Consensus peaks of chromatin accessibility in the human genome
Meng, Q.; Wu, X.; Li, C.; Li, J.; Xi, X.; Chen, S.; Jiang, R.; Wei, L.; Zhang, X.
Show abstract
The rapid advancement of transposase-accessible chromatin using sequencing (ATAC-seq) technology, particularly with the emergence of single-cell ATAC-seq (scATAC-seq), has accelerated the studies of gene regulation. However, the absence of a generic feature reference for ATAC-seq data limits single-cell analyses and hinders the development of comprehensive cell atlases. To address this, we constructed a generic chromatin accessibility reference by aggregating peaks from 624 high-quality bulk ATAC-seq datasets, defining more than 1 million consensus peaks (cPeaks). Leveraging a deep neural network model, we expanded cPeaks to include previously unobserved regions, enhancing their coverage across diverse tissues and cell types. cPeaks exhibit consistent shapes across tissue types, sequencing technologies, and peak-calling methods, indicating that they represent inherent genomic features. Compared to existing feature defining methods and references, cPeaks show superior performance in scATAC-seq analyses, improving cell annotation and rare cell type identification. Additionally, cPeaks provide insights into chromatin dynamics during cellular differentiation and tumor progression. cPeaks can serve as a robust reference for chromatin accessibility studies to promote cross-dataset consistency and accelerate biological discoveries.
Matching journals
The top 2 journals account for 50% of the predicted probability mass.
Similar papers in this journal
- Pathway Centric Analysis for single-cell RNA-seq and Spatial Transcriptomics Data with GSDensity 98%
- PACS allows comprehensive dissection of multiple factors governing chromatin accessibility from snATAC-seq data 98%
- Decoding the genomic landscape of chromatin-associated biomolecular condensates 97%
Similar papers in this journal
- DeTOKI identifies and characterizes the dynamics of chromatin topologically associating domains in a single cell 98%
- High-precision cell-type mapping and annotation of single-cell spatial transcriptomics with STAMapper 98%
- APEC: an accesson-based method for single-cell chromatin accessibility analysis 97%
Similar papers in this journal
- Prioritization of enhancer mutations by combining allele-specific chromatin accessibility with deep learning 96%
- PAST: latent feature extraction with a Prior-based self-Attention framework for Spatial Transcriptomics 96%
- Harnessing Agent-Based Modeling in CellAgentChat to Unravel Cell-Cell Interactions from Single-Cell Data 96%
Similar papers in this journal
"Similar papers" are the closest papers from that journal in the model's embedding space. They show what the match is built on, but the ranking comes mostly from a classifier over the whole training set, not from these examples alone.