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Kmers and super-kmers

Kmers

A kmer is a DNA subsequence of fixed length \(k\). Two constraints apply to \(k\), both enforced when a command starts (an invalid value exits immediately with an error):

  • \(k \in [11, 31]\): long enough to be specific, short enough to fit in a single 64-bit word at 2 bits/base (\(k \le 32\) is the hard limit; \(k < 11\) gives insufficient specificity).
  • \(k\) is odd: an odd-length sequence can never equal its own reverse complement, so the two orientations of any kmer are always distinct. This is required for the canonical form (see DNA encoding) to be well defined.

Super-kmers

A super-kmer is a maximal run of consecutive, overlapping kmers from a read that share the same canonical minimizer (see Minimizer selection). Each kmer in the run overlaps the next by \(k-1\) nucleotides. A super-kmer is capped at 256 nucleotides; a longer run is split at that boundary.

For a random minimizer of length \(m\) over kmers of length \(k\), the expected length of a super-kmer is approximately (Golan & Shur 2025; Zheng et al. 2020)1 2:

\[L_{\text{nt}} \approx \frac{k-m+2}{2} + k - 1\]

For \(k=31\), \(m=13\) this is about 40 nucleotides; in practice super-kmers rarely exceed a few dozen nucleotides.

Canonical super-kmers

A canonical super-kmer is the lexicographic minimum of a super-kmer and its reverse complement. When a read and its reverse complement are both encountered, they produce super-kmers that are reverse complements of each other; both reduce to the same canonical super-kmer, so a genomic region is represented once regardless of which strand was read.

Super-kmers are the unit of work used throughout construction and querying: sequences are decomposed into super-kmers first, and every downstream step (partition routing, deduplication, counting) operates on them rather than on individual kmers.

  1. Zheng, H., Kingsford, C. & Marçais, G. (2020). Improved design and analysis of practical minimizers. Bioinformatics (Oxford, England), 36, i119--i127. ↩

  2. Golan, S. & Shur, A.M. (2025). Expected density of random minimizers. In: Lecture notes in computer science, Lecture notes in computer science. Springer Nature Switzerland, Cham, pp. 347--360. ↩