ModSeqR: an R package for efficient analysis of modified nucleotide data

DNA methylation regulates a wide range of biological processes, including gene expression, disease progression, and cell identity. Long-read technologies now enable more comprehensive and accurate methylome analyses than ever before, but they are hindered by the computational resources needed to analyze the massive datasets. Here, we present the CH3 file format, which aids data storage and transfer by reducing file sizes by more than 95%, and the ModSeqR R package, which builds on the CH3 format and a database backend to enable a broad range of epigenetic analyses. Together, these tools enable high-throughput methylation analysis while minimizing computational resource requirements.

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Publication Details

Journal
BMC Genomics
Published
2026-02-22
DOI
https://doi.org/10.1186/s12864-026-12655-5
Primary Topic
Gene expression and cancer classification
Type
article
Field-Weighted Citation Impact
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article

ModSeqR: an R package for efficient analysis of modified nucleotide data

Hailey E. Johnson, Jonathon T. Hill, Isaac Stirland, Ryan H. Miller et al.
BMC Genomics
Gene expression and cancer classification
article

ModSeqR: an R package for efficient analysis of modified nucleotide data

Hailey E. Johnson, Jonathon T. Hill, Isaac Stirland, Ryan H. Miller, Erin Saito, Tim Jenkins, Jordan L Moore
article en

Abstract

DNA methylation regulates a wide range of biological processes, including gene expression, disease progression, and cell identity. Long-read technologies now enable more comprehensive and accurate methylome analyses than ever before, but they are hindered by the computational resources needed to analyze the massive datasets. Here, we present the CH3 file format, which aids data storage and transfer by reducing file sizes by more than 95%, and the ModSeqR R package, which builds on the CH3 format and a database backend to enable a broad range of epigenetic analyses. Together, these tools enable high-throughput methylation analysis while minimizing computational resource requirements.

BMC GenomicsVol. 27(1)
Brigham Young University (US), Atkins (United States) (US), JT Pharmaceuticals (United States) (US), Fortem Technologies (United States) (US)
Openalex Percentile: Top 46%
Gene expression and cancer classification
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