Sequence aligners can guarantee accuracy in almostO(mlogn) time: a rigorous average-case analysis of the seed-chain-extend heuristic

Abstract

Seed-chain-extend with k-mer seeds is a powerful heuristic technique for sequence alignment employed by modern sequence aligners. While effective in practice for both runtime and accuracy, theoretical guarantees on the resulting alignment do not exist for seed-chain-extend. In this work, we give the first rigorous bounds for the efficacy of seed-chain-extend with k-mers in expectation. Assume we are given a random nucleotide sequence of length ≈ n that is indexed (or seeded) and a mutated substring of length ≈ m ≤ n with mutation rate θ < 0.206. We prove that we can find a k = Θ(log n) for the k-mer size such that the expected runtime of seed-chain-extend under optimal linear gap cost chaining and quadratic time gap extension is O(mnf(θ)log n) where f(θ) < 2.43·θ holds as a loose bound. In fact, for reasonable θ = 0.05, f(θ) < 0.08, indicating nearly quasilinear running time in practice. The alignment also turns out to be good; we prove that more than 1 − O(1/√m) fraction of the homologous bases are recoverable under an optimal chain. We also show that our bounds work when k-mers are sketched, i.e. only a subset of all k-mers is selected. Under the open syncmer sketching method, one can sketch with decreasing density as a function of n and achieve asymptotically smaller chaining time, yet the same bounds for extension time and recoverability hold. In other words, sketching reduces chaining time without increasing alignment time or decreasing accuracy too much, justifying the effectiveness of sketching as a practical speedup in sequence alignment. We verify our results in simulation and conjecture that f(θ) can be further reduced.

Fast and robust metagenomic sequence comparison through sparse chaining with skani

“…Under the usual assumption of no repetitive k-mers [19], it is easy to estimate θ from k-mer matching statistics [7,19,4] between G and G ′ . We proved in a previous work that for random, mutating strings, the expected value of such spurious matches for a string of length n is (Theorem 2 in Shaw and Yu [20]), so this is not a bad assumption in practice when k is reasonably large and for simpler, non-eukaryotic genomes. However, when dealing with MAGs, we don’t have G and G ′ but instead fragmented, contaminated, and incomplete versions of G and G ′ .…”

Haplotype-aware sequence alignment to pangenome graphs

“…Most alignment tools use seed-chain-extend heuristic to compute the alignments quickly [53,54]. Given a set of seed matches (anchors) as input, co-linear chaining is a rigorous optimization technique to identify promising alignment regions in a reference.…”

A survey of mapping algorithms in the long-reads era

“…There has also been recent advancement on the theoretical side. In [92], the authors show that long read mapping using the seed-chain-extend method is both fast and accurate with some guarantees on the average-case time complexity. We therefore believe this methodology will continue to be a popular approach in the domain of long read mapping.…”