We propose a polynomial algorithm computing a minimum plain-text representation of k-mer sets, as well as an efficient near-minimum greedy heuristic. When compressing read sets of large model organisms or bacterial pangenomes, with only a minor runtime increase, we shrink the representation by up to 59% over unitigs and 26% over previous work. Additionally, the number of strings is decreased by up to 97% over unitigs and 90% over previous work. Finally, a small representation has advantages in downstream applications, as it speeds up SSHash-Lite queries by up to 4.26x over unitigs and 2.10x over previous work.

Matchtigs: minimum plain text representation of k-mer sets

Pibiri, Giulio E;
2023-01-01

Abstract

We propose a polynomial algorithm computing a minimum plain-text representation of k-mer sets, as well as an efficient near-minimum greedy heuristic. When compressing read sets of large model organisms or bacterial pangenomes, with only a minor runtime increase, we shrink the representation by up to 59% over unitigs and 26% over previous work. Additionally, the number of strings is decreased by up to 97% over unitigs and 90% over previous work. Finally, a small representation has advantages in downstream applications, as it speeds up SSHash-Lite queries by up to 4.26x over unitigs and 2.10x over previous work.
2023
24
File in questo prodotto:
File Dimensione Formato  
GBIO2023.pdf

accesso aperto

Tipologia: Versione dell'editore
Licenza: Accesso libero (no vincoli)
Dimensione 2.75 MB
Formato Adobe PDF
2.75 MB Adobe PDF Visualizza/Apri

I documenti in ARCA sono protetti da copyright e tutti i diritti sono riservati, salvo diversa indicazione.

Utilizza questo identificativo per citare o creare un link a questo documento: https://hdl.handle.net/10278/5034706
Citazioni
  • ???jsp.display-item.citation.pmc??? 5
  • Scopus 3
  • ???jsp.display-item.citation.isi??? 2
social impact