Equencing for profiling the content of a sample), and source attribution, including profiling, sample comparison, sample engineering, and other microbial evolution or epidemiology applications. As with any analytical tool(s) for forensic application, the utility of HTS operating conditions and tolerances must be carefully defined. Regardless of the variation in technologies and software, guiding principles, such as the criteria listed in Table 1, must be established to validate HTS systems. Here we define the criteria and offer a process for validation of HTS systems in microbial forensics. Rather than delineating a set of protocols for a particular set of tools and reagents that apply to a limited set of instances, which may quickly become obsolete, those tools and reagents universally needed for protocol validation are described. By addressing each area described below, an investigator will be able to establish, validate and implement HTS as a tool for microbial forensics.Application and validation of HTS for microbial forensics Microbial forensic applications of HTS include single isolate sequencing with de novo assembly, read mapping, targeted sequencing of specified genes or other regions of interest (which generally PubMed ID:https://www.ncbi.nlm.nih.gov/pubmed/28549975 include diagnostic markers, for example, SNPs, indels, and so on) [63,64], and metagenomics. Metagenomics analyzes by sequencing DNA (or RNA) samples to identify or describe microbial community composition of environmental samples such as soil [65], plants [41,42], sea water [66,67], hospital environments [68] and human-associated habitats [69,70]. HTS makes metagenomics readily LY2510924 custom synthesis feasible since culturing is not required for sample enrichment. HTS and associated bioinformatic technologies make it possible to detect microorganisms of interest when they are present in low abundance and differentiate them from near neighbors by using diagnostic genomic signatures.Budowle et al. Investigative Genetics 2014, 5:9 http://www.investigativegenetics.com/content/5/1/Page 4 ofTable 1 Validation criteria for analytical performance metricsCriteria Analytical sensitivity Definitions Likelihood that the assay will detect a target (for example, organism variant, sequence region, functional element, and so on) in a sample (that is, target), if present; can include target attribution when defined as strain- or isolate-level detection. Also known as the true positive rate. Calculated by dividing number of true positives by the sum of true positive and false negatives (TP/(TP + FN)). Likelihood that the assay will not detect a target, if not in the sample; can include false target attribution. Also known as the true negative rate. Calculated by dividing true negatives by the sum of true negatives plus false positives (TN/(TN + FP)). May be impractical to calculate for methods designed to detect the known universe of organisms. The degree that individual measurements of the same sample are similar with regard to the presence and absence of target. Determined by the distribution of random errors and not the true or underlying value. Degree that the material measured is similar to its true value. Calculated by (TP + TN)/(TP + FP + FN + TN). The degree to which the same result(s) is obtained for a sample when the assay is repeated between/among different operators and/or detection instruments. The degree to which the same result(s) is obtained for a sample when the assay is repeated by the same operator and/or detection instrument. Minimum level.
