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

Authors: Andres Espindola; William Schneider; Peter R. Hoyt; Stephen M. Marek; Carla Garzon

Addresses: Entomology and Plant Pathology Department, Oklahoma State University, 127 Noble Research Center Stillwater OK 74078, USA ' USDA-ARS FDWSRU, 1301 Ditto Ave. Fort Detrick MD 21702, USA ' Department of Biochemistry and Molecular Biology, 246 Noble Research Center Stillwater OK 74078, USA ' Department of Entomology and Plant Pathology, Oklahoma State University, 127 Noble Research Center Stillwater OK 7408, USA ' Department of Entomology and Plant Pathology, 127 Noble Research Center Stillwater OK 74078, USA

Abstract: Early stage infections caused by fungal/oomycete spores may not be detected 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 for diagnosis of 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 Diagnostic Nucleic acid Analysis (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.

Keywords: EDNA; nucleic acid analysis; electronic probes; e-probes; Puccinia graminis; Pythium ultimum; Phakopsora pachyrhizi; Phytophthora ramorum; 454 Roche; diagnostics; fungi; pucciniomycetes; Chromalveolata; oomycetes; eukaryotic plant pathogens; next generation sequencing; NGS; metagenome data; early stage infections; fungal spores; oomycete spores; simulation.

DOI: 10.1504/IJDMB.2015.069422

International Journal of Data Mining and Bioinformatics, 2015 Vol.12 No.2, pp.115 - 128

Received: 14 Feb 2014
Accepted: 24 Jun 2014
Published online: 15 May 2015 *

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