• 文献检索
  • 文档翻译
  • 深度研究
  • 学术资讯
  • Suppr Zotero 插件Zotero 插件
  • 邀请有礼
  • 套餐&价格
  • 历史记录
应用&插件
Suppr Zotero 插件Zotero 插件浏览器插件Mac 客户端Windows 客户端微信小程序
定价
高级版会员购买积分包购买API积分包
服务
文献检索文档翻译深度研究API 文档MCP 服务
关于我们
关于 Suppr公司介绍联系我们用户协议隐私条款
关注我们

Suppr 超能文献

核心技术专利:CN118964589B侵权必究
粤ICP备2023148730 号-1Suppr @ 2026

文献检索

告别复杂PubMed语法,用中文像聊天一样搜索,搜遍4000万医学文献。AI智能推荐,让科研检索更轻松。

立即免费搜索

文件翻译

保留排版,准确专业,支持PDF/Word/PPT等文件格式,支持 12+语言互译。

免费翻译文档

深度研究

AI帮你快速写综述,25分钟生成高质量综述,智能提取关键信息,辅助科研写作。

立即免费体验

esATAC:一个用于 ATAC-seq 数据分析的易用系统流程。

esATAC: an easy-to-use systematic pipeline for ATAC-seq data analysis.

机构信息

Ministry of Education Key Laboratory of Bioinformatics, Center for Synthetic and System Biology, BNRist, Department of Automation, Tsinghua University, Beijing, China.

出版信息

Bioinformatics. 2018 Aug 1;34(15):2664-2665. doi: 10.1093/bioinformatics/bty141.

DOI:10.1093/bioinformatics/bty141
PMID:29522192
原文链接:https://pmc.ncbi.nlm.nih.gov/articles/PMC6061683/
Abstract

SUMMARY

ATAC-seq is rapidly emerging as one of the major experimental approaches to probe chromatin accessibility genome-wide. Here, we present 'esATAC', a highly integrated easy-to-use R/Bioconductor package, for systematic ATAC-seq data analysis. It covers essential steps for full analyzing procedure, including raw data processing, quality control and downstream statistical analysis such as peak calling, enrichment analysis and transcription factor footprinting. esATAC supports one command line execution for preset pipelines and provides flexible interfaces for building customized pipelines.

AVAILABILITY AND IMPLEMENTATION

esATAC package is open source under the GPL-3.0 license. It is implemented in R and C++. Source code and binaries for Linux, MAC OS X and Windows are available through Bioconductor (https://www.bioconductor.org/packages/release/bioc/html/esATAC.html).

SUPPLEMENTARY INFORMATION

Supplementary data are available at Bioinformatics online.

摘要

摘要

ATAC-seq 技术正在迅速成为研究染色质可及性的主要实验方法之一。本文介绍了 esATAC,这是一个高度集成的、易于使用的 R/Bioconductor 包,用于系统的 ATAC-seq 数据分析。它涵盖了完整分析流程的基本步骤,包括原始数据处理、质量控制以及下游的统计分析,如峰调用、富集分析和转录因子足迹分析。esATAC 支持针对预设管道的单个命令行执行,并提供了用于构建自定义管道的灵活接口。

可用性和实现

esATAC 包是遵循 GPL-3.0 许可证的开源软件。它是用 R 和 C++编写的。适用于 Linux、MAC OS X 和 Windows 的源代码和二进制文件可通过 Bioconductor(https://www.bioconductor.org/packages/release/bioc/html/esATAC.html)获得。

补充信息

补充数据可在《生物信息学》在线获取。

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/56c1/6061683/97dfe69ff403/bty141f1.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/56c1/6061683/97dfe69ff403/bty141f1.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/56c1/6061683/97dfe69ff403/bty141f1.jpg

