STUDENT PROJECT What lives within and on a plant: our understanding from genome NGS data
DOI:
https://doi.org/10.24823/Sibbaldia.2026.2130Keywords:
Contaminants, Genome assembly, Pathogens, Pests , Symbionts, Biosecurity, GesneriaceaeAbstract
Next-generation sequencing (NGS) can generate gigabytes of genome data. Unlike Sanger sequencing, NGS generates a ‘read’ from a single DNA molecule, reflecting directly the starting DNA, including non-target organisms such as symbionts and pathogens. Non-target organism sequences are usually discarded during genome assembly as contaminants; these are potentially a great source of information for understanding the microbiome surrounding the plant. The present study explores bioinformatically the identification of the non-target organisms from genome NGS datasets of two cultivated Gesneriaceae species. The datasets were generated using different NGS technologies: one is from Streptocarpus rexii (Bowie ex Hook.) Lindl., sequenced using Oxford Nanopore Technologies long-read sequencing, and the second from Aeschynanthus angustifolius (Blume) Steud., sequenced using Illumina short-read sequencing. The reads were first assembled and then analysed using BlobTools to identify the contaminants. For S. rexii, Actinomycetota and Basidiomycota occupied the highest ratio among genome contaminants, followed by Arthropoda, Ascomycota and Acidobacteriota. In A. angustifolius, the highest contaminant class was Pseudomonadota and the second Actinomycetota, followed by Basidiomycota and Chordata. Arthropoda included mealybugs which were also observed in the glasshouse. The differences in contaminant composition between S. rexii and A. angustifolius may be linked to the relatively short-lived leaves of the former and the long-lived ones of the latter. This pilot study demonstrates that, in principle, this method is suitable to detect and identify associated organisms, and the pipelines designed here greatly facilitated this process. This approach might be useful in a horticultural setting for the assessment of plant material in quarantine or biosecure conditions and may be able to detect pathogens prior to plants showing symptoms. It also has potentially more widespread applications for studying plant–microbiome interactions.
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