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Title: A new approach for detecting fungal and oomycete plant pathogens in next generation sequencing metagenome data utilising electronic probes

item ESPINDOLA, ANDRES - Oklahoma State University
item Schneider, William
item HOYT, PETER - Oklahoma State University
item GARZON, CARLA - Oklahoma State University
item MAREK, STEPHEN - Oklahoma State University

Submitted to: International Journal of Data Mining and Bioinformatics
Publication Type: Peer Reviewed Journal
Publication Acceptance Date: 4/6/2015
Publication Date: 5/12/2015
Citation: Espindola, A., Schneider, W.L., Hoyt, P., Garzon, C., Marek, S.M. 2015. A new approach for detecting fungal and oomycete plant pathogens in next generation sequencing metagenome data utilising electronic probes. International Journal of Data Mining and Bioinformatics. 12(2):115-128.

Interpretive Summary: One of the most critical weapons in the fight against plant diseases is early detection, the ability to correctly identify the microbe that is causing a given disease outbreak. Many plant disease detection assays exist, but the vast majority of these assays are limited to identifying a single microbe at a time. A new technology, called next-generation sequencing, was developed for determining the genetic sequence of organisms. This technology has been used as a tool to detect microbes, but the huge amount of data generated by the technology makes next-generation sequencing microbe detection difficult and unwieldy. To correct this problem, a new computer software tool was developed, called E-probe Diagnostic Nucleic acid Assay (EDNA). This tool has the ability to ignore a lot of the less useful information that is generated by next-generation sequencing technologies, seeking out microbial sequences of interest in a manner similar to a Google search of web pages. EDNA was successfully used to detect viruses, bacteria and fungi in simulated next-generation sequencing. However, this work demonstrated that a one size fits all approach was not ideal for dealing with such diverse targets. After all, viruses are very different from bacteria, which are very different from fungi in terms of genome content. This paper describes modifications to the original EDNA tool to dramatically improve fungal detection by limiting false positive and false negative results. This technology represents a significant step forward in turning a highly powerful sequencing technology into a very useful plant disease detection technology.

Technical Abstract: Early stage infections caused by fungal/oomycete spores can remain undetected until signs or symptoms develop. Serological and molecular techniques are currently used for detecting these pathogens. Next-generation sequencing (NGS) has potential as a diagnostic tool, due to the capacity to target multiple unique signature loci of pathogens in an infected plant metagenome. NGS has significant potential as a diagnostic tool for important eukaryotic plant pathogens. However, the assembly and analysis of huge amounts of sequence is laborious, time consuming, and not necessary for diagnostic purposes. Previous work demonstrated that a bioinformatic tool termed Electronic probe for Diagnostic Nucleic Acid (EDNA) had potential for greatly simplifying detecting fungal and oomycete plant pathogens in simulated metagenomes. The initial study demonstrated limitations for detection accuracy related to the analysis of matches between queries and metagenome reads. This study is a modification of EDNA demonstrating a better accuracy for detecting Fungal and Oomycete plant pathogens.