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Title: Omic-enabled dectection and studies of Phytophthora capsici in the field
Author: Mcleod, Rory
ISNI:       0000 0004 9348 5843
Awarding Body: University of Dundee
Current Institution: University of Dundee
Date of Award: 2020
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Phytophthora capsici is a devastating pathogen capable of infecting important crops worldwide. Disease in the field can lead to eradication of crops and large financial losses if left untreated. Current diagnosis of the pathogen from the field is time consuming, difficult and requires highly trained specialists to handle and process samples. A more efficient diagnosis method is needed to ensure farmers can effectively maintain and manage their crops. Working with recently isolated P. capsici strains from the field is beneficial for both phytopathologists and plant breeders to identify the mechanisms used by the pathogen to cause infection and to develop resistant, commercial crops. To bridge the gap between laboratory and field knowledge, successful diagnosis and isolation of the pathogen are necessary. A genome of a single isolate of P. capsici has been sequenced and is publicly available. I have used this genome – as well as sequencing three other field isolates using three different sequencing technologies – to use a genomics approach to address the diagnostic issues that are currently faced. Here, I have employed two bioinformatic pipelines to aid the diagnosis effort to diagnose P. capsici from the field. The first (OEDs), designs diagnostic primers that are species-specific; and the second (PDP), designs primers that can discriminate within the species, resulting in isolate-specific primers. I conclude that both pipelines can design discriminatory diagnostic primers, but more sequence data and validation are required to substantiate these claims. These pipelines show great promise for diagnosing eukaryote plant pathogens found in the field.
Supervisor: Huitema, Edgar ; Pritchard, Leighton Sponsor: Not available
Qualification Name: Thesis (Ph.D.) Qualification Level: Doctoral
EThOS ID:  DOI: Not available
Keywords: Plant Pathology ; Diagnostics ; Phytophthora capsici ; Oomycete ; Genomics ; Bioinformatics