ChromFound: Towards A Universal Foundation Model for Single-Cell Chromatin Accessibiltiy Data
Yifeng Jiao, Yuchen Liu, Yu Zhang, Xin Guo, Yushuai Wu, Chen Jiang, Jiyang Li, Hongwei Zhang, Limei Han, Xin Gao, Yuan Qi, Yuan Cheng
摘要
The advent of single-cell Assay for Transposase-Accessible Chromatin using sequencing (scATAC-seq) offers an innovative perspective for deciphering regulatory mechanisms by assembling a vast repository of single-cell chromatin accessibility data. While foundation models have achieved significant success in single-cell transcriptomics, there is currently no foundation model for scATAC-seq that supports zero-shot high-quality cell identification and comprehensive multi-omics analysis simultaneously. Key challenges lie in the high dimensionality and sparsity of scATAC-seq data, as well as the lack of a standardized schema for representing open chromatin regions (OCRs). Here, we present ChromFound, a foundation model tailored for scATAC-seq. ChromFound utilizes a hybrid architecture and genome-aware tokenization to effectively capture genome-wide long contexts and regulatory signals from dynamic chromatin landscapes. Pretrained on 1.97 million cells from 30 tissues and 6 disease conditions, ChromFound demonstrates broad applicability across 6 diverse tasks. Notably, it achieves robust zero-shot performance in generating universal cell representations and exhibits excellent transferability in cell type annotation and cross-omics prediction. By uncovering enhancer-gene links undetected by existing computational methods, Chrom-Found offers a promising framework for understanding disease risk variants in the noncoding genome. The implementation of ChromFound is available via https://github.com/JohnsonKlose/ChromFound.
问问这篇 Paper
智能体会读完全文。
Lune 把这篇 Paper 索引到了最后一个公式,引用它的顶会 Paper 也一样。你提问,回答直接引用原文。
它引用的顶会 Paper6
- Efficiently Modeling Long Sequences with Structured State SpacesAlbert Gu, Karan Goel, Christopher RéICLR 2022 · 被引用 3,482 次
- Combining Recurrent, Convolutional, and Continuous-time Models with Linear State Space LayersAlbert Gu, Isys Johnson, Karan Goel, Khaled Saab 等NeurIPS 2021 · 被引用 1,280 次
- Resurrecting Recurrent Neural Networks for Long SequencesAntonio Orvieto, Samuel L. Smith, Albert Gu, Anushan Fernando 等ICML 2023 · 被引用 474 次
- CellPLM: Pre-training of Cell Language Model Beyond Single CellsHongzhi Wen, Wenzhuo Tang, Xinnan Dai, Jiayuan Ding 等ICLR 2024 · 被引用 76 次
- xTrimoGene: An Efficient and Scalable Representation Learner for Single-Cell RNA-Seq DataJing Gong, Minsheng Hao, Xingyi Cheng, Xin Zeng 等NeurIPS 2023 · 被引用 48 次
相关 Paper
- CLM-Access: A Specialized Foundation Model for High-Dimensional Single-Cell ATAC-Seq AnalysisZiqiang Liu, Bowen Li, Zhenyu Xu, Yantao Li 等AAAI 2026 · 被引用 1 次
- Cell ontology guided transcriptome foundation modelXinyu Yuan, Zhihao Zhan, Zuobai Zhang, Manqi Zhou 等NeurIPS 2024 · 被引用 23 次
- Towards Universal Gene Regulatory Network Inference: Unlocking Generalizable Regulatory Knowledge in Single-cell Foundation ModelsJiaxin Qi, Hang Li, Yan Cui, Yuhua Zheng 等ICML 2026
- Omni-DNA: A Genomic Model Supporting Sequence Understanding, Long-context, and Textual AnnotationZehui Li, Vallijah Subasri, Yifei Shen, Dongsheng Li 等NeurIPS 2025 · 被引用 6 次
- PanFoMa: A Lightweight Foundation Model and Benchmark for Pan-CancerXiaoshui Huang, Tianlin Zhu, Yifan Zuo, Xue Xia 等AAAI 2026
