The mod-minimizer: a simple and efficient sampling algorithm for long k -mers
Ragnar Groot Koerkamp, Giulio Ermanno Pibiri Β· bioRxiv (Cold Spring Harbor Laboratory) Β· 2024
Abstract Motivation Given a string S , a minimizer scheme is an algorithm defined by a triple ( k, w , πͺ) that samples a subset of k -mers ( k -long substrings) from a string S . Specifically, it samples the minimal k -mer according to the order πͺ from each window of w consecutive k -mers in S . Because consecutive windows can sample the same k -mer, the set of the sampled k -mers is typically much smaller than S . More generally, we consider substring sampling algorithms that respect a window guarantee : at least one k -mer must be sampled from every window of w consecutive k -mers. As a sampled k -mer is uniquely identified by its absolute position in S , we can define the density of a sampling algorithm as the fraction of distinct sampled positions. Good methods have low density which, by respecting the window guarantee, is lower bounded by 1 /w . It is however difficult to design a sequence-agnostic algorithm with provably optimal density. In practice, the order πͺ is usually implemented using a pseudo-random hash function to obtain the so-called random minimizer. This scheme is simple to implement, very fast to compute even in streaming fashion, and easy to analyze. However, its density is almost a factor of 2 away from the lower bound for large windows. Methods In this work we introduce mod-sampling , a two-step sampling algorithm to obtain new minimizer schemes. Given a (small) parameter t , the mod-sampling algorithm finds the position i of the minimal t -mer in a window. It then samples the k -mer at position i mod w . The lr-minimizer uses t = k β w and the mod-minimizer uses t β‘ k (mod w ). Results These new schemes have provably lower density than random minimizers and other schemes when k is large compared to w , while being as fast to compute. Importantly, the mod-minimizer achieves optimal density when k β β. Although the mod-minimizer is not the first method to achieve optimal density for large k , its proof of optimality is simpler than previous work. We provide pseudocode for a number of other methods and compare to them. In practice, the mod-minimizer has considerably lower density than the random minimizer and other state-of-the-art methods, like closed syncmers and miniception, when k > w . We plugged the mod-minimizer into SSHash, a k -mer dictionary based on minimizers. For default parameters ( w, k ) = (11, 21), space usage decreases by 15% when indexing the whole human genome (GRCh38), while maintaining its fast query time.