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      Comparison of DNA-, PMA-, and RNA-based 16S rRNA Illumina sequencing for detection of live bacteria in water

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          Abstract

          The limitation of 16S rRNA gene sequencing (DNA-based) for microbial community analyses in water is the inability to differentiate live (dormant cells as well as growing or non-growing metabolically active cells) and dead cells, which can lead to false positive results in the absence of live microbes. Propidium-monoazide (PMA) has been used to selectively remove DNA from dead cells during downstream sequencing process. In comparison, 16S rRNA sequencing (RNA-based) can target live microbial cells in water as both dormant and metabolically active cells produce rRNA. The objective of this study was to compare the efficiency and sensitivity of DNA-based, PMA-based and RNA-based 16S rRNA Illumina sequencing methodologies for live bacteria detection in water samples experimentally spiked with different combination of bacteria (2 gram-negative and 2 gram-positive/acid fast species either all live, all dead, or combinations of live and dead species) or obtained from different sources (First Nation community drinking water; city of Winnipeg tap water; water from Red River, Manitoba, Canada). The RNA-based method, while was superior for detection of live bacterial cells still identified a number of 16S rRNA targets in samples spiked with dead cells. In environmental water samples, the DNA- and PMA-based approaches perhaps overestimated the richness of microbial community compared to RNA-based method. Our results suggest that the RNA-based sequencing was superior to DNA- and PMA-based methods in detecting live bacterial cells in water.

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          FLASH: fast length adjustment of short reads to improve genome assemblies.

          Next-generation sequencing technologies generate very large numbers of short reads. Even with very deep genome coverage, short read lengths cause problems in de novo assemblies. The use of paired-end libraries with a fragment size shorter than twice the read length provides an opportunity to generate much longer reads by overlapping and merging read pairs before assembling a genome. We present FLASH, a fast computational tool to extend the length of short reads by overlapping paired-end reads from fragment libraries that are sufficiently short. We tested the correctness of the tool on one million simulated read pairs, and we then applied it as a pre-processor for genome assemblies of Illumina reads from the bacterium Staphylococcus aureus and human chromosome 14. FLASH correctly extended and merged reads >99% of the time on simulated reads with an error rate of <1%. With adequately set parameters, FLASH correctly merged reads over 90% of the time even when the reads contained up to 5% errors. When FLASH was used to extend reads prior to assembly, the resulting assemblies had substantially greater N50 lengths for both contigs and scaffolds. The FLASH system is implemented in C and is freely available as open-source code at http://www.cbcb.umd.edu/software/flash. t.magoc@gmail.com.
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            Evaluating rRNA as an indicator of microbial activity in environmental communities: limitations and uses.

            Microbes exist in a range of metabolic states (for example, dormant, active and growing) and analysis of ribosomal RNA (rRNA) is frequently employed to identify the 'active' fraction of microbes in environmental samples. While rRNA analyses are no longer commonly used to quantify a population's growth rate in mixed communities, due to rRNA concentration not scaling linearly with growth rate uniformly across taxa, rRNA analyses are still frequently used toward the more conservative goal of identifying populations that are currently active in a mixed community. Yet, evidence indicates that the general use of rRNA as a reliable indicator of metabolic state in microbial assemblages has serious limitations. This report highlights the complex and often contradictory relationships between rRNA, growth and activity. Potential mechanisms for confounding rRNA patterns are discussed, including differences in life histories, life strategies and non-growth activities. Ways in which rRNA data can be used for useful characterization of microbial assemblages are presented, along with questions to be addressed in future studies.
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              The truth about metagenomics: quantifying and counteracting bias in 16S rRNA studies

              Background Characterizing microbial communities via next-generation sequencing is subject to a number of pitfalls involving sample processing. The observed community composition can be a severe distortion of the quantities of bacteria actually present in the microbiome, hampering analysis and threatening the validity of conclusions from metagenomic studies. We introduce an experimental protocol using mock communities for quantifying and characterizing bias introduced in the sample processing pipeline. We used 80 bacterial mock communities comprised of prescribed proportions of cells from seven vaginally-relevant bacterial strains to assess the bias introduced in the sample processing pipeline. We created two additional sets of 80 mock communities by mixing prescribed quantities of DNA and PCR product to quantify the relative contribution to bias of (1) DNA extraction, (2) PCR amplification, and (3) sequencing and taxonomic classification for particular choices of protocols for each step. We developed models to predict the “true” composition of environmental samples based on the observed proportions, and applied them to a set of clinical vaginal samples from a single subject during four visits. Results We observed that using different DNA extraction kits can produce dramatically different results but bias is introduced regardless of the choice of kit. We observed error rates from bias of over 85% in some samples, while technical variation was very low at less than 5% for most bacteria. The effects of DNA extraction and PCR amplification for our protocols were much larger than those due to sequencing and classification. The processing steps affected different bacteria in different ways, resulting in amplified and suppressed observed proportions of a community. When predictive models were applied to clinical samples from a subject, the predicted microbiome profiles were better reflections of the physiology and diagnosis of the subject at the visits than the observed community compositions. Conclusions Bias in 16S studies due to DNA extraction and PCR amplification will continue to require attention despite further advances in sequencing technology. Analysis of mock communities can help assess bias and facilitate the interpretation of results from environmental samples. Electronic supplementary material The online version of this article (doi:10.1186/s12866-015-0351-6) contains supplementary material, which is available to authorized users.
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                Author and article information

                Contributors
                Annemieke.Farenhorst@umanitoba.ca
                Ehsan.Khafipour@umanitoba.ca
                Journal
                Sci Rep
                Sci Rep
                Scientific Reports
                Nature Publishing Group UK (London )
                2045-2322
                18 July 2017
                18 July 2017
                2017
                : 7
                : 5752
                Affiliations
                [1 ]ISNI 0000 0004 1936 9609, GRID grid.21613.37, Department of Soil Science, , University of Manitoba, ; Winnipeg, MB R3T 2N2 Canada
                [2 ]ISNI 0000 0004 1936 9609, GRID grid.21613.37, Department of Animal Science, , University of Manitoba, ; Winnipeg, MB R3T 2N2 Canada
                [3 ]ISNI 0000 0004 1936 9609, GRID grid.21613.37, Department of Medical Microbiology, , University of Manitoba, ; Winnipeg, MB R3T 2N2 Canada
                [4 ]ISNI 0000 0004 1936 9609, GRID grid.21613.37, Department of Microbiology, , University of Manitoba, ; Winnipeg, MB R3T 2N2 Canada
                [5 ]ISNI 0000 0004 1936 9609, GRID grid.21613.37, Department of Plant Science, , University of Manitoba, ; Winnipeg, MB R3T 2N2 Canada
                [6 ]GRID grid.410696.c, Department of plant protection, , Yunnan Agricultural University, ; Kunming, Yunnan province 650201 China
                [7 ]GRID grid.17089.37, Department of Pediatrics, , University of Alberta, ; AB, Canada
                Author information
                http://orcid.org/0000-0001-7597-5062
                Article
                2516
                10.1038/s41598-017-02516-3
                5515937
                28720878
                2477bd78-2882-47b5-a837-2a605a75d3fc
                © The Author(s) 2017

                Open Access This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made. The images or other third party material in this article are included in the article’s Creative Commons license, unless indicated otherwise in a credit line to the material. If material is not included in the article’s Creative Commons license and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this license, visit http://creativecommons.org/licenses/by/4.0/.

                History
                : 30 January 2017
                : 30 March 2017
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