【原】综述：ATAC-Seq 数据分析工具大全

技能树今年的新专辑《ATAC-Seq 数据分析2025》会介绍各种关于 ATAC-Seq 数据分析的小知识点，欢迎关注~

当然，我们前面也给大家分享过相关的内容：

• ATAC-Seq入门加高阶传送门 （点击进入交流群）

• 第1篇：ATAC-seq的背景介绍以及与ChIP-Seq的异同

• 第2篇：原始数据的质控、比对和过滤

• 第3篇：用MACS2软件call peaks

• 第4篇：对ATAC-Seq/ChIP-seq的质量评估（一）——phantompeakqualtools

• 第5篇：对ATAC-Seq/ChIP-seq的质量评估（二）——ChIPQC

• 第6篇：重复样本的处理——IDR

• 第7篇：用Y叔的ChIPseeker对peaks进行注释和可视化

• 第8篇：用网页版工具做功能分析和motif分析

• 第9篇：差异peaks分析——DiffBind

• 第10篇：ATAC-Seq、ChIP-Seq、RNA-Seq整合分析（本系列完结，内附目录）

今年会在以往的基础上进行迭代与更新，并进行扩展，添加新的内容如scATAC-Seq，欢迎关注新专辑《ATAC-Seq 数据分析2025》~

今天给大家分享的这篇文献综合性地解释了 ATAC-seq 数据处理的基本原理，总结了常见的分析方法，并回顾了计算工具，为不同的研究问题提供建议。这篇文章为 ATAC-seq 数据的分析提供了一个起点和参考。

标题：Analytical Approaches for ATAC-seq Data Analysis

发表：Curr Protoc Hum Genet. 2020 Jun;106(1):e101. doi: 10.1002/cphg.101

链接：https://currentprotocols.onlinelibrary./doi/abs/10.1002/cphg.101

ATAC-seq 全称

ATAC-seq 全称为：the Assay for Transpose Accessible Chromatin using sequencing，翻译为转座酶可及染色质测序分析法。

研究目的有：

• 定位核小体
• 识别转录因子结合位点
• 识别对外部因子可及的DNA区域，包括启动子、增强子和其他类型的元件
• 测量DNA调控元件的差异性活性

ATAC-seq的研究数量在短短几年内就接近1万项

ATAC-seq 实验原理图

ATAC-seq 依赖于一种活跃的Tn5转座酶的活性，Tn5转座酶简介如下：

Tn5转座酶是一种广泛应用于基因组学研究的工具酶，以下是关于Tn5转座酶的详细介绍：

1. 来源与特性

Tn5转座酶来源于大肠杆菌（E. coli），是一种经过改造的突变体，具有极高的活性。它能够特异性识别转座子两端的反向重复序列（如嵌合端Mosaic End, ME），并随机将转座子插入目标DNA序列中。这种转座酶在原核和真核生物的DNA中都表现出高效的插入能力。

2. 作用机制

Tn5转座酶通过形成转座复合体，催化四个磷酸转移反应（包括DNA切割、发夹形成、发夹分解和链转移到目标DNA），从而将转座子整合到新的DNA位点。其插入位点具有一定的随机性，但也有偏好性，首选的DNA靶序列是A-GNT（T/C）（A/T）（A/G）ANC-T。

3. 应用领域

基因组学研究

Tn5转座酶被广泛应用于基因组学研究，尤其是在ATAC-seq（染色质开放性测序）中。它可以识别染色质上的开放区域，剪切DNA片段，并在剪切的同时插入特定序列，从而用于分析基因组的开放性区域。

高通量测序文库构建

Tn5转座酶能够高效地将DNA片段打断并连接接头序列，因此被广泛用于二代测序文库的构建。它能够在单个反应中完成片段化和接头连接，大大简化了文库构建的步骤。

转基因技术

Tn5转座酶可以将外源基因插入宿主细胞基因组中，用于构建转基因细胞系或模型生物。其插入的随机性和高效性使其成为一种理想的基因插入工具。

4. 优势

• 高效性：Tn5转座酶具有极高的活性，能够在短时间内完成DNA片段的插入。
• 随机性：其插入位点具有较高的随机性，适用于需要广泛插入的应用场景。
• 多功能性：除了基因插入，Tn5转座酶还被用于基因组片段化和接头连接，广泛应用于高通量测序。

