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Updated: May 10, 2026

Identification of Alternative Splicing and Polyadenylation in RNA-seq Data
Published on: June 24, 2021
DeepSAGE reveals genetic variants associated with alternative polyadenylation and expression of coding and non-coding
Daria V Zhernakova1, Eleonora de Klerk, Harm-Jan Westra
1University of Groningen, University Medical Center Groningen, Department of Genetics, Groningen, The Netherlands.
DeepSAGE analysis of gene expression reveals novel genetic effects on traits. This method identifies expression quantitative trait loci (eQTLs) and alternative polyadenylation, offering new insights into disease variants.
Area of Science:
- Genomics
- Molecular Biology
- Bioinformatics
Background:
- Genetic variants influence gene expression, impacting traits and diseases.
- Expression quantitative trait loci (eQTLs) are key to understanding these effects.
- Next-generation sequencing (NGS) technologies facilitate eQTL detection.
Purpose of the Study:
- To investigate how genetic variants affect gene expression and 3'-untranslated region (3'-UTR) lengths using DeepSAGE.
- To identify novel cis-eQTLs for Genome-Wide Association Study (GWAS) hits.
- To explore the impact of single nucleotide polymorphisms (SNPs) on alternative polyadenylation.
Main Methods:
- Analysis of 94 whole blood samples using DeepSAGE technology.
- Meta-analysis combining DeepSAGE and RNA-sequencing datasets.
- Identification and confirmation of SNPs affecting alternative polyadenylation sites.
Main Results:
- Discovery of previously unknown cis-eQTL effects for disease-associated GWAS hits.
- Detection of cis-eQTLs for non-coding and antisense transcripts, including those near retrotransposons.
- Identification of SNPs influencing messenger RNA (mRNA) stability via alternative polyadenylation.
Conclusions:
- DeepSAGE is effective for eQTL mapping of known and novel transcripts.
- DeepSAGE complements RNA-sequencing for a more comprehensive understanding of genetic variant effects.
- The integrative approach provides deeper insights into the molecular consequences of disease-associated variants.
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