A workflow for simplified analysis of ATAC-cap-seq data in R.
A workflow for simplified analysis of ATAC-cap-seq data in R.
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DOI：
10.1093/gigascience/giy080
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发表时间：
2018-07-01
期刊：
影响因子：
9.2
通讯作者：
MacLean D
中科院分区：
文献类型：
--
作者：
Shrestha RK;Ding P;Jones JDG;MacLean D
转座酶可及染色质 (ATAC)-cap-seq 测定是一种高通量测序方法，它将 ATAC-seq 与沉淀 DNA 片段的靶向核酸富集相结合。由于对一组数量可能较少且具有生物学依赖性的感兴趣区域进行研究，分析难度不断增加。 RNA 测序的通用统计流程可能被假设适用，但可能会在 ATAC-cap-seq 数据上给出误导性结果。需要一种工具来允许非专业用户快速轻松地汇总数据并应用明智且有效的标准化和分析。我们开发 atacR 是为了让用户能够轻松分析他们的 ATAC 富集实验。它提供了全面的汇总函数和诊断图，用于研究富集标签丰度。样本间归一化的应用变得简单。提供了基于用户定义的控制区域、整个库大小以及从数据集中的最小可变区域中选择的区域进行标准化的函数。提供了三种从富集方法中检测标签差异丰度的方法，包括 bootstrap t、贝叶斯因子和 edgeR 包中标准精确测试的包装版本。我们在不同的重复、显着性阈值和基因变化的重采样数据集上比较了每种检测方法的精度、召回率和 F 分数，发现贝叶斯因子方法具有最大的整体检测能力，尽管在变化基因数量较少的模拟中，edgeR 稍强一些。我们的软件包允许非专业用户以可重复的方式轻松有效地应用适合 ATAC-cap-seq 分析的方法。该包以纯 R 语言实现，并且与 Bioconductor 中的常见工作流程完全互操作。
Assay for Transposase-Accessible Chromatin (ATAC)-cap-seq is a high-throughput sequencing method that combines ATAC-seq with targeted nucleic acid enrichment of precipitated DNA fragments. There are increased analytical difficulties arising from working with a set of regions of interest that may be small in number and biologically dependent. Common statistical pipelines for RNA sequencing might be assumed to apply but can give misleading results on ATAC-cap-seq data. A tool is needed to allow a nonspecialist user to quickly and easily summarize data and apply sensible and effective normalization and analysis. We developed atacR to allow a user to easily analyze their ATAC enrichment experiment. It provides comprehensive summary functions and diagnostic plots for studying enriched tag abundance. Application of between-sample normalization is made straightforward. Functions for normalizing based on user-defined control regions, whole library size, and regions selected from the least variable regions in a dataset are provided. Three methods for detecting differential abundance of tags from enriched methods are provided, including bootstrap t, Bayes factor, and a wrapped version of the standard exact test in the edgeR package. We compared the precision, recall, and F-score of each detection method on resampled datasets at varying replicate, significance threshold, and genes changed and found that the Bayes factor method had the greatest overall detection power, though edgeR was slightly stronger in simulations with lower numbers of genes changed. Our package allows a nonspecialist user to easily and effectively apply methods appropriate to the analysis of ATAC-cap-seq in a reproducible manner. The package is implemented in pure R and is fully interoperable with common workflows in Bioconductor.
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影响因子：
48
作者：
Huber W;Carey VJ;Gentleman R;Anders S;Carlson M;Carvalho BS;Bravo HC;Davis S;Gatto L;Girke T;Gottardo R;Hahne F;Hansen KD;Irizarry RA;Lawrence M;Love MI;MacDonald J;Obenchain V;Oleś AK;Pagès H;Reyes A;Shannon P;Smyth GK;Tenenbaum D;Waldron L;Morgan M
通讯作者：
Morgan M
DOI：
10.1111/tpj.12307
发表时间：
2013-11
期刊：
The Plant journal : for cell and molecular biology
影响因子：
--
作者：
Jupe F;Witek K;Verweij W;Sliwka J;Pritchard L;Etherington GJ;Maclean D;Cock PJ;Leggett RM;Bryan GJ;Cardle L;Hein I;Jones JD
通讯作者：
Jones JD
影响因子：
14.9
作者：
Lun AT;Smyth GK
通讯作者：
Smyth GK
单细胞表观基因组变异性揭示了功能性癌症异质性。
影响因子：
12.3
作者：
Litzenburger UM;Buenrostro JD;Wu B;Shen Y;Sheffield NC;Kathiria A;Greenleaf WJ;Chang HY
通讯作者：
Chang HY
影响因子：
64.8
作者：
通讯作者：
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