A whole-genome sequencing dataset of nanopore raw signals for bacterial genotyping and methylation analysis

This dataset comprises raw signal data from a multicenter study evaluating the accuracy of bacterial whole-genome genotyping using Oxford Nanopore long-read sequencing. The raw data comprises 79 isolates across six bacterial species, including 12 triplicates from three different laboratories (totalling ~1.4 TB of data). Sequencing was conducted on the latest R10.4.1 flow cells with V14 chemistry, producing on average 16 gigabases per flow cell. The generated raw ion current signals retain information beyond nucleotide sequences, supporting in-depth reanalysis for nucleotide modifications, resistance genes, and bacterial strain differentiation. The dataset enables re-basecalling with future models to keep up with the newest developments, e.g. to mitigate methylation-based calling errors, enhancing the reliability of SNP profiling and cgMLST analyses crucial for genomic surveillance. By sharing this raw signal data, accompanied by additional phenotypic resistance-data and an extensive quality control pipeline, we aim to advance reproducibility, support error correction studies and the continued development of bioinformatics tools, and encourage sharing raw data for broader genomic and epigenetic investigations as general best practice.

Authors

Institutions

Publication Details

Journal
Scientific Data
Published
2025-12-02
DOI
https://doi.org/10.1038/s41597-025-06319-4
Primary Topic
Genomics and Phylogenetic Studies
Type
article
Field-Weighted Citation Impact
0.00

Funders

Controls
|||
ALL TIME
JAN
FEB
MAR
APR
MAY
JUN
JUL
AUG
SEP
OCT
article

A whole-genome sequencing dataset of nanopore raw signals for bacterial genotyping and methylation analysis

Johanna Dabernig‐Heinz, Gabriel E. Wagner, Martin Hölzer, Somayyeh Sedaghatjoo et al.
Scientific Data
Genomics and Phylogenetic Studies
article

A whole-genome sequencing dataset of nanopore raw signals for bacterial genotyping and methylation analysis

Johanna Dabernig‐Heinz, Gabriel E. Wagner, Martin Hölzer, Somayyeh Sedaghatjoo, Ivo Steinmetz, Christian Köhler, Valentina Galeone
article en

Abstract

This dataset comprises raw signal data from a multicenter study evaluating the accuracy of bacterial whole-genome genotyping using Oxford Nanopore long-read sequencing. The raw data comprises 79 isolates across six bacterial species, including 12 triplicates from three different laboratories (totalling ~1.4 TB of data). Sequencing was conducted on the latest R10.4.1 flow cells with V14 chemistry, producing on average 16 gigabases per flow cell. The generated raw ion current signals retain information beyond nucleotide sequences, supporting in-depth reanalysis for nucleotide modifications, resistance genes, and bacterial strain differentiation. The dataset enables re-basecalling with future models to keep up with the newest developments, e.g. to mitigate methylation-based calling errors, enhancing the reliability of SNP profiling and cgMLST analyses crucial for genomic surveillance. By sharing this raw signal data, accompanied by additional phenotypic resistance-data and an extensive quality control pipeline, we aim to advance reproducibility, support error correction studies and the continued development of bioinformatics tools, and encourage sharing raw data for broader genomic and epigenetic investigations as general best practice.

Scientific DataVol. 12(1)
Friedrich-Loeffler-Institut (DE), Medical University of Graz (AT), Robert Koch Institute (DE)
Koch Institute for Integrative Cancer Research, Massachusetts Institute of Technology, Universitätsklinikum Jena, Robert Koch Institut
Openalex Percentile: Top 66%
Genomics and Phylogenetic Studies
AI Navigator

Ask Laika to Summarize, Analyze, and Connect papers live on the map.

Summarize Papers & Methodologies

Extract key findings, datasets, and comparative methods across publications.

Benchmark Rankings & Visual Analytics

Rank top research institutions, authors, funders, topics, and journals by Field-Weighted Citation Impact (FWCI) and paper volume with instant charts.

Connect Distant Disciplines

Bridge topological clusters on the map to find hidden collaborative intersections.