CleanUpRNAseq: An R/Bioconductor Package for Detecting and Correcting DNA Contamination in RNA-Seq Data.

IF 2.7 Q3 BIOTECHNOLOGY & APPLIED MICROBIOLOGY
BioTech Pub Date : 2024-08-03 DOI:10.3390/biotech13030030
Haibo Liu, Kai Hu, Kevin O'Connor, Michelle A Kelliher, Lihua Julie Zhu
{"title":"CleanUpRNAseq: An R/Bioconductor Package for Detecting and Correcting DNA Contamination in RNA-Seq Data.","authors":"Haibo Liu, Kai Hu, Kevin O'Connor, Michelle A Kelliher, Lihua Julie Zhu","doi":"10.3390/biotech13030030","DOIUrl":null,"url":null,"abstract":"<p><p>RNA sequencing (RNA-seq) has become a standard method for profiling gene expression, yet genomic DNA (gDNA) contamination carried over to the sequencing library poses a significant challenge to data integrity. Detecting and correcting this contamination is vital for accurate downstream analyses. Particularly, when RNA samples are scarce and invaluable, it becomes essential not only to identify but also to correct gDNA contamination to maximize the data's utility. However, existing tools capable of correcting gDNA contamination are limited and lack thorough evaluation. To fill the gap, we developed CleanUpRNAseq, which offers a comprehensive set of functionalities for identifying and correcting gDNA-contaminated RNA-seq data. Our package offers three correction methods for unstranded RNA-seq data and a dedicated approach for stranded data. Through rigorous validation on published RNA-seq datasets with known levels of gDNA contamination and real-world RNA-seq data, we demonstrate CleanUpRNAseq's efficacy in detecting and correcting detrimental levels of gDNA contamination across diverse library protocols. CleanUpRNAseq thus serves as a valuable tool for post-alignment quality assessment of RNA-seq data and should be integrated into routine workflows for RNA-seq data analysis. Its incorporation into OneStopRNAseq should significantly bolster the accuracy of gene expression quantification and differential expression analysis of RNA-seq data.</p>","PeriodicalId":34490,"journal":{"name":"BioTech","volume":null,"pages":null},"PeriodicalIF":2.7000,"publicationDate":"2024-08-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11348166/pdf/","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"BioTech","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.3390/biotech13030030","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q3","JCRName":"BIOTECHNOLOGY & APPLIED MICROBIOLOGY","Score":null,"Total":0}
引用次数: 0

Abstract

RNA sequencing (RNA-seq) has become a standard method for profiling gene expression, yet genomic DNA (gDNA) contamination carried over to the sequencing library poses a significant challenge to data integrity. Detecting and correcting this contamination is vital for accurate downstream analyses. Particularly, when RNA samples are scarce and invaluable, it becomes essential not only to identify but also to correct gDNA contamination to maximize the data's utility. However, existing tools capable of correcting gDNA contamination are limited and lack thorough evaluation. To fill the gap, we developed CleanUpRNAseq, which offers a comprehensive set of functionalities for identifying and correcting gDNA-contaminated RNA-seq data. Our package offers three correction methods for unstranded RNA-seq data and a dedicated approach for stranded data. Through rigorous validation on published RNA-seq datasets with known levels of gDNA contamination and real-world RNA-seq data, we demonstrate CleanUpRNAseq's efficacy in detecting and correcting detrimental levels of gDNA contamination across diverse library protocols. CleanUpRNAseq thus serves as a valuable tool for post-alignment quality assessment of RNA-seq data and should be integrated into routine workflows for RNA-seq data analysis. Its incorporation into OneStopRNAseq should significantly bolster the accuracy of gene expression quantification and differential expression analysis of RNA-seq data.

CleanUpRNAseq:用于检测和校正 RNA-Seq 数据中 DNA 污染的 R/Bioconductor 软件包。
RNA 测序(RNA-seq)已成为基因表达谱分析的一种标准方法,但测序文库中的基因组 DNA(gDNA)污染对数据完整性构成了巨大挑战。检测和纠正这种污染对准确的下游分析至关重要。特别是当 RNA 样本稀缺且无价时,不仅要识别而且要校正 gDNA 污染,以最大限度地发挥数据的效用。然而,现有能够校正 gDNA 污染的工具非常有限,而且缺乏全面的评估。为了填补这一空白,我们开发了 CleanUpRNAseq,它为识别和校正受 gDNA 污染的 RNA-seq 数据提供了一套全面的功能。我们的软件包为无链 RNA-seq 数据提供了三种校正方法,为有链数据提供了一种专用方法。通过对已知 gDNA 污染水平的已发表 RNA-seq 数据集和实际 RNA-seq 数据的严格验证,我们证明了 CleanUpRNAseq 在检测和校正不同文库协议中有害的 gDNA 污染水平方面的功效。因此,CleanUpRNAseq 是对 RNA-seq 数据进行配准后质量评估的重要工具,应纳入 RNA-seq 数据分析的常规工作流程。将 CleanUpRNAseq 纳入 OneStopRNAseq 将大大提高基因表达量化和 RNA-seq 数据差异表达分析的准确性。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
求助全文
约1分钟内获得全文 求助全文
来源期刊
BioTech
BioTech Immunology and Microbiology-Applied Microbiology and Biotechnology
CiteScore
3.70
自引率
0.00%
发文量
51
审稿时长
11 weeks
×
引用
GB/T 7714-2015
复制
MLA
复制
APA
复制
导出至
BibTeX EndNote RefMan NoteFirst NoteExpress
×
提示
您的信息不完整,为了账户安全,请先补充。
现在去补充
×
提示
您因"违规操作"
具体请查看互助需知
我知道了
×
提示
确定
请完成安全验证×
copy
已复制链接
快去分享给好友吧!
我知道了
右上角分享
点击右上角分享
0
联系我们:info@booksci.cn Book学术提供免费学术资源搜索服务,方便国内外学者检索中英文文献。致力于提供最便捷和优质的服务体验。 Copyright © 2023 布克学术 All rights reserved.
京ICP备2023020795号-1
ghs 京公网安备 11010802042870号
Book学术文献互助
Book学术文献互助群
群 号:481959085
Book学术官方微信