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      Characterizing Microbiomes via Sequencing of Marker Loci: Techniques To Improve Throughput, Account for Cross-Contamination, and Reduce Cost

      research-article
      a , , a , a
      (ad hoc peer reviewer)
      mSystems
      American Society for Microbiology
      microbiome, high throughput, next-generation sequencing, spike in, internal standard, library preparation, PCR, automation, multiplexing, metabarcoding

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          ABSTRACT

          New approaches to characterizing microbiomes via high-throughput sequencing provide impressive gains in efficiency and cost reduction compared to approaches that were standard just a few years ago. However, the speed of method development has been such that staying abreast of the latest technological advances is challenging. Moreover, shifting laboratory protocols to include new methods can be expensive and time consuming. To facilitate adoption of new techniques, we provide a guide and review of recent advances that are relevant for single-locus sequence-based study of microbiomes—from extraction to library preparation—including a primer regarding the use of liquid-handling automation in small-scale academic settings. Additionally, we describe several amendments to published techniques to improve throughput, track contamination, and reduce cost. Notably, we suggest adding synthetic DNA molecules to each sample during nucleic acid extraction, thus providing a method of documenting incidences of cross-contamination. We also describe a dual-indexing scheme for Illumina sequencers that allows multiplexing of many thousands of samples with minimal PhiX input. Collectively, the techniques that we describe demonstrate that laboratory technology need not impose strict limitations on the scale of molecular microbial ecology studies.

          IMPORTANCE New methods to characterize microbiomes reduce technology-imposed limitations to study design, but many new approaches have not been widely adopted. Here, we present techniques to increase throughput and reduce contamination alongside a thorough review of current best practices.

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          Most cited references125

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          Predictive functional profiling of microbial communities using 16S rRNA marker gene sequences

          Profiling phylogenetic marker genes, such as the 16S rRNA gene, is a key tool for studies of microbial communities but does not provide direct evidence of a community’s functional capabilities. Here we describe PICRUSt (Phylogenetic Investigation of Communities by Reconstruction of Unobserved States), a computational approach to predict the functional composition of a metagenome using marker gene data and a database of reference genomes. PICRUSt uses an extended ancestral-state reconstruction algorithm to predict which gene families are present and then combines gene families to estimate the composite metagenome. Using 16S information, PICRUSt recaptures key findings from the Human Microbiome Project and accurately predicts the abundance of gene families in host-associated and environmental communities, with quantifiable uncertainty. Our results demonstrate that phylogeny and function are sufficiently linked that this ‘predictive metagenomic’ approach should provide useful insights into the thousands of uncultivated microbial communities for which only marker gene surveys are currently available.
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            Greengenes, a Chimera-Checked 16S rRNA Gene Database and Workbench Compatible with ARB

            A 16S rRNA gene database ( http://greengenes.lbl.gov ) addresses limitations of public repositories by providing chimera screening, standard alignment, and taxonomic classification using multiple published taxonomies. It was found that there is incongruent taxonomic nomenclature among curators even at the phylum level. Putative chimeras were identified in 3% of environmental sequences and in 0.2% of records derived from isolates. Environmental sequences were classified into 100 phylum-level lineages in the Archaea and Bacteria .
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              Development of a dual-index sequencing strategy and curation pipeline for analyzing amplicon sequence data on the MiSeq Illumina sequencing platform.

              Rapid advances in sequencing technology have changed the experimental landscape of microbial ecology. In the last 10 years, the field has moved from sequencing hundreds of 16S rRNA gene fragments per study using clone libraries to the sequencing of millions of fragments per study using next-generation sequencing technologies from 454 and Illumina. As these technologies advance, it is critical to assess the strengths, weaknesses, and overall suitability of these platforms for the interrogation of microbial communities. Here, we present an improved method for sequencing variable regions within the 16S rRNA gene using Illumina's MiSeq platform, which is currently capable of producing paired 250-nucleotide reads. We evaluated three overlapping regions of the 16S rRNA gene that vary in length (i.e., V34, V4, and V45) by resequencing a mock community and natural samples from human feces, mouse feces, and soil. By titrating the concentration of 16S rRNA gene amplicons applied to the flow cell and using a quality score-based approach to correct discrepancies between reads used to construct contigs, we were able to reduce error rates by as much as two orders of magnitude. Finally, we reprocessed samples from a previous study to demonstrate that large numbers of samples could be multiplexed and sequenced in parallel with shotgun metagenomes. These analyses demonstrate that our approach can provide data that are at least as good as that generated by the 454 platform while providing considerably higher sequencing coverage for a fraction of the cost.
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                Author and article information

                Contributors
                Role: Editor
                Role: ad hoc peer reviewer
                Journal
                mSystems
                mSystems
                msystems
                mSystems
                American Society for Microbiology (1752 N St., N.W., Washington, DC )
                2379-5077
                13 July 2021
                Jul-Aug 2021
                13 July 2021
                : 6
                : 4
                : e00294-21
                Affiliations
                [a ] University of Wyoming, Laramie, Wyoming, USA
                University of California, San Francisco
                National Institute of Advanced Industrial Science and Technology (AIST)
                Author notes

                Citation Harrison JG, Randolph GD, