The study examined the variability in 16S ribosomal RNA (16S rRNA) and tetracycline resistance tetA gene quantification from environmental samples in relation to modifications in quantitative polymerase chain reaction (qPCR) workflow and subsequent data evaluation and analysis. We analysed three types of soil samples using two DNA extraction methods, two qPCR chemistries (SYBR green, LUX™), and qPCR reaction kits from different manufacturers. To improve data quality, we employed a three-step amplification outlier removal approach prior to gene quantification calculations. We compared three variants of target gene enumerations and four variants of functional tetA gene normalisations against 16S rRNA genes. Results reveal that modifications in qPCR workflow steps significantly influence the gene quantification results from environmental samples. Primary factors affecting qPCR amplification efficiency included the variability of the target amplicon and the qPCR chemistry; the quality of the resulting datasets also had an impact. Although LUX™ qPCR has shown promise for environmental samples, SYBR green qPCR yielded considerably better-quality datasets and higher, more stable amplification efficiency values. Gene enumeration data of outlier-removed and unmodified sample sets showed minor differences for good-quality datasets (i.e., amplifications with SYBR green), but differed by up to 40% among lower-quality datasets. Different DNA extraction methods yielded varying amounts and purities of extracted microbial community DNA from environmental samples, with as much as an order of magnitude variation in gene copy numbers. Target gene normalisations yielded stable results on good-quality data, regardless of the DNA extraction method or qPCR chemistry used. Even though qPCR is regarded as a precise method with low detection limit, technical variability in the qPCR workflow tends to overestimate or effectively mask minute changes in community.
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