BMC Microbiology · 2012 · 288 citations · 30 references
Life SciencesBioinformaticsMicrobiologyBacteriologyMicrobial BiotechnologyMicrobial Ecology16S Ribosomal Ribonucleic Acid AnalysisMicrobial Community ProfilingAssayCoverage AnalysisGene Copy Number AnalysisMolecular Detection AssaysMolecular Detection Of BacteriaPlasmid CloningPolymerase Chain Reaction (Pcr) AssaysQuantitative Pcr Detection
BACKGROUND: Bacterial load quantification is a critical component of bacterial community analysis, but a culture-independent method capable of detecting and quantifying diverse bacteria is needed. Based on our analysis of a diverse collection of 16 S rRNA gene sequences, we designed a broad-coverage quantitative real-time PCR (qPCR) assay--BactQuant--for quantifying 16 S rRNA gene copy number and estimating bacterial load. We further utilized in silico evaluation to complement laboratory-based qPCR characterization to validate BactQuant. METHODS: The aligned core set of 4,938 16 S rRNA gene sequences in the Greengenes database were analyzed for assay design. Cloned plasmid standards were generated and quantified using a qPCR-based approach. Coverage analysis was performed computationally using >670,000 sequences and further evaluated following the Minimum Information for Publication of Quantitative Real-Time PCR Experiments (MIQE) guidelines. RESULTS: A bacterial TaqMan® qPCR assay targeting a 466 bp region in V3-V4 was designed. Coverage analysis showed that 91% of the phyla, 96% of the genera, and >80% of the 89,537 species analyzed contained at least one perfect sequence match to the BactQuant assay. Of the 106 bacterial species evaluated, amplification efficiencies ranged from 81 to 120%, with r2-value of >0.99, including species with sequence mismatches. Inter- and intra-run coefficient of variance was <3% and <16% for Ct and copy number, respectively. CONCLUSIONS: The BactQuant assay offers significantly broader coverage than a previously reported universal bacterial quantification assay BactQuant in vitro performance was better than the in silico predictions.
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R: A Language and Environment for Statistical Computing
R Core Team · 2000 · 353K citations · Full text
Natural Sciences, Computer Science, Statistical Computing +10
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Koichiro Tamura, Joel T. Dudley, M Nei et al. · Molecular Biology and Evolution · 2007 · 28.8K citations · Full text
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Peter J. Turnbaugh, Micah Hamady, Tanya Yatsunenko et al. · Nature · 2008 · 7.9K citations · Full text