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Cited by 3Genomics and Chromatin DynamicsEpigenetics and DNA MethylationChromatin Remodeling and Cancer

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PATTY corrects open-chromatin bias for improved bulk and single-cell CUT&Tag profiling

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Hu, Shengen Shawn, Su, Zhangli, Liu, Lin, Chen, Qingying, Grieco, Megan C., Tian, Mengxue, Dutta, Anindya, Zang, Chongzhi·2026·Nature Communications
DOI 10.1038/s41467-026-73599-8PMC PMC12424971

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Abstract

Precise profiling of epigenomes is essential for better understanding chromatin biology and gene regulation. Cleavage Under Targets & Tagmentation (CUT&Tag) is an efficient epigenomic profiling technique that can be performed on a low number of cells and at the single-cell level. With its growing adoption, CUT&Tag datasets spanning diverse biological systems are rapidly accumulating in the field. CUT&Tag assays use the hyperactive transposase Tn5 for DNA tagmentation. Tn5's preference toward accessible chromatin alters CUT&Tag sequence read distributions in the genome and introduces open-chromatin bias that can confound downstream analysis, an issue more substantial in sparse single-cell data. We show that open-chromatin bias extensively exists in published CUT&Tag datasets, including those generated with recently optimized high-salt protocols. To address this challenge, we present PATTY (Propensity Analyzer for Tn5 Transposase Yielded bias), a comprehensive computational method that corrects open-chromatin bias in CUT&Tag data by leveraging accompanying ATAC-seq. By integrating transcriptomic and epigenomic data using machine learning and integrative modeling, we demonstrate that PATTY enables accurate and robust detection of occupancy sites for both active and repressive histone modifications, including H3K27ac, H3K27me3, and H3K9me3, with experimental validation. We further develop a single-cell CUT&Tag analysis framework built on PATTY and show improved cell clustering when using bias-corrected single-cell CUT&Tag data compared to using uncorrected data. Beyond CUT&Tag, PATTY sets a foundation for further development of bias correction methods for improving data analysis for all Tn5-based high-throughput assays.

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Metadata

Authors
Hu, S. S., Su, Z., Liu, L., Chen, Q., Grieco, M. C., Tian, M., Dutta, A., & Zang, C.
Affiliations
  • University of Virginia Cancer Center (US)
  • University of Alabama at Birmingham (US)
  • Shanghai Jiao Tong University (CN)
  • University of Virginia (US)
Corresponding author
Zang, Chongzhi
ORCID
Venue
Nature Communications
Publisher
Springer Science and Business Media LLC
Published
2026-05-22
Volume
17
Issue
1
ISSN
2041-1723 (ISSN-L)
DOI
10.1038/s41467-026-73599-8
PMC
PMC12424971
OpenAlex
W4414043796
Cited by
3
References
6
Citations by year
2026: 2
FWCI
4.13
Type
journal article
Language
en
Open access
Licence
Locations
  • Nature Communications (publishedVersion, cc-by-nc-nd)
  • bioRxiv (Cold Spring Harbor Laboratory) (acceptedVersion, cc-by-nc-nd)
  • Research Square (acceptedVersion, cc-by)
  • PubMed (publishedVersion)
  • PubMed Central (publishedVersion, cc-by-nc-nd)
  • PubMed Central (publishedVersion, cc-by)
  • Europe PMC (PubMed Central) (submittedVersion, cc-by)
  • DOAJ (DOAJ: Directory of Open Access Journals) (submittedVersion)
Funders
  • U.S. Department of Health & Human Services | NIH | National Institute of General Medical Sciences (R35GM133712)
  • U.S. Department of Health & Human Services | NIH | National Human Genome Research Institute (R21HG012981)
Topics
  • Life Sciences › Biochemistry, Genetics and Molecular Biology › Molecular Biology › Genomics and Chromatin Dynamics
  • Life Sciences › Biochemistry, Genetics and Molecular Biology › Molecular Biology › Epigenetics and DNA Methylation
  • Life Sciences › Biochemistry, Genetics and Molecular Biology › Molecular Biology › Chromatin Remodeling and Cancer
Keywords
CUT&Tag, Tn5 transposase, bias correction, patties, single-cell epigenomics, ATAC-seq, histone modifications, H3K27ac, H3K27me3, H3K9me3, chromatin accessibility, cell clustering, epigenetic profiling

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