DOI: 10.5281/zenodo.17582115
title: Eulertigs for benchmarking kmer dictionaries

description: Introduction

The datasets in this collection are meant to benchmark kmer dictionaries (i.e., data structures that represent a set of kmers and support, at least, exact membership queries), like SSHash [1], SBWT [2], and FMSI [3].

There are two types of datasets in this collection:



Those with extension eulertigs.fa.gz contain the actual kmers to be indexed in FASTA format, represented as substrings of longer eulertigs [4]. Eulertigs were computed using the GGCAT algorithm [5] (commit 14b2853731787495d0874c7ec7b6ca3ee97cd3a4), for both k=31 and k=63.

Those with extension fastq.gz contain reads in FASTQ format can be used to query the dictionaries.


All strings in this collection are relative to the DNA alphabet consisting in the four symbols {A, C, G, T}.

Whole genomes: cod, kestrel, human

The datasets headed "cod", "kestrel", and "human" were obtained by processing with GGCAT the whole genomes of Gadus morhua, Falco tinnunculus, and Homo sapiens respectively, as follows.

wget http://ftp.ensembl.org/pub/current_fasta/gadus_morhua/dna/Gadus_morhua.gadMor3.0.dna.toplevel.fa.gz -O Gadus_morhua.gadMor3.0.dna.toplevel.fa.gzwget http://ftp.ensembl.org/pub/current_fasta/falco_tinnunculus/dna/Falco_tinnunculus.FalTin1.0.dna.toplevel.fa.gz -O Falco_tinnunculus.FalTin1.0.dna.toplevel.fa.gzwget http://ftp.ensembl.org/pub/current_fasta/homo_sapiens/dna/Homo_sapiens.GRCh38.dna.toplevel.fa.gz -O Homo_sapiens.GRCh38.dna.toplevel.fa.gz

ggcat build -k 31 -j 64 ~/Gadus_morhua.gadMor3.0.dna.toplevel.fa.gz -s 1 --eulertigs -o cod.k31.eulertigs.faggcat build -k 63 -j 64 ~/Gadus_morhua.gadMor3.0.dna.toplevel.fa.gz -s 1 --eulertigs -o cod.k63.eulertigs.fa

ggcat build -k 31 -j 64 ~/Falco_tinnunculus.FalTin1.0.dna.toplevel.fa.gz -s 1 --eulertigs -o kestrel.k31.eulertigs.faggcat build -k 63 -j 64 ~/Falco_tinnunculus.FalTin1.0.dna.toplevel.fa.gz -s 1 --eulertigs -o kestrel.k63.eulertigs.fa

ggcat build -k 31 -j 64 ~/Homo_sapiens.GRCh38.dna.toplevel.fa.gz -s 1 --eulertigs -o human.k31.eulertigs.faggcat build -k 63 -j 64 ~/Homo_sapiens.GRCh38.dna.toplevel.fa.gz -s 1 --eulertigs -o human.k63.eulertigs.fa

 

Number of distinct kmers




Collection
Num. distinct 31-mers
Num. distinct 63-mers


Cod
  502,465,200
  556,585,658


Kestrel
1,150,399,205
1,155,250,667


Human
2,505,678,680
2,771,316,093




 

Pangenomes: NCBI-virus, SE, HPRC

The datasets headed "ncbi-virus", "se", and "hprc" are pangenomes.

NCBI-virus

This is a collection of 18,836 virus assemblies downloaded from https://www.ncbi.nlm.nih.gov/labs/virus/vssi/#/virus?SeqType_s=Genome&SourceDB_s=RefSeq in November 2025. After downloading, and assuming the file is named ncbi-virus.fasta.gz, the collection can be processed as follows.

ggcat build -k 31 -j 64 ncbi-virus.fasta.gz -s 1 --eulertigs -o ncbi-virus.k31.eulertigs.faggcat build -k 63 -j 64 ncbi-virus.fasta.gz -s 1 --eulertigs -o ncbi-virus.k63.eulertigs.fa

SE

This is a pangenome containing are all the 534,751 Salmonella enterica genomes from the "All The Bacteria" collection [6] (v0.2).

ggcat build -k 31 -j 64 -l salmonella_enterica-all.txt -s 1 --eulertigs -o se.k31.eulertigs.faggcat build -k 63 -j 64 -l salmonella_enterica-all.txt -s 1 --eulertigs -o se.k63.eulertigs.fa

HPRC

This is a human pangenome. We downloaded the Linux 3.2.1 binary of AGC [7] from https://github.com/refresh-bio/agc/releases and the human472.agc file from https://zenodo.org/records/14854401.

Then, we did:

agc getcol -o output_folder human472.agc

to extract all the individual files to be processed by GGCAT.ggcat build -k 31 -j 64 -l hprc_filenames.txt -s 1 --eulertigs -o hprc.k31.eulertigs.faggcat build -k 63 -j 64 -l hprc_filenames.txt -s 1 --eulertigs -o hprc.k63.eulertigs.fa

 

Number of distinct kmers




Collection
Num. distinct 31-mers
Num. distinct 63-mers


NCBI-virus
   376,205,185
    412,515,880


SE
  894,310,084
 1,524,904,156


HPRC
3,718,120,949
5,926,785,469




 

References



Pibiri, Giulio Ermanno. "Sparse and skew hashing of k-mers." Bioinformatics 38.Supplement_1 (2022): i185-i194.

Alanko, Jarno N., Simon J. Puglisi, and Jaakko Vuohtoniemi. "Small searchable k-spectra via subset rank queries on the spectral burrows-wheeler transform." SIAM Conference on Applied and Computational Discrete Algorithms (ACDA), 2023.



Ondřej Sladký, Pavel Veselý, Karel Břinda. "FroM Superstring to Indexing: a space-efficient index for unconstrained k-mer sets using the Masked Burrows-Wheeler Transform (MBWT)", Bioinformatics Advances, vbaf290, 2025.


Schmidt, Sebastian, and Jarno N. Alanko. "Eulertigs: minimum plain text representation of k-mer sets without repetitions in linear time." Algorithms for Molecular Biology 18.1 (2023): 5.

Cracco, Andrea, and Alexandru I. Tomescu. "Extremely fast construction and querying of compacted and colored de Bruijn graphs with GGCAT." Genome Research 33.7 (2023): 1198-1207.



Hunt et al. "AllTheBacteria – all bacterial genomes assembled, available, and searchable", BioRxiv, 2025. https://www.biorxiv.org/content/10.1101/2024.03.08.584059v7




S. Deorowicz, A. Danek, H. Li. "AGC: Compact representation of assembled genomes with fast queries and updates." Bioinformatics (2023).