A performant bridge between fixed-size and variable-size seeding

Abstract

Background: Seeding is usually the initial step of high-throughput sequence aligners. Two popular seeding strategies are fixed-size seeding (k-mers, minimizers) and variable-size seeding (MEMs, SMEMs, maximal spanning seeds). The former strategy supports fast seed computation, while the latter one benefits from a high seed uniqueness. Algorithmic bridges between instances of both seeding strategies are of interest for combining their respective advantages. Results: We introduce an efficient strategy for computing MEMs out of fixed-size seeds (k-mers or minimizers). In contrast to previously proposed extend-purge strategies, our merge-extend strategy prevents the creation and filtering of duplicate MEMs. Further, we describe techniques for extracting SMEMs or maximal spanning seeds out of MEMs. A comprehensive benchmarking shows the applicability, strengths, shortcomings and computational requirements of all discussed seeding techniques. Additionally, we report the effects of seed occurrence filters in the context of these techniques. Aside from our novel algorithmic approaches, we analyze hierarchies within fixedsize and variable-size seeding along with a mapping between instances of both seeding strategies. Conclusion: Benchmarking shows that our proposed merge-extend strategy for MEM computation outperforms previous extend-purge strategies in the context of PacBio reads. The observed superiority grows with increasing read size and read quality. Further, the presented filters for extracting SMEMs or maximal spanning seeds out of MEMs outperform FMD-index based extension techniques. All code used for benchmarking is available via GitHub at https://github.com/ITBE-Lab/seed-evaluation.

MSV: a modular structural variant caller that reveals nested and complex rearrangements by unifying breakends inferred directly from reads

“…4 C). The required basic techniques for obtaining such seeds using an index computed on the fly are proposed in [ 35 ]. There, a hash table is used for computing Minimizers that are merged and extended afterward for obtaining MEMs.…”

“…For 1, we compute MEMs of a specific minimum size using Minimizers [ 41 ] and a merge-extend scheme [ 35 ]. In this context, we apply an occurrence filtering that eliminates hash table entries of Minimizers with too many matches on the reference genome or the sequenced genome.…”

“… is chosen so that the probability of a random match of size in an area of size is lower than 1%. For each rectangular area, we reseed by computing a single-use Minimizer index and by utilizing the merge-extend scheme proposed in [ 35 ] for turning the Minimizers into MEMs. The above procedure is repeatedly applied until it does not deliver new MEMs.…”