5. 使用注意事项

• 保存条件：Tn5转座酶通常需要在-80℃保存，解冻后可在-20℃保存2个月。
• 反应体系：在使用Tn5转座酶进行高通量测序文库构建时，需要根据具体应用优化反应体系和条件。

Tn5转座酶因其高效性和多功能性，已成为基因组学研究和高通量测序中不可或缺的工具。

Generalized ATAC-seq library preparation protocol

数据分析流程

• 1、比对, 去接头, 和去除线粒体 reads

• 2、reads 去重复

• 3、生成信号轨迹图

• 4、Peak Calling

• 5、下游分析

ATAC-seq general workflow

非常详细的 ATAC-seq 数据分析指导资源

Title and	author	Notes	link
ATAC-seq data analysis: from FASTQ to peaks	Yiwei Niu，Last updated: 2019	Blog style walkthrough of generalized ATAC-seq data analysis.	https://yiweiniu./blog/2019/03/ATAC-seq-data-analysis-from-FASTQ-to-peaks/
BIOINF525 Lab 3.2	Steve Parker，Last updated: 2016	Minimal standard ATAC-seq analysis walkthrough.	https://github.com/ParkerLab/
Analysis of ATAC-seq data in R and Bioconductor	Rockefeller Bioinformatics Resource， Last updated: 2018	Bioconductor ATAC-seq analysis course.	https://rockefelleruniversity./RU_ATACseq/
ATAC-seq	John M. Gaspar，Last updated: 2019	Generalized ATAC-seq analysis walkthrough with included custom scripts.	https://github.com/harvardinformatics/ATAC-seq
ATAC-seq data analysis	Delisle L; Doyle M; & Heyl F，Last updated: 2020	Galaxy training walkthrough of generalized ATAC-seq analysis.	https://galaxyproject./training-material/topics/epigenetics/tutorials/atac-seq/tutorial.html

ATAC-seq 原始数据处理 Pipelines

软件名	Language	Notes	Docs	Citation
AIAP	Bash; R; Python	Optimized analysis with novel QC metrics	++	Liu et al. (2019) Last updated: 2019
ATAC2GRN	Bash; Python	Parameter optimized ATAC-seq pipeline	+	Pranzatelli, Michael, & Chiorini (2018) Last updated: 2018
ATAC-pipe	Python; R	Analysis pipeline for ATAC-seq data including TF footprinting; cell-type classification; and regulatory network creation	+++	Zuo et al. (2019) Last updated: 2019
ATACProc	Bash; Python; R	Complete pipeline with additional downstream analyses included	++	Unpublished Last updated: 2019
Basepair	NA	Commercial. Web-based GUI for complete analysis	?	Unpublished
CIPHER	R; Perl; Python	A data processing platform for ChIP-seq; RNA-seq; MNase-seq; DNase-seq; ATAC-seq; and GRO-seq datasets	+	Guzman & D’Orso (2017) Last updated: 2017
ENCODE	Python; Bash	Complete pipeline following ENCODE standards for ATAC/DNase-seq analysis	++	Unpublished Last updated: 2020
esATAC	R	Complete pipeline including downstream analyses	+++	Wei, Zhang, Fang, Li, & Wang (2018) Last updated: 2019
GUAVA	Java; Python; R	GUI based complete ATAC-seq pipeline	+	Divate & Cheung (2018) Last updated: 2019
I-ATAC	Java	GUI based interactive ATAC-seq pipeline	+	Ahmed & Ucar (2017) Last updated: 2017
nfcore/atacseq	Python; R	Complete pipeline build using Nextflow	+++	Ewels et al. (2019) Last updated: 2019
PEPATAC	Python; R; Perl	Complete pipeline with unique analytical approaches and QC metrics	+++	Unpublished Last updated: 2019
pyflow-ATACseq	Bash; Python	ATAC-seq snakemake pipeline with included nucleosome positioning and TF footprinting	++	Unpublished Last updated: 2019
snakePipes ATAC-seq	Python	Workflow system including ATAC-seq analysis	+++	Bhardwaj et al. (2019) Last updated: 2019
Tobias Rausch	Bash; R; Python	Complete pipeline with emphasis on downstream analyses	++	Rausch et al. (2019) Last updated: 2020

