LexicHash: sequence similarity estimation via lexicographic comparison of hashes.

ABSTRACT:

Motivation

Pairwise sequence alignment is a heavy computational burden, particularly in the context of third-generation sequencing technologies. This issue is commonly addressed by approximately estimating sequence similarities using a hash-based method such as MinHash. In MinHash, all k-mers in a read are hashed and the minimum hash value, the min-hash, is stored. Pairwise similarities can then be estimated by counting the number of min-hash matches between a pair of reads, across many distinct hash functions. The choice of the parameter k controls an important tradeoff in the task of identifying alignments: larger k-values give greater confidence in the identification of alignments (high precision) but can lead to many missing alignments (low recall), particularly in the presence

SUBMITTER: Greenberg G 

PROVIDER: S-EPMC10628434 | biostudies-literature | 2023 Nov

REPOSITORIES: biostudies-literature