ArticleNature communications2020
Consistent RNA sequencing contamination in GTEx and other data sets.
Article in Nature communications, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 36 papers, 2 of them syntheses that pooled it.
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Who cites it
36 citing papers in PubMed, 2 syntheses or guidelines pooled it.
- PancrESS - a meta-analysis resource for understanding cell-type specific expression in the human pancreas.BMC genomics · 2024Pooled it
- Pooled it
- Identification and functional assessment of CDSN gene variants associated with horn phenotypes in sheep.BMC genomics · 2026Article
- NGSTroubleFinder: a tool for detection and quantification of contamination and kinship across human NGS data.NAR genomics and bioinformatics · 2026Article
- Targeted decontamination of sequencing data with CLEAN.NAR genomics and bioinformatics · 2025Article
- ALLTogether recommendations for biobanking samples from patients with acute lymphoblastic leukaemia: a modified Delphi study.British journal of cancer · 2025Review
- Beyond the Bubble: A Debate on microRNA Sorting Into Extracellular Vesicles.Laboratory investigation; a journal of technical methods and pathology · 2025Review
- A real-world multi-center RNA-seq benchmarking study using the Quartet and MAQC reference materials.Nature communications · 2024Article
- Patterns of Unwanted Biological and Technical Expression Variation Among 49 Human Tissues.Laboratory investigation; a journal of technical methods and pathology · 2024Article
- scCDC: a computational method for gene-specific contamination detection and correction in single-cell and single-nucleus RNA-seq data.Genome biology · 2024Article
- 'Candidatus Phytoplasma mali' SAP11-Like protein modulates expression of genes involved in energy production, photosynthesis, and defense in Nicotiana occidentalis leaves.BMC plant biology · 2024Article
- T cell-mediated tumor killing sensitivity gene signature-based prognostic score for acute myeloid leukemia.Discover oncology · 2024Article
- Brooklyn plots to identify co-expression dysregulation in single cell sequencing.NAR genomics and bioinformatics · 2024Article
- Multi-omic analysis of human kidney tissue identified medulla-specific gene expression patterns.Kidney international · 2024Article
- GTax: improving de novo transcriptome assembly by removing foreign RNA contamination.Genome biology · 2024Article
- PLAE Web App Enables Powerful Searching and Multiple Visualizations Across One Million Unified Single-Cell Ocular Transcriptomes.Translational vision science & technology · 2023Article
- TargetedCell reports methods · 2023Article
- FixItFelix: improving genomic analysis by fixing reference errors.Genome biology · 2023Article
- ElasticBLAST: Accelerating Sequence Search via Cloud Computing.bioRxiv : the preprint server for biology · 2023Article
- Large-scale assessment of pros and cons of autopsy-derived or tumor-matched tissues as the norms for gene expression analysis in cancers.Computational and structural biotechnology journal · 2023Article
Corrections and comments
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Authors and funding
8 authors.
Funding
Abstract
A challenge of next generation sequencing is read contamination. We use Genotype-Tissue Expression (GTEx) datasets and technical metadata along with RNA-seq datasets from other studies to understand factors that contribute to contamination. Here we report, of 48 analyzed tissues in GTEx, 26 have variant co-expression clusters of four highly expressed and pancreas-enriched genes (PRSS1, PNLIP, CLPS, and/or CELA3A). Fourteen additional highly expressed genes from other tissues also indicate contamination. Sample contamination is strongly associated with a sample being sequenced on the same day as a tissue that natively expresses those genes. Discrepant SNPs across four contaminating genes validate the contamination. Low-level contamination affects ~40% of samples and leads to numerous eQTL assignments in inappropriate tissues among these 18 genes. This type of contamination occurs widely, impacting bulk and single cell (scRNA-seq) data set analysis. In conclusion, highly expressed, tissue-enriched genes basally contaminate GTEx and other datasets impacting analyses.
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