Browsing by Author "Stander, Allison Anne"

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    De novo assembly of the rooibos genome
    (University of Western Cape, 2020) Stander, Allison Anne; Hesse, Uljana
    Rooibos (Aspalathus linearis) is endemic to the Cederberg region of South Africa, and one of the few indigenous medicinal plants commercially cultivated in the country. International interest in rooibos is growing, and currently most of the rooibos harvest is exported overseas to more than 30 countries. Various problems hamper the growth of the rooibos industry, including insect pests, diseases, drought and a decreasing lifespan of the plants. The availability of whole-genome data for rooibos can contribute to the selection of genetically superior plants, facilitating not only the identification of important genes and metabolic pathways in rooibos, but also the establishment of breeding programs.
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    Rooibos (Aspalathus linearis) genome size estimation using flow cytometry and k-mer analyses
    (MDPI, 2020) Mgwatyu, Yamkela; Stander, Allison Anne; Ferreira, Stephan
    Plant genomes provide information on biosynthetic pathways involved in the production of industrially relevant compounds. Genome size estimates are essential for the initiation of genome projects. The genome size of rooibos (Aspalathus linearis species complex) was estimated using DAPI flow cytometry and k-mer analyses. For flow cytometry, a suitable nuclei isolation buffer, plant tissue and a transport medium for rooibos ecotype samples collected from distant locations were identified. When using radicles from commercial rooibos seedlings, Woody Plant Buffer and Vicia faba as an internal standard, the flow cytometry-estimated genome size of rooibos was 1.24 ± 0.01 Gbp. The estimates for eight wild rooibos growth types did not deviate significantly from this value. K-mer analysis was performed using Illumina paired-end sequencing data from one commercial rooibos genotype. For biocomputational estimation of the genome size, four k-mer analysis methods were investigated: A standard formula and three popular programs (BBNorm, GenomeScope, and FindGSE). GenomeScope estimates were strongly affected by parameter settings, specifically CovMax.