![]() Indeed Gyarmati et al., 2016 4, used a sequence-based metagenomics approach directly from blood to detect non-culturable, difficult-to-culture and non-bacterial pathogens. Studies have shown that it has added value in terms of detection sensitivity and personalized treatment in clinical microbiology, when identifying bacteria 1, 2 or viruses 3. Shotgun metagenomics (SMg) is a culture-independent technique that provides valuable information not only at the identification level, but also at the level of molecular characterization. Ideally, a single method should provide rapid identification and characterization of clinically relevant pathogens directly from a sample in order to guide therapy, predict potential treatment failures and to reveal possible transmission events. However, microbial culture is laborious and time-consuming and new methods are needed to replace it. Even when unbiased molecular approaches are used, such as 16 S/18 S rRNA gene sequencing, these do not provide all the information that can be obtained by culturing, e.g., antimicrobial susceptibility and molecular typing information. Several molecular detection techniques have been implemented but these are generally geared towards specific pathogens (e.g. The tools and databases used for taxonomic classification and antimicrobial resistance identification had a key impact on the results, recommending that efforts need to be aimed at standardization of the analysis methods if metagenomics is to be used routinely in clinical microbiology.Ĭlassical microbial culture is still considered the gold standard in medical microbiology. In three samples, we could infer the probable multilocus sequence type of the most abundant species. ![]() In two cases the high number of human reads resulted in insufficient sequencing depth of bacterial DNA for identification. Most pathogens identified by culture were also identified through metagenomics, but substantial differences were noted between the taxonomic classification tools. None of the kits was clearly superior suggesting that the initial ratio between host and microbial DNA or other sample characteristics were the major determinants of the proportion of microbial reads. On average, 75% of the reads corresponded to human DNA, being a major determinant in the analysis outcome. The results of microbial identification were compared to standard culture-based microbiological methods. Data was analyzed using methods likely to be available in clinical microbiology laboratories using genomics. Libraries were prepared and sequenced with Illumina chemistry. We applied shotgun metagenomics on diverse types of patient samples using three different methods to deplete human DNA prior to DNA extraction. However, it is unclear how the variety of available methods impacts the end results. Error probabilities.High throughput sequencing has been proposed as a one-stop solution for diagnostics and molecular typing directly from patient samples, allowing timely and appropriate implementation of measures for treatment, infection prevention and control. Ewing B, Green P (1998) Base-calling of automated sequencer traces using phred.Koch CM, Chiu SF, Akbarpour M, Bharat A, Ridge KM, Bartom ET, Winter DR (2018) A Beginner’s guide to analysis of RNA sequencing data.Mortazavi A, Williams BA, McCue K, Schaeffer L, Wold B (2008) Mapping and quantifying mammalian transcriptomes by RNA-seq.Byron SA, Van Keuren-Jensen KR, Engelthaler DM, Carpten JD, Craig DW (2016) Translating RNA sequencing into clinical diagnostics: opportunities and challenges.Ozsolak F, Milos PM (2011) RNA sequencing: advances, challenges and opportunities.Royce TE, Rozowsky JS, Gerstein MB (2007) Toward a universal microarray: prediction of gene expression through nearest-neighbor probe sequence identification.Okoniewski MJ, Miller CJ (2006) Hybridization interactions between probesets in short oligo microarrays lead to spurious correlations.van Hal NL, Vorst O, van Houwelingen AM, Kok EJ, Peijnenburg A, Aharoni A, van Tunen AJ, Keijer J (2000) The application of DNA microarrays in gene expression analysis.Wang Z, Gerstein M, Snyder M (2009) RNA-seq: a revolutionary tool for transcriptomics.
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