ATAC-seq 数据质控工具

Languages	Notes	Docs	Citation
ATAqC	Bash; Python	Generate ATAC-seq specific quality control metrics.	+	Unpublished Last updated: 2017
ATACseqQC	R	Provides ATAC-seq specific quality control metrics and transcription factor footprinting.	+++	Ou et al. (2018) Last updated: 2018
ataqv	C++; Bash	ATAC-seq QC and visualization.	+++	Orchard, Kyono, Hensley, Kitzman, & Parker (2020) Last updated: 2020

Peak Calling 工具

软件名	Languages	Notes	Docs	Citation
F-Seq	Java	Can be used as general peak caller to identify regions of open chromatin.	++	Boyle et al. (2008) Last updated: 2016
Genrich	C	Peak caller for genomic enrichment assays with specific ATAC-seq mode.	+++	unpublished Last updated: 2020
HMMRATAC	Java	Identify nucleosome positioning and leverage ATAC-seq specific read outs to call peaks.	+++	Tarbell & Liu (2019) Last updated: 2020
Hotspot2	C++	Identify significantly enriched genomic regions.	++	Unpublished Last updated: 2019
HOMER	Perl; C++	Suite of tools that include the ability to call peaks from DNA enrichment assays.	+++	Heinz et al. (2010) Last updated: 2010
MACS2	Python	Specifically designed for CHiP-seq but broadly applicable to any DNA enrichment assay to call peaks.	+++	Zhang et al. (2020) Last updated: 2020
PeaKDEck	Perl	Peak calling program for DNase-seq data.	+++	McCarthy & O’Callaghan (2014) Last updated: 2014

差异可及区域分析工具

软件	Languages	Notes	Docs	Citation
DAStk	Python	Identifies changes in transcription factor activity by looking at changes in chromatin accessibility	+++	Tripodi et al. (2018) Last updated: 2020
diffTF	Python; R	Identifies differential transcription factors. Can operate in basic mode with just chromatin accessibility or in classification mode where it integrates RNA-seq.	+++	Berest et al. (2019) Last updated: 2020

Motif 富集 和转录因子 Footprinting 工具

Languages	Notes	Docs	Citation
BiFET	R	Identify overrepresented transcription factor footprints.	++	Youn et al. (2019) Last updated: 2019
BinDNase	R	Transcription factor binding prediction using DNase-seq.	+	Kähärä & Lähdesmäki (2015) Last updated: 2015
CENTIPEDE	R	Transcription factor footprinting and binding site prediction.	++	Pique-Regi et al. (2011) Last updated: 2010
DeFCoM	Python	Detecting transcription factor footprints and underlying motifs using supervised learning.	+++	Quach & Furey (2017) Last updated: 2017
DNase2TF	R	Identify footprint candidates from DNase-seq data on user-specified regions.	+	Sung et al. (2014) Last updated: 2017
HINT-ATAC	Python	Use open chromatin data to identify transcription factor footprints with modifications specific to ATAC-seq data.	+++	Li et al. (2019) Last updated: 2019
HOMER	Perl; C++	A suite of tools for motif discovery and enrichment.	+++	Heinz et al. (2010) Last updated: 2019
MEME Suite	Perl; Python	Suite of tools for motif discovery; enrichment; and GO term analyses.	+++	Bailey et al. (2009) Last updated: 2020
PIQ	Bash; R	Models genome-wide DNase profiles to identify transcription factor binding sites.	++	Sherwood et al. (2014) Last updated: 2016
TOBIAS	Python	Identify transcription factor footprints.	++	Bentsen et al. (2019) Last updated: 2020
TRACE	Python	Transcription factor footprinting.	++	Ouyang & Boyle (2019) Last updated: 2020
Wellington	Python	Identify TF footprints using DNase-seq data.	+++	Piper et al. (2013) Last updated: 2019

核小体定位分析工具

软件	Languages	Notes	Docs	Citation
HMMRATAC	Java	Identify nucleosome positioning and leverage ATAC-seq specific read outs to call peaks.	+++	Tarbell & Liu (2019) Last updated: 2020
NucleoATAC	Python; R	Call nucleosomes using ATAC-seq data.	+++	Schep et al. (2015) Last updated: 2019
NucTools	Perl; R	Calculate nucleosome occupancy profiles on chromatin accessibility data.	+++	Vainshtein et al. (2017) Last updated: 2019

