Skip to content
DraconiticResearch
Search Sources API
203,234 studies31 sourcesUpdated 17 min ago
Peer-reviewedCC BYAdvanced science (Weinheim, Baden-Wurttemberg, Germany)

XChrom: A Cross-Cell Chromatin Accessibility Prediction Model Integrating Genomic Sequence and Cellular Context.

  • Yuanyuan Miao1
  • Xuan Liang1
  • Wenwen Zhang1
  • Dongmei Han2
  • Yurun Li1
  • Zhen Wang2,3
  1. 1Shanghai Institute of Nutrition and Health, Chinese Academy of Sciences, University of Chinese Academy of Sciences, Shanghai, China.
  2. 2Zhejiang Key Laboratory of Signaling Mechanisms in Tumor Progression and Intervention, School of Life Science, Hangzhou Institute for Advanced Study, University of Chinese Academy of Sciences, Hangzhou, China.
  3. 3Yuhang Institute of Medical Science Innovation and Transformation, Hangzhou, China.
Read full textPDFDOICite

Abstract

Single-cell chromatin accessibility offers unique insights into transcriptional regulation beyond gene expression. However, paired datasets of these two modalities remain relatively scarce, and existing computational models cannot simultaneously predict chromatin accessibility for unseen genomic regions and cells. Here, we present XChrom, a deep learning framework for genome-wide cross-cell chromatin accessibility prediction that integrates genomic sequence and single-cell transcriptomics-derived cell identity into a unified model. Comprehensive evaluations show that XChrom consistently outperforms existing methods in cross-region and cross-cell predictions, and uniquely enables cross-both predictions. It also supports cross-sample prediction when batch effects are properly corrected. Cross-species analyses demonstrate XChrom's robust performance and its ability to capture evolutionarily conserved regulatory rules. Applied to peripheral blood mononuclear cells from COVID-19 patients, XChrom accurately identifies transcription factor activities in novel but related cell states and reveals pathogenesis-associated regulatory alterations. XChrom is available as an open-source Python package, facilitating single-cell transcriptional regulation research.

Related

  1. Cellular, chromatin-accessibility, and spatial contextualization of bulk transcriptional patterns linked to positive peritoneal cytology in endometrial cancer
    Jiangang Huang, Binglai Luo, Zhiqing Hu +3
    Frontiers in geneticsPeer-reviewedCC BYFull textGeneral biochemistryEurope PMC
  2. integrating single-cell transcriptomics and chromatin accessibility profiling to decipher the molecular landscape of Hepatocellular carcinoma
    Kaile Pan, Lijian Chen, Yitao Zheng
    PLOS OnePeer-reviewedCC BY 4.0Full textMolecular biology & gene regulationPLOS
  3. PATZ1 remodels the nucleosome landscape to promote chromatin accessibility in injured neurons
    Netra Krishna, Anisha S. Menon, Manojkumar Kumaran +2
    Communications BiologyPeer-reviewedCC BY-NC-ND 4.0Full textMolecular biology & gene regulationCrossref
  4. Genetic integration reveals causal effects of chromatin accessibility on nucleosome positioning across diverse muscle cells
    Xiaoou Wang, Catherine C. Robertson, Arushi Varshney +11
    Nature CommunicationsPeer-reviewedCC BY-NC-ND 4.0Molecular biology & gene regulationCrossref
  5. Protocol for profiling chromatin accessibility in human small-cell lung cancer FFPE-derived samples using Omni-ATAC-seq
    Eralda Salataj, Kenta Kawasaki, Joan Massagué +4
    STAR protocolsPeer-reviewedCC BYFull textMolecular biology & gene regulationEurope PMCPMC

Cite

BibTeXRIS
BibTeX
@article{miao2026xchrom,
  title = {{XChrom: A Cross-Cell Chromatin Accessibility Prediction Model Integrating Genomic Sequence and Cellular Context.}},
  author = {Yuanyuan Miao and Xuan Liang and Wenwen Zhang and Dongmei Han and Yurun Li and Zhen Wang},
  journal = {Advanced science (Weinheim, Baden-Wurttemberg, Germany)},
  year = {2026},
  month = oct,
  doi = {10.1002/advs.78456},
  url = {https://europepmc.org/article/MED/42949763},
  note = {CC BY},
}
RIS
TY  - JOUR
TI  - XChrom: A Cross-Cell Chromatin Accessibility Prediction Model Integrating Genomic Sequence and Cellular Context.
AU  - Yuanyuan Miao
AU  - Xuan Liang
AU  - Wenwen Zhang
AU  - Dongmei Han
AU  - Yurun Li
AU  - Zhen Wang
PY  - 2026
DA  - 2026/10/28
JO  - Advanced science (Weinheim, Baden-Wurttemberg, Germany)
DO  - 10.1002/advs.78456
UR  - https://europepmc.org/article/MED/42949763
AB  - Single-cell chromatin accessibility offers unique insights into transcriptional regulation beyond gene expression. However, paired datasets of these two modalities remain relatively scarce, and existing computational models cannot simultaneously predict chromatin accessibility for unseen genomic regions and cells. Here, we present XChrom, a deep learning framework for genome-wide cross-cell chromatin accessibility prediction that integrates genomic sequence and single-cell transcriptomics-derived cell identity into a unified model. Comprehensive evaluations show that XChrom consistently outperforms existing methods in cross-region and cross-cell predictions, and uniquely enables cross-both predictions. It also supports cross-sample prediction when batch effects are properly corrected. Cross-species analyses demonstrate XChrom's robust performance and its ability to capture evolutionarily conserved regulatory rules. Applied to peripheral blood mononuclear cells from COVID-19 patients, XChrom accurately identifies transcription factor activities in novel but related cell states and reveals pathogenesis-associated regulatory alterations. XChrom is available as an open-source Python package, facilitating single-cell transcriptional regulation research.
SN  - 2198-3844
ER  -