Background <p>DNA adenine methyltransferase identification followed by sequencing (DamID-seq) is a powerful method used to map genome-wide chromatin-protein interactions. However, the bioinformatic analysis of DamID-seq data presents significant challenges due to the inherent complexities of the data and a notable lack of comprehensive software solutions for data-processing and downstream analysis.</p> Results <p>To address these challenges, we present a comprehensive bioinformatic workflow for DamID-seq data analysis, DamMapper, using the Snakemake workflow management system. Key features include straightforward processing of multiple biological replicates, visualisation of quality control, such as correlation heatmaps and principal component analysis (PCA), and robust code quality maintained through continuous integration (CI). Reproducibility is ensured across diverse computational environments, including cloud computing and high-performance computing (HPC) clusters, through the implementation of software environments (Conda) and containerisation (Docker/Apptainer). We validate this workflow using a previously published DamID-seq dataset and apply it to analyse novel datasets for proteins involved in the hypoxia response, specifically the transcription factor HIF-1α and the histone methyltransferase SET1B. This application reveals a strong concordance between our HIF-1α DamID-seq results and ChIP-seq data, and importantly, provides the first genome-wide DNA binding map for SET1B.</p> Conclusions <p>This work provides a validated, reproducible, and feature-rich workflow that overcomes common hurdles in DamID-seq data analysis. By streamlining the processing and ensuring robustness, DamMapper facilitates reliable analysis and enables new biological discoveries, as demonstrated by the characterization of SET1B binding sites. The workflow is available under an MIT license at <a href="https://github.com/niekwit/damid-seq">https://github.com/niekwit/damid-seq</a>.</p>