相似文献

1
esATAC: an easy-to-use systematic pipeline for ATAC-seq data analysis.esATAC:一个用于 ATAC-seq 数据分析的易用系统流程。
Bioinformatics. 2018 Aug 1;34(15):2664-2665. doi: 10.1093/bioinformatics/bty141.
2
QuasR: quantification and annotation of short reads in R.QuasR:R语言中短读长的定量与注释
Bioinformatics. 2015 Apr 1;31(7):1130-2. doi: 10.1093/bioinformatics/btu781. Epub 2014 Nov 21.
3
ATACseqQC: a Bioconductor package for post-alignment quality assessment of ATAC-seq data.ATACseqQC:一个用于评估 ATAC-seq 数据的基于 Bioconductor 的序列后质量评估工具包。
BMC Genomics. 2018 Mar 1;19(1):169. doi: 10.1186/s12864-018-4559-3.
4
FindIT2: an R/Bioconductor package to identify influential transcription factor and targets based on multi-omics data.FindIT2:一个基于多组学数据识别有影响力的转录因子和靶标的 R/Bioconductor 包。
BMC Genomics. 2022 Apr 7;23(Suppl 1):272. doi: 10.1186/s12864-022-08506-8.
5
GUIDEseq: a bioconductor package to analyze GUIDE-Seq datasets for CRISPR-Cas nucleases.GUIDEseq:一个用于分析CRISPR-Cas核酸酶的GUIDE-Seq数据集的Bioconductor软件包。
BMC Genomics. 2017 May 15;18(1):379. doi: 10.1186/s12864-017-3746-y.
6
RepViz: a replicate-driven R tool for visualizing genomic regions.RepViz:一种用于可视化基因组区域的复制驱动型R工具。
BMC Res Notes. 2019 Jul 19;12(1):441. doi: 10.1186/s13104-019-4473-z.
7
DaMiRseq-an R/Bioconductor package for data mining of RNA-Seq data: normalization, feature selection and classification.DaMiRseq-一个用于 RNA-Seq 数据挖掘的 R/Bioconductor 包:归一化、特征选择和分类。
Bioinformatics. 2018 Apr 15;34(8):1416-1418. doi: 10.1093/bioinformatics/btx795.
8
GenoGAM: genome-wide generalized additive models for ChIP-Seq analysis.GenoGAM:用于ChIP-Seq分析的全基因组广义相加模型
Bioinformatics. 2017 Aug 1;33(15):2258-2265. doi: 10.1093/bioinformatics/btx150.
9
BRGenomics for analyzing high-resolution genomics data in R.BRGenomics 用于在 R 中分析高分辨率基因组学数据。
Bioinformatics. 2023 Jun 1;39(6). doi: 10.1093/bioinformatics/btad331.
10
StructuralVariantAnnotation: a R/Bioconductor foundation for a caller-agnostic structural variant software ecosystem.结构变异注释:一种无调用者偏见的结构变异软件生态系统的 R / Bioconductor 基础。
Bioinformatics. 2022 Mar 28;38(7):2046-2048. doi: 10.1093/bioinformatics/btac042.

引用本文的文献

1
Efficient derivation of functional astrocytes from human induced pluripotent stem cells (hiPSCs).从人诱导多能干细胞(hiPSC)高效衍生功能性星形胶质细胞。
PLoS One. 2024 Dec 4;19(12):e0313514. doi: 10.1371/journal.pone.0313514. eCollection 2024.
2
Assessment and Evaluation of Contemporary Approaches for Astrocyte Differentiation from hiPSCs: A Modeling Paradigm for Alzheimer's Disease.人诱导多能干细胞向星形胶质细胞分化的当代方法的评估与评价:阿尔茨海默病的一种建模范式
Biol Proced Online. 2024 Sep 28;26(1):30. doi: 10.1186/s12575-024-00257-y.
3
cisDynet: An integrated platform for modeling gene-regulatory dynamics and networks.

