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bioRxiv · 10.64898/2026.08.21.746151

Addressing technical variations in ATAC-seq data and improving motif accessibility analyses

Wang, J.·Sonder, E.·Domcke, S.·Robinson, M. D.·Germain, P.-L.
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Abstract

Tagmentation-based methods such as ATAC-seq and Cut&Tag have provided easy ways to profile the epigenome in low-input samples and even single cells. In this contribution, we discuss forms of bias (i.e. technical variations) in tagmentation-based data, in particular ATAC-seq, and introduce three R/bioconductor packages to facilitate bulk and single-cell epigenomic data analysis, with a special focus on motif accessibility analysis. The weightedMotifAccess package uses weight models to enable motif accessibility analysis, including transcription factor footprint information. The betterChromVAR package provides a novel, analytical re-implementation of the popular chromVAR method that offers substantial speed improvements, eliminates stochasticity, and offers additional features. Based on this, we also propose a method, CVnorm, that outperforms alternatives in normalizing technical bias in peak count data. The computational efficiency of these tools further enables a new framework for systematically investigating synergistic and antagonistic interactions between transcription factor motifs. Finally, the epiwraps package streamlines the visualization, normalization, and summarization of epigenomic data.

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Wang, J., Sonder, E., Domcke, S., Robinson, M. D., Germain, P.-L.. 2026-08-25. Addressing technical variations in ATAC-seq data and improving motif accessibility analyses. https://doi.org/10.64898/2026.08.21.746151

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