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RNA-seq

RNA-seq

RNA sequencing, or RNA-Seq, is a high-throughput sequencing technology used to study the transcriptome of a cell. Transcriptomics helps to interpret the functional elements of a genome and identify the molecular constituents of an organism. Additionally, it also helps in understanding the development of an organism and the occurrence of diseases.
Before the discovery of RNA-seq, microarray-based methods and Sanger sequencing were used for transcriptome analysis. However, while...

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Related Experiment Video

Updated: Dec 23, 2025

Assessment of DNA Contamination in RNA Samples Based on Ribosomal DNA

Assessment of DNA Contamination in RNA Samples Based on Ribosomal DNA

Published on: January 22, 2018

Consistent RNA sequencing contamination in GTEx and other data sets.

Tim O Nieuwenhuis^1,2, Stephanie Y Yang², Rohan X Verma¹

¹Department of Pathology, Johns Hopkins University SOM, Baltimore, MD, 21205, USA.

Nature Communications

|April 24, 2020

Summary

Next-generation sequencing faces read contamination challenges. Highly expressed, tissue-enriched genes in Genotype-Tissue Expression (GTEx) datasets can contaminate samples, affecting downstream analyses like eQTL mapping.

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Area of Science:

Genomics
Molecular Biology
Bioinformatics

Background:

Next-generation sequencing (NGS) is crucial for biological research but susceptible to data contamination.
Understanding sources of contamination is vital for accurate genomic analysis.
The Genotype-Tissue Expression (GTEx) project provides valuable insights into human gene expression across tissues.

Purpose of the Study:

To investigate factors contributing to read contamination in RNA-sequencing datasets.
To identify specific genes and conditions associated with sample contamination.
To assess the impact of contamination on downstream analyses, such as expression quantitative trait loci (eQTL) mapping.

Main Methods:

Analysis of Genotype-Tissue Expression (GTEx) datasets and associated technical metadata.
Utilized RNA-sequencing datasets from various studies to identify contamination patterns.
Examined variant co-expression clusters of highly expressed, tissue-enriched genes.
Validated contamination using discrepant single nucleotide polymorphisms (SNPs) in suspected contaminating genes.

Main Results:

Of 48 GTEx tissues analyzed, 26 showed contamination linked to four pancreas-enriched genes (PRSS1, PNLIP, CLPS, CELA3A).
Fourteen additional highly expressed genes in other tissues also indicated contamination.
Contamination strongly correlated with samples sequenced on the same day as tissues expressing these genes.
Low-level contamination (~40% of samples) led to incorrect eQTL assignments in multiple genes and tissues.
Contamination impacts both bulk and single-cell RNA sequencing (scRNA-seq) data.

Conclusions:

Highly expressed, tissue-enriched genes are a common source of basal contamination in GTEx and other genomic datasets.
This contamination can significantly impact the accuracy of various molecular analyses, including eQTL studies.
Awareness and mitigation strategies are necessary to ensure the integrity of NGS data.