Variant Detection in Brassica Napus

The advent of Next Generation Sequencing (NGS) made molecular markers, such as Single Nucleotide Polymorphisms (SNPs) more commonly used. However, detection of SNPs in polyploid genomes may be challenging i.e. due to higher volumes of repetitive DNA. This project aims to create a computational pipeline for variant detection in the polyploid genome of Brassica Napus. A total of 304 Single Nucleotide Polymorphisms (SNPs) and 16 Insertions or Deletions (INDELs) were detected across 6 genes. For sequencing, 4 SNPs and 2 INDELs were selected. Despite the latter variation not passing the penultimate filter, sequencing data did reveal a 4bp deletion at position 153 of the FAE1 gene. The presence of variation, which was computationally filtered out, indicates that too stringent filters were used. However, we demonstrated that our pipeline was able to accurately discard low-quality variants. To mitigate the effects of lenient filtering methods, we suggest further separating the pipeline into independent SNPs and INDELs pipelines to apply variation-specific filters. Moreover, we showed that FASTA clusters could be used as an effective tool to gain insights into complex genomes.