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Article

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

by
Haibo Liu
1,
Kai Hu
1,
Kevin O’Connor
1,
Michelle A. Kelliher
1 and
Lihua Julie Zhu
1,2,3,*
1
Department of Molecular, Cell and Cancer Biology, University of Massachusetts Chan Medical School, 364 Plantation Street, Worcester, MA 01605, USA
2
Department of Molecular Medicine, University of Massachusetts Chan Medical School, 364 Plantation Street, Worcester, MA 01605, USA
3
Department of Genomics and Computational Biology, University of Massachusetts Chan Medical School, 364 Plantation Street, Worcester, MA 01605, USA
*
Author to whom correspondence should be addressed.
BioTech 2024, 13(3), 30; https://doi.org/10.3390/biotech13030030
Submission received: 5 June 2024 / Revised: 1 July 2024 / Accepted: 14 July 2024 / Published: 3 August 2024
(This article belongs to the Section Computational Biology)

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.
Keywords: RNA-seq; DNA contamination; R package; bioinformatics; GC bias; gene expression RNA-seq; DNA contamination; R package; bioinformatics; GC bias; gene expression

Share and Cite

MDPI and ACS Style

Liu, H.; Hu, K.; O’Connor, K.; Kelliher, M.A.; Zhu, L.J. CleanUpRNAseq: An R/Bioconductor Package for Detecting and Correcting DNA Contamination in RNA-Seq Data. BioTech 2024, 13, 30. https://doi.org/10.3390/biotech13030030

AMA Style

Liu H, Hu K, O’Connor K, Kelliher MA, Zhu LJ. CleanUpRNAseq: An R/Bioconductor Package for Detecting and Correcting DNA Contamination in RNA-Seq Data. BioTech. 2024; 13(3):30. https://doi.org/10.3390/biotech13030030

Chicago/Turabian Style

Liu, Haibo, Kai Hu, Kevin O’Connor, Michelle A. Kelliher, and Lihua Julie Zhu. 2024. "CleanUpRNAseq: An R/Bioconductor Package for Detecting and Correcting DNA Contamination in RNA-Seq Data" BioTech 13, no. 3: 30. https://doi.org/10.3390/biotech13030030

APA Style

Liu, H., Hu, K., O’Connor, K., Kelliher, M. A., & Zhu, L. J. (2024). CleanUpRNAseq: An R/Bioconductor Package for Detecting and Correcting DNA Contamination in RNA-Seq Data. BioTech, 13(3), 30. https://doi.org/10.3390/biotech13030030

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