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      FungiQuant: A broad-coverage fungal quantitative real-time PCR assay

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          Abstract

          Background

          Fungal load quantification is a critical component of fungal community analyses. Limitation of current approaches for quantifying the fungal component in the human microbiome suggests the need for new broad-coverage techniques.

          Methods

          We analyzed 2,085 18S rRNA gene sequences from the SILVA database for assay design. We generated and quantified plasmid standards using a qPCR-based approach. We evaluated assay coverage against 4,968 sequences and performed assay validation following the Minimum Information for Publication of Quantitative Real-Time PCR Experiments (MIQE) guidelines.

          Results

          We designed FungiQuant, a TaqMan ® qPCR assay targeting a 351 bp region in the fungal 18S rRNA gene. Our in silico analysis showed that FungiQuant is a perfect sequence match to 90.0% of the 2,617 fungal species analyzed. We showed that FungiQuant’s is 100% sensitive and its amplification efficiencies ranged from 76.3% to 114.5%, with r 2 -values of >0.99 against the 69 fungal species tested. Additionally, FungiQuant inter- and intra-run coefficients of variance ranged from <10% and <20%, respectively. We further showed that FungiQuant has a limit of quantification 25 copies and a limit of detection at 5 copies. Lastly, by comparing results from human-only background DNA with low-level fungal DNA, we showed that amplification in two or three of a FungiQuant performed in triplicate is statistically significant for true positive fungal detection.

          Conclusions

          FungiQuant has comprehensive coverage against diverse fungi and is a robust quantification and detection tool for delineating between true fungal detection and non-target human DNA.

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          Most cited references28

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          OligoCalc: an online oligonucleotide properties calculator

          We developed OligoCalc as a web-accessible, client-based computational engine for reporting DNA and RNA single-stranded and double-stranded properties, including molecular weight, solution concentration, melting temperature, estimated absorbance coefficients, inter-molecular self-complementarity estimation and intra-molecular hairpin loop formation. OligoCalc has a familiar ‘calculator’ look and feel, making it readily understandable and usable. OligoCalc incorporates three common methods for calculating oligonucleotide-melting temperatures, including a nearest-neighbor thermodynamic model for melting temperature. Since it first came online in 1997, there have been more than 900 000 accesses of OligoCalc from nearly 200 000 distinct hosts, excluding search engines. OligoCalc is available at http://basic.northwestern.edu/biotools/OligoCalc.html, with links to the full source code, usage patterns and statistics at that link as well.
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            The magnitude of fungal diversity: the 1.5 million species estimate revisited

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              Quantifying microbial communities with 454 pyrosequencing: does read abundance count?

              Pyrosequencing technologies have revolutionized how we describe and compare complex microbial communities. In 454 pyrosequencing data sets, the abundance of reads pertaining to taxa or phylotypes is commonly interpreted as a measure of genic or taxon abundance, useful for quantitative comparisons of community similarity. Potentially systematic biases inherent in sample processing, amplification and sequencing, however, may alter read abundance and reduce the utility of quantitative metrics. Here, we examine the relationship between read abundance and biological abundance in a sample of house dust spiked with known quantities and identities of fungi along a dilution gradient. Our results show one order of magnitude differences in read abundance among species. Precision of quantification within species along the dilution gradient varied from R(2) of 0.96-0.54. Read-quality based processing stringency profoundly affected the abundance of one species containing long homopolymers in a read orientation-biased manner. Order-level composition of background environmental fungal communities determined from pyrosequencing data was comparable with that derived from cloning and Sanger sequencing and was not biased by read orientation. We conclude that read abundance is approximately quantitative within species, but between-species comparisons can be biased by innate sequence structure. Our results showed a trade off between sequence quality stringency and quantification. Careful consideration of sequence processing methods and community analyses are warranted when testing hypotheses using read abundance data. © 2010 Her Majesty the Queen in Right of Canada, as represented by the Minister of Agriculture and Agri-Food Canada.
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                Author and article information

                Contributors
                Journal
                BMC Microbiol
                BMC Microbiol
                BMC Microbiology
                BioMed Central
                1471-2180
                2012
                8 November 2012
                : 12
                : 255
                Affiliations
                [1 ]Division of Pathogen Genomics, Translational Genomics Research Institute, Flagstaff, AZ, 86011, USA
                [2 ]Center for Microbial Genetics and Genomics, Northern Arizona University, Flagstaff, AZ, 86011, USA
                [3 ]Department of Epidemiology, The Johns Hopkins Bloomberg School of Public Health, Baltimore, MD, 21201, USA
                [4 ]Departments of Laboratory Medicine and Internal Medicine, National Taiwan University Hospital, National Taiwan University College of Medicine, Taipei, Taiwan
                [5 ]Department of Internal Medicine, Far Eastern Memorial Hospital, New Taipei City, Taiwan
                [6 ]Department of Biological Sciences, Northern Arizona University, Flagstaff, AZ, 86011, USA
                [7 ]Current address: Ross University School of Medicine, Roseau, Dominica
                Article
                1471-2180-12-255
                10.1186/1471-2180-12-255
                3565980
                23136846
                4bcd0170-6706-4f12-876a-a761cfca7bbf
                Copyright ©2012 Liu et al.; licensee BioMed Central Ltd.

                This is an Open Access article distributed under the terms of the Creative Commons Attribution License ( http://creativecommons.org/licenses/by/2.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

                History
                : 10 May 2012
                : 24 October 2012
                Categories
                Methodology Article

                Microbiology & Virology
                Microbiology & Virology

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