alpha and beta diversity estimates based on k-mer frequencies closely correspond to phylogenetically aware diversity metrics, suggesting that k-mer-based diversity estimates are useful proxy measurements especially when reliable phylogenies are not available, as is often the case for some DNA sequence targets such as for internal transcribed spacer sequences.