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Understanding the impact of structural variations on gene expression using pangenome graphs in Brassica napus

Gözde Yildiz

Associated student, JLU

Structural variations (SVs) are large genomic polymorphisms compared with single nucleotide polymorphisms (SNPs) and smaller InDels, leading to a strong effect on transcriptional regulation and gene expression. B. napus has an allotetraploid (2N=38), and different accessions highlight genomic variation including extensive SVs and SNPs. Some SVs have been shown to affect candidate genes associated with important agronomic traits. However, large and complex variations can lead to biased variant detection and gene expression quantification thus pangenome graphs are an excellent framework for expression quantitative trait loci (eQTL) analyses, facilitating the association between SVs and gene expression. As well as these graphs reduce reference bias effects and enable accurate variant calling missing from the reference genomes. In the first part of this project, we constructed a benchmarking workflow for structural variations detection using Oxford Nanopore technologies (ONT). Currently, we are focusing on pangenome graph design using long-reads (ONT), short-reads (Illumina) and RNA-Seq-reads combination to understand the impact of SVs on gene expression levels in B. napus genomes.

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