区域富集分析工具

软件	Languages	Notes	Docs	Citation
Annotatr	R	Annotate summarize and visualize genomic regions.	+++	Cavalcante & Sartor (2017) Last updated: 2019
BART/BARTweb	Python	Predict factors that bind at cis-regulatory regions.	+++	Wang et al. (2018) Last updated: 2020
chipenrich	R	Perform gene set enrichment testing using genomic regions.	+++	Welch et al. (2014) Last updated: 2020
coloc-stats	Python	Perform co-localization analysis of genomic regions.	+++	Simovski et al. (2018) Last updated: 2019
COLO	JSP	Identify genomic features in close proximity to user-submitted genomic regions.	++	Kim et al. (2015) Last updated: 2015
FEATnotator	Perl; R	Annotate genomic regions.	++	Podicheti & Mockaitis (2015) Last updated: 2018
GenomeRunner	.NET	Perform annotation and enrichment of genomic regions against default or custom regulatory regions.	++	Dozmorov et al. (2016) Last updated: 2016
GenometriCorr	R	Determine spatial correlation between region sets.	++	Favorov et al. (2012) Last updated: 2020
Genomic Association Tester	Python	Calculate the significance of overlaps between multiple genomic region sets.	+++	Heger et al. (2013) Last updated: 2019
GIGGLE	C	Genomics search engine to uncover significantly shared genomic loci (regions) between data.	+++	Layer et al. (2018) Last updated: 2019
GLANET	Java; Perl	Genomic loci annotation and enrichment tool between sets of genomic regions.	+++	Otlu et al. (2017) Last updated: 2019
GREAT	C	Annotate genomic regions.	+++	McLean et al. (2010) Last updated: 2019
LOLA/LOLAweb	R	Determine significant enrichment between region sets to inform on biological meaning.	+++	Sheffield & Bock (2016) Last updated: 2019
regioneR	R	Evaluate significant associations between region sets using permutation testing.	+++	Gel et al. (2016) Last updated: 2020
StereoGene	C++; R	Estimate genome-wide correlation between pairs of genomic features.	++	Stavrovskaya et al. (2017) Last updated: 2019

单细胞 scATAC-seq 数据处理工具

软件	Languages	Notes	Docs	Citation
BAP	R; Python	Bead-based scATAC-seq data processing.	++	Lareau et al. (2019) Last updated: 2019
BROCKMAN	R; Bash; Ruby	Convert genomics data into K-mer words associated with chromatin marks used to compare and identify changes across samples.	++	de Boer & Regev (2018) Last updated: 2018
Cell Ranger ATAC	NA	Commercial. Set of analysis pipelines for Chromium single cell ATAC-seq.	+++	Unpublished
chromVAR	R	Identify transcription factor accessibility in single-cell data. Enables clustering of single-cell ATAC-seq data.	+++	Schep et al. (2017) Last updated: 2019
Cicero	R	Predict cis-regulatory DNA interactions using single-cell chromatin accessibility data.	+++	Pliner et al. (2018) Last updated: 2019
cisTopic	R	Identify cell states and cis-regulatory topics from single-cell data.	+++	Bravo González-Blas et al.(2019) Last updated: 2019
scABC	R	Classify single-cell ATAC using unsupervised clustering and identify chromatin regions specific to cell identity.	+	Zamanighomi et al. (2018) Last updated: 2019
SCALE	Python	Clustering and visualization of single-cell ATAC-seq data into interpretable cell populations.	++	Xiong et al. (2019) Last updated: 2019
Scasat	Bash; Python; R	Complete pipeline to process scATAC-seq data with simple steps.	+++	Baker et al. (2019) Last updated: 2019
scATAC-pro	R; Python	Comprehensive pipeline for single cell ATAC-seq analysis.	+++	Yu et al. (2019) Last updated: 2020
scOpen	Python	Chromatin-accessibility estimation of single-cell ATAC data.	+	Li et al. (2019) Last updated: 2020
SCRAT	R	Useful for studying single cell heterogeneity. Can identify changes in gene sets or transcription factor binding sites. Includes GUI and web-based service.	+++	Ji et al. (2017) Last updated: 2018
SnapATAC	R; Python	Single Nucleus Analysis Pipeline for ATAC-seq.	+++	Fang et al. (2019) Last updated: 2019

此外：作者维护了一个不断扩大的 ATAC-seq 工具列表，可前往关注：

https://github.com/databio/awesome-atac-analysis