Quality assessment and refinement of chromatin accessibility data using a sequence-based predictive model

Seong Kyu Han, Yoshiharu Muto, Parker C. Wilson, Benjamin D. Humphreys, Matthew G. Sampson, Aravinda Chakravarti, Dongwon Lee

Research output: Contribution to journalArticlepeer-review

3 Scopus citations


Chromatin accessibility assays are central to the genome-wide identification of gene regulatory elements associated with transcriptional regulation. However, the data have highly variable quality arising from several biological and technical factors. To surmount this problem, we developed a sequence-based machine learning method to evaluate and refine chromatin accessibility data. Our framework, gapped k-mer SVM quality check (gkmQC), provides the quality metrics for a sample based on the prediction accuracy of the trained models. We tested 886 DNase-seq samples from the ENCODE/Roadmap projects to demonstrate that gkmQC can effectively identify “high-quality” (HQ) samples with low conventional quality scores owing to marginal read depths. Peaks identified in HQ samples are more accurately aligned at functional regulatory elements, show greater enrichment of regulatory elements harboring functional variants, and explain greater heritability of phenotypes from their relevant tissues. Moreover, gkmQC can optimize the peak-calling threshold to identify additional peaks, especially for rare cell types in single-cell chromatin accessibility data.

Original languageEnglish
Article numbere2212810119
JournalProceedings of the National Academy of Sciences of the United States of America
Issue number51
StatePublished - Dec 20 2022


  • chromatin accessibility
  • gkmQC
  • quality control
  • sequence-based model


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