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Genomic sketching with multiplicities and locality-sensitive hashing using Dashing 2

A genomic sketch is a small, probabilistic representation of the set of [Formula: see text]-mers in a sequencing data set. Sketches are building blocks for large-scale analyses that consider similarities between many pairs of sequences or sequence collections. Although existing tools can easily comp...

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Detalles Bibliográficos
Autores principales: Baker, Daniel N., Langmead, Ben
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Cold Spring Harbor Laboratory Press 2023
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10538361/
https://www.ncbi.nlm.nih.gov/pubmed/37414575
http://dx.doi.org/10.1101/gr.277655.123
Descripción
Sumario:A genomic sketch is a small, probabilistic representation of the set of [Formula: see text]-mers in a sequencing data set. Sketches are building blocks for large-scale analyses that consider similarities between many pairs of sequences or sequence collections. Although existing tools can easily compare tens of thousands of genomes, data sets can reach millions of sequences and beyond. Popular tools also fail to consider [Formula: see text]-mer multiplicities, making them less applicable in quantitative settings. Here, we describe a method called Dashing 2 that builds on the SetSketch data structure. SetSketch is related to HyperLogLog (HLL) but discards use of leading zero count in favor of a truncated logarithm of adjustable base. Unlike HLL, SetSketch can perform multiplicity-aware sketching when combined with the ProbMinHash method. Dashing 2 integrates locality-sensitive hashing to scale all-pairs comparisons to millions of sequences. It achieves superior similarity estimates for the Jaccard coefficient and average nucleotide identity compared with the original Dashing, but in much less time while using the same-sized sketch. Dashing 2 is a free, open source software.