{"title":"Capturing cell-type-specific activities of cis-regulatory elements from peak-based single-cell ATAC-seq.","authors":"Mengjie Chen","doi":"10.1016/j.xgen.2025.100806","DOIUrl":null,"url":null,"abstract":"<p><p>Single-cell ATAC sequencing (scATAC-seq), a state-of-the-art genomic technique designed to map chromatin accessibility at the single-cell level, presents unique analytical challenges due to limited sampling and data sparsity. In this study, we use case studies to highlight the limitations of conventional peak-based methods for processing scATAC-seq data. These methods can fail to capture precise cell-type-specific regulatory signals, producing results that are difficult to interpret and lack portability, thereby compromising the reproducibility of research findings. To overcome these issues, we introduce CREscendo, a method that utilizes Tn5 cleavage frequencies and regulatory annotations to identify differential usage of candidate regulatory elements (CREs) across cell types. Our research advocates for moving away from traditional peak-based quantification in scATAC-seq toward a more robust framework that relies on a standardized reference of annotated CREs, enhancing both the accuracy and reproducibility of genomic studies.</p>","PeriodicalId":72539,"journal":{"name":"Cell genomics","volume":" ","pages":"100806"},"PeriodicalIF":11.1000,"publicationDate":"2025-02-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"Cell genomics","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1016/j.xgen.2025.100806","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q1","JCRName":"CELL BIOLOGY","Score":null,"Total":0}
引用次数: 0
Abstract
Single-cell ATAC sequencing (scATAC-seq), a state-of-the-art genomic technique designed to map chromatin accessibility at the single-cell level, presents unique analytical challenges due to limited sampling and data sparsity. In this study, we use case studies to highlight the limitations of conventional peak-based methods for processing scATAC-seq data. These methods can fail to capture precise cell-type-specific regulatory signals, producing results that are difficult to interpret and lack portability, thereby compromising the reproducibility of research findings. To overcome these issues, we introduce CREscendo, a method that utilizes Tn5 cleavage frequencies and regulatory annotations to identify differential usage of candidate regulatory elements (CREs) across cell types. Our research advocates for moving away from traditional peak-based quantification in scATAC-seq toward a more robust framework that relies on a standardized reference of annotated CREs, enhancing both the accuracy and reproducibility of genomic studies.