ArticleScientific reports2019
QuantSeq. 3' Sequencing combined with Salmon provides a fast, reliable approach for high throughput RNA expression analysis.
Article in Scientific reports, 2019. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 33 papers.
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Who cites it
33 citing papers in PubMed.
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- Strong correlation of gene counts and differentially expressed genes between a 3' RNA-Seq and an RNA hybridization platform in transcriptome analyses from canine archival tissues.Frontiers in veterinary science · 2025Article
- The cacao gene atlas: a transcriptome developmental atlas reveals highly tissue-specific and dynamically-regulated gene networks in Theobroma cacao L.BMC plant biology · 2024Article
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- LZTR1 deficiency exerts high metastatic potential by enhancing sensitivity to EMT induction and controlling KLHL12-mediated collagen secretion.Cell death & disease · 2023Article
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- Longitudinal APOE4- and amyloid-dependent changes in the blood transcriptome in cognitively intact older adults.Alzheimer's research & therapy · 2023Article
- RNA-seq datasets of field rapeseedData in brief · 2022Article
- Quantitative Sequencing Analysis of the Striatal Transcriptome in a Mouse Model of Alzheimer Disease.International neurourology journal · 2022Article
- Activation of Oncogenic and Immune-Response Pathways Is Linked to Disease-Specific Survival in Merkel Cell Carcinoma.Cancers · 2022Article
- Approaches in Gene Coexpression Analysis in Eukaryotes.Biology · 2022Review
Corrections and comments
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Authors and funding
4 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
RNA-Seq is increasingly used for the diagnosis of patients, targeting of therapies and for single cell transcriptomics. These applications require cost effective, fast and reliable ways of capturing and analyzing gene expression data. Here we compared Lexogen's QuantSeq which captures only the 3' end of RNA transcripts and Illumina's TruSeq, using both Tophat2 and Salmon for gene quantification. We also compared these results to microarray. This analysis was performed on peripheral blood mononuclear cells stimulated with Poly (I:C), a viral mimic that induces innate antiviral responses. This provides a well-established model to determine if RNA-Seq and QuantSeq identify the same biological signatures. Gene expression levels in QuantSeq and RNA-Seq were strongly correlated (Spearman's rho ~0.8), Salmon and Tophat2 (Spearman's rho > 0.9). There was high consistency in protein coding genes, non-concordant genes had a high proportion of shorter, non-coding features. RNA-Seq identified more differentially expressed genes than QuantSeq, both methods outperformed microarray. The same key biological signals emerged in each of these approaches. We conclude that QuantSeq, coupled with a fast quantification method such as Salmon, should provide a viable alternative to traditional RNA-Seq in many applications and may be of particular value in the study of the 3'UTR region of mRNA.
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