Literature DB >> 23867249

E-probe Diagnostic Nucleic acid Analysis (EDNA): a theoretical approach for handling of next generation sequencing data for diagnostics.

Anthony H Stobbe1, Jon Daniels, Andres S Espindola, Ruchi Verma, Ulrich Melcher, Francisco Ochoa-Corona, Carla Garzon, Jacqueline Fletcher, William Schneider.   

Abstract

Plant biosecurity requires rapid identification of pathogenic organisms. While there are many pathogen-specific diagnostic assays, the ability to test for large numbers of pathogens simultaneously is lacking. Next generation sequencing (NGS) allows one to detect all organisms within a given sample, but has computational limitations during assembly and similarity searching of sequence data which extend the time needed to make a diagnostic decision. To minimize the amount of bioinformatic processing time needed, unique pathogen-specific sequences (termed e-probes) were designed to be used in searches of unassembled, non-quality checked, sequence data. E-probes have been designed and tested for several selected phytopathogens, including an RNA virus, a DNA virus, bacteria, fungi, and an oomycete, illustrating the ability to detect several diverse plant pathogens. E-probes of 80 or more nucleotides in length provided satisfactory levels of precision (75%). The number of e-probes designed for each pathogen varied with the genome size of the pathogen. To give confidence to diagnostic calls, a statistical method of determining the presence of a given pathogen was developed, in which target e-probe signals (detection signal) are compared to signals generated by a decoy set of e-probes (background signal). The E-probe Diagnostic Nucleic acid Analysis (EDNA) process provides the framework for a new sequence-based detection system that eliminates the need for assembly of NGS data.
© 2013.

Keywords:  Bioinformatics; Next-generation sequencing; Pathogen detection

Mesh:

Substances:

Year:  2013        PMID: 23867249     DOI: 10.1016/j.mimet.2013.07.002

Source DB:  PubMed          Journal:  J Microbiol Methods        ISSN: 0167-7012            Impact factor:   2.363


  9 in total

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Authors:  Andres S Espindola; William Schneider; Kitty F Cardwell; Yisel Carrillo; Peter R Hoyt; Stephen M Marek; Hassan A Melouk; Carla D Garzon
Journal:  PLoS One       Date:  2018-10-16       Impact factor: 3.240

3.  Cluster oligonucleotide signatures for rapid identification by sequencing.

Authors:  Manuel Zahariev; Wen Chen; Cobus M Visagie; C André Lévesque
Journal:  BMC Bioinformatics       Date:  2018-10-29       Impact factor: 3.169

4.  Microbe Finder (MiFi®): Implementation of an Interactive Pathogen Detection Tool in Metagenomic Sequence Data.

Authors:  Andres S Espindola; Kitty F Cardwell
Journal:  Plants (Basel)       Date:  2021-01-28

Review 5.  A Primer on the Analysis of High-Throughput Sequencing Data for Detection of Plant Viruses.

Authors:  Denis Kutnjak; Lucie Tamisier; Ian Adams; Neil Boonham; Thierry Candresse; Michela Chiumenti; Kris De Jonghe; Jan F Kreuze; Marie Lefebvre; Gonçalo Silva; Martha Malapi-Wight; Paolo Margaria; Irena Mavrič Pleško; Sam McGreig; Laura Miozzi; Benoit Remenant; Jean-Sebastien Reynard; Johan Rollin; Mike Rott; Olivier Schumpp; Sébastien Massart; Annelies Haegeman
Journal:  Microorganisms       Date:  2021-04-14

Review 6.  Metagenomic search strategies for interactions among plants and multiple microbes.

Authors:  Ulrich Melcher; Ruchi Verma; William L Schneider
Journal:  Front Plant Sci       Date:  2014-06-11       Impact factor: 5.753

Review 7.  Validation of high throughput sequencing and microbial forensics applications.

Authors:  Bruce Budowle; Nancy D Connell; Anna Bielecka-Oder; Rita R Colwell; Cindi R Corbett; Jacqueline Fletcher; Mats Forsman; Dana R Kadavy; Alemka Markotic; Stephen A Morse; Randall S Murch; Antti Sajantila; Sarah E Schmedes; Krista L Ternus; Stephen D Turner; Samuel Minot
Journal:  Investig Genet       Date:  2014-07-30

8.  LAMP Detection Assays for Boxwood Blight Pathogens: A Comparative Genomics Approach.

Authors:  Martha Malapi-Wight; Jill E Demers; Daniel Veltri; Robert E Marra; Jo Anne Crouch
Journal:  Sci Rep       Date:  2016-05-20       Impact factor: 4.379

9.  TASPERT: Target-Specific Reverse Transcript Pools to Improve HTS Plant Virus Diagnostics.

Authors:  Andres S Espindola; Daniela Sempertegui-Bayas; Danny F Bravo-Padilla; Viviana Freire-Zapata; Francisco Ochoa-Corona; Kitty F Cardwell
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  9 in total

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