Evidence map›Paper›PMID 39189209›Full record

ArticleBiotech (Basel (Switzerland))2024

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

Haibo Liu, Kai Hu, Kevin O'Connor, Michelle A Kelliher, Lihua Julie Zhu

Abstract read
In one paragraph

Article in Biotech (Basel (Switzerland)), 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 4 papers.

0numbers the graph read from it
0cells of the map it votes in
4citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

4 citing papers in PubMed.

  1. Article
  2. Review
  3. Article
  4. Article
4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

5 authors.

Haibo LiuDepartment of Molecular, Cell and Cancer Biology, University of Massachusetts Chan Medical School, 364 Plantation Street, Worcester, MA 01605, USA.
Kai HuDepartment of Molecular, Cell and Cancer Biology, University of Massachusetts Chan Medical School, 364 Plantation Street, Worcester, MA 01605, USA.ORCID 0000-0002-6555-2581
Kevin O'ConnorDepartment of Molecular, Cell and Cancer Biology, University of Massachusetts Chan Medical School, 364 Plantation Street, Worcester, MA 01605, USA.
Michelle A KelliherDepartment of Molecular, Cell and Cancer Biology, University of Massachusetts Chan Medical School, 364 Plantation Street, Worcester, MA 01605, USA.
Lihua Julie ZhuDepartment of Molecular, Cell and Cancer Biology, University of Massachusetts Chan Medical School, 364 Plantation Street, Worcester, MA 01605, USA.ORCID 0000-0001-7416-0590

Funding

Mechanism(s) of TAL-1/SCL-mediated LeukemogenesisR01CA096899 · NCI · UNIV OF MASSACHUSETTS MED SCH WORCESTER · PI MICHELLE ALICE KELLIHER · 2004 to 2026
$6.9M
NCI NIH HHS R01 CA096899
6 · The paper itself

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.

Indexed as

bioinformaticsDNA contaminationGC biasgene expressionRNA-seqR package

Identifiers

PMID39189209
PMCPMC11348166

What OpenQuestion holds

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LicenceCC BY
Read underepoch 390

Registered trials

None linked

Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.