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Updated: Jan 31, 2026

Mapping Alzheimer's Disease Variants to Their Target Genes Using Computational Analysis of Chromatin Configuration
Published on: January 9, 2020
Advances in scCUT&Tag and computational analysis for single-cell gene regulatory element mapping
Jun Wu1, Md Wahiduzzaman1, Pengfei Yin1
1School of Biomedical Sciences, Hunan University, Fubuhe Road, Yuelu District, Changsha 410082, Hunan, China.
Abstract:
Histone modifications (HMs) and transcription factors (TFs) are central to chromatin dynamics and transcriptional regulation. Conventional bulk approaches like ChIP-seq require large cell populations, limiting applicability to heterogeneous studies and tissue samples. In contrast, single-cell cleavage under targets and tagmentation (scCUT&Tag) and its variants have enabled high-resolution profiling of HMs and TFs for investigating gene regulatory mechanisms in individual cells, transformatively broadening single-cell epigenomics beyond chromatin accessibility measured by scATAC-seq. Despite rapid advances in scCUT&Tag-related methods and the accumulation of ~21 public datasets, a systematic overview of the current research status, especially the forefront of computational analysis and ensuing challenges, remains lacking. Here, we comprehensively overview current scCUT&Tag studies from a bioinformatics perspective. We catalog representative applications spanning diverse chromatin features, experimental designs, and data characteristics. We delineate a typical computational workflow from matrix generation to downstream functional annotations, emphasizing distinctions from scATAC-seq analysis, and highlighting critical analytical considerations. We extensively survey commonly used computational tools and key algorithms, compare analytical features between scCUT&Tag and scATAC-seq, and discuss major challenges in integrative analysis. This work provides a structured reference for understanding the current research landscape of scCUT&Tag and offers computational perspectives for researchers aiming to explore gene regulatory machinery at single-cell resolution.
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