本文引用的文献

1
AdapterRemoval v2: rapid adapter trimming, identification, and read merging.AdapterRemoval v2:快速去除接头、识别及序列合并
BMC Res Notes. 2016 Feb 12;9:88. doi: 10.1186/s13104-016-1900-2.
2
JASPAR 2016: a major expansion and update of the open-access database of transcription factor binding profiles.JASPAR 2016:转录因子结合谱开放获取数据库的重大扩展与更新
Nucleic Acids Res. 2016 Jan 4;44(D1):D110-5. doi: 10.1093/nar/gkv1176. Epub 2015 Nov 3.
3
QuasR: quantification and annotation of short reads in R.QuasR:R语言中短读长的定量与注释
顺式动态网络(cisDynet):一个用于模拟基因调控动力学和网络的集成平台。
Imeta. 2023 Nov 23;2(4):e152. doi: 10.1002/imt2.152. eCollection 2023 Nov.
4
CircSeqAlignTk: An R package for end-to-end analysis of RNA-seq data for circular genomes.CircSeqAlignTk:一个用于环状基因组 RNA-seq 数据端到端分析的 R 包。
F1000Res. 2024 Apr 30;11:1221. doi: 10.12688/f1000research.127348.1. eCollection 2022.
5
Chromatin accessibility profiling reveals that human fibroblasts respond to mechanical stimulation in a cell-specific manner.染色质可及性分析表明,人类成纤维细胞以细胞特异性方式对机械刺激作出反应。
JBMR Plus. 2024 Feb 29;8(5):ziae025. doi: 10.1093/jbmrpl/ziae025. eCollection 2024 May.
6
An in vitro neurogenetics platform for precision disease modeling in the mouse.用于在小鼠中进行精确疾病建模的体外神经遗传学平台。
Sci Adv. 2024 Apr 5;10(14):eadj9305. doi: 10.1126/sciadv.adj9305. Epub 2024 Apr 3.
7
Calcium Induces the Cleavage of NopA and Regulates the Expression of Nodulation Genes and Secretion of T3SS Effectors in NGR234.钙诱导 NopA 的切割,调节 NGR234 中结瘤基因的表达和 T3SS 效应子的分泌。
Int J Mol Sci. 2024 Mar 19;25(6):3443. doi: 10.3390/ijms25063443.
8
16S rRNA gene primer choice impacts off-target amplification in human gastrointestinal tract biopsies and microbiome profiling.16S rRNA 基因引物选择会影响人类胃肠道活检和微生物组分析中的非靶向扩增。
Sci Rep. 2023 Aug 3;13(1):12577. doi: 10.1038/s41598-023-39575-8.
9
NeuronMotif: Deciphering cis-regulatory codes by layer-wise demixing of deep neural networks.神经元基序:通过深度神经网络的逐层解混来破译顺式调控代码。
Proc Natl Acad Sci U S A. 2023 Apr 11;120(15):e2216698120. doi: 10.1073/pnas.2216698120. Epub 2023 Apr 6.
10
The 3D enhancer network of the developing T cell genome is shaped by SATB1.发育中的 T 细胞基因组的 3D 增强子网络由 SATB1 塑造。
Nat Commun. 2022 Nov 14;13(1):6954. doi: 10.1038/s41467-022-34345-y.
Bioinformatics. 2015 Apr 1;31(7):1130-2. doi: 10.1093/bioinformatics/btu781. Epub 2014 Nov 21.
4
A comparison of peak callers used for DNase-Seq data.用于DNase-Seq数据的峰检测工具比较。
PLoS One. 2014 May 8;9(5):e96303. doi: 10.1371/journal.pone.0096303. eCollection 2014.
5
Transposition of native chromatin for fast and sensitive epigenomic profiling of open chromatin, DNA-binding proteins and nucleosome position.天然染色质易位用于快速灵敏的染色质开放性、DNA 结合蛋白和核小体位置的表观基因组分析。
Nat Methods. 2013 Dec;10(12):1213-8. doi: 10.1038/nmeth.2688. Epub 2013 Oct 6.
6
Fast gapped-read alignment with Bowtie 2.快速缺口读对准与 Bowtie 2。
Nat Methods. 2012 Mar 4;9(4):357-9. doi: 10.1038/nmeth.1923.
7
F-Seq: a feature density estimator for high-throughput sequence tags.F-Seq:一种用于高通量序列标签的特征密度估计器。
Bioinformatics. 2008 Nov 1;24(21):2537-8. doi: 10.1093/bioinformatics/btn480. Epub 2008 Sep 10.