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MiFi ™ is a computer program that was developed on the concept of E-probe diagnostics for nucleic acid analysis (Stobbe, et al., 2013; Espindola et al., 2015) to detect and diagnose multiple pathogens in a given sample using metagenomic sequencing and bioinformatic tools. Developed in collaboration between the Oklahoma State University, National Institute of Microbial Forensics and Food and Agricultural Biosecurity, and the U.S. Department of Agriculture Agricultural Research Service, MiFi™ is an online graphic user interface comprised of two parts.


  1. MiProbe© houses all tools needed for building and validating E-probes. E-probes are carefully designed unique nucleic acid signatures of plant, animal and food-borne pathogen genomes, validated for sensitivity, specificity, and limit of detection. E-probes are used in silicoto detect presence or absence of one or more pathogens in raw host sequence data.
  2. MiDetect© is the diagnostic side of the program that rapidly identifies the genetic signatures of target pathogens (E-probes) in metagenomic datasets (DNA or RNA sequence data of host tissue as well as all associated microbes). E-Probes can be developed for any pathogen or pest of regulatory or commercial importance to an industry, so that rather than doing many sequential tests, one per each pathogen, with MiDetect©users can test for all pathogens of concern in a single test on a single sample. MiDetect©eliminates many of the time consuming steps in disease diagnostics, requiring no genomic or transcriptomic assembly or other costly and resource intensive protocols like development of PCR primers for the identification of microbes and pathogens. No isolation or amplification of the target pathogen is required. Host genomic data can be left in the query, or removed in silico. The price of DNA sequencing is rapidly decreasing, making whole genome sequencing more affordable and enhancing the future of nucleic acid diagnostic testing. This can make MiDetect©the diagnostic tool of choice in real case scenarios. Common uses in plant pathogen detection include sampling from the plant rhizosphere, phyllosphere, seed or soil and subsequent sequencing of the plant specimen containing whatever microbes and pathogens are in residence. For animal disease diagnostics, a tissue sample or swab is sequenced and queried with known microbe genomic probes. Raw sequence reads can then be uploaded into the MiDetect©platform and analyzed with a library of relevant pathogen E-probes to verify presence or absence of all suspected organisms in minutes.


Espindola, A.S., Schneider, W., Hoyt, P.R., Marek, S.M., Cardwell, K.F., Garzon, C.D. 2018 (submitted). Inferring the presence of aflatoxin producing Aspergillus flavus strains using RNA Sequencing. Phytopathology, 0:00.


Espindola, A., W. Schneider, K.F. Cardwell, P. Hoyt, S. Marek and C. Garzon. 2018 (submitted) Rapid identification of Oomycete plant pathogens using EDNA as a metagenome screening tool. Phytopathology, 0:00.


Lizbeth Peña Zuñiga, Andrés Espíndola, Hassan Melouk, Akhtar Ali, Cardwell Kitty and Francisco M. Ochoa-Corona. 2017. Detection of cucurbit viruses in Oklahoma combining EDNA with Multiplex RT-PCR coupled with High Resolution Melting. (Abstr.) Phytopathology 107:S5.36.


Lizbeth Peña, Andrés Espíndola, Patricia Klein, Thomas Debener, Jasper Rees, David Byrne, Kitty Cardwell and Francisco M. Ochoa-Corona. 2017. EDNA-Rose a novel approach for detecting rose viruses combining next generation sequencing and bioinformatics. (Abstr.) Phytopathology 107: S5.58.


Blagden, T., W. Schneider, U. Melcher, J. Daniels, and J. Fletcher. 2016. Adaptation and validation of E-probe diagnostic nucleic acid analysis for detection of Escherichia coli O157:H7 in metagenomics data of complex food matrices. J. Food Sci. 79:574-581.


Espindola A, Schneider W, Hoyt PR, Marek SM, Garzon C:. A new approach for detecting fungal and oomycete plant pathogens in next generation sequencing metagenome data utilizing electronic probes. Int. J. Data Mining and Bioinformatics. 2015. Vol. 12 (2):115-128.


Stobbe AH., Daniels J., Espindola AS., Verma R., Melcher U., Ochoa-Corona F., Garzon C., Fletcher J., Schneider W. 2013. E-probe Diagnostic Nucleic acid Analysis (EDNA): A theoretical approach for handling of next generation sequencing data for diagnostics. Microbiological Methods. 94(3):356-66.


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