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Next-generation diagnostics: Eliminating the excessive sequence processing associated with next-generation sequencing using EDNA.
W. L. SCHNEIDER (1), A. H. Stobbe (2), J. Daniels (2), A. S. Espindola (2), R. Verma (2), T. Blagden (2), J. Fletcher (2), F. Ochoa-Corona (2), C. Garzon (2), P. R. Hoyt (2), U. Melcher (2). (1) USDA-ARS FDWSRU, Fort Detrick, MD, U.S.A.; (2) Oklahoma State University, Stillwater, OK, U.S.A.

Deep sequencing (or next-generation sequencing) has altered the molecular biology landscape in many ways, including the development of the field of metagenomics. Not surprisingly, deep sequencing using metagenomics principles have been applied to diagnostics. The massive volumes of data generated by individual deep sequencing runs increases the likelihood of finding pathogens present at low levels. However, sorting through the volumes of data, particularly if diagnosis is the primary concern, can be cumbersome. Significant portions of any metagenome are irrelevant for pathogen detection, and the assembly of complete genomes is not actually necessary for pathogen diagnostics. The initial objective of this work was to develop bioinformatic tools for streamlining sequence data analysis for diagnostic purposes. The bioinformatic process, termed E-probe Diagnostic Nucleic acids Analysis (EDNA) avoids assembly and GenBank BLAST steps while successfully finding nucleic acid signatures of microbes of interest. <i>In silico</i> simulations indicated that EDNA was both sensitive and specific in the detection of viruses, prokaryotic and eukaryotic organisms. Initial experiments using infected plant samples suggest that EDNA can detect the same range of organisms in real metagenomes, provided the pathogen titers are sufficient. EDNA also features a high degree of flexibility, allowing adjustment of both specificity and sensitivity levels to suit the needs of the user.<p><p>Keywords:

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