Department of Pharmacy and Biotechnology, University of Bologna, Bologna, Italy.
National Council of Research, CNR, Institute of Biomembranes, Bioenergetics and Molecular Biotechnologies, Bari, Italy.
BMC Bioinformatics. 2019 Aug 6;20(1):414. doi: 10.1186/s12859-019-3009-9.
R-loops are three-stranded nucleic acid structures that usually form during transcription and that may lead to gene regulation or genome instability. DRIP (DNA:RNA Immunoprecipitation)-seq techniques are widely used to map R-loops genome-wide providing insights into R-loop biology. However, annotation of DRIP-seq peaks to genes can be a tricky step, due to the lack of strand information when using the common basic DRIP technique.
Here, we introduce DRIP-seq Optimized Peak Annotator (DROPA), a new tool for gene annotation of R-loop peaks based on gene expression information. DROPA allows a full customization of annotation options, ranging from the choice of reference datasets to gene feature definitions. DROPA allows to assign R-loop peaks to the DNA template strand in gene body with a false positive rate of less than 7%. A comparison of DROPA performance with three widely used annotation tools show that it identifies less false positive annotations than the others.
DROPA is a fully customizable peak-annotation tool optimized for co-transcriptional DRIP-seq peaks, which allows a finest gene annotation based on gene expression information. Its output can easily be integrated into pipelines to perform downstream analyses, while useful and informative summary plots and statistical enrichment tests can be produced.
R 环是一种三链核酸结构,通常在转录过程中形成,可能导致基因调控或基因组不稳定。DRIP(DNA:RNA 免疫沉淀)-seq 技术广泛用于全基因组范围内绘制 R 环,为 R 环生物学提供了深入的了解。然而,由于在使用常见的基本 DRIP 技术时缺乏链信息,因此将 DRIP-seq 峰注释到基因可能是一个棘手的步骤。
在这里,我们介绍了 DRIP-seq 优化峰注释器(DROPA),这是一种基于基因表达信息注释 R 环峰的新工具。DROPA 允许对注释选项进行完全自定义,范围从参考数据集的选择到基因特征定义。DROPA 允许将 R 环峰分配到基因体中的 DNA 模板链上,假阳性率低于 7%。DROPA 与三种广泛使用的注释工具的性能比较表明,它比其他工具识别的假阳性注释更少。
DROPA 是一种完全可定制的峰注释工具,针对共转录 DRIP-seq 峰进行了优化,可根据基因表达信息进行最精细的基因注释。它的输出可以轻松集成到管道中以进行下游分析,同时可以生成有用且信息丰富的摘要图和统计富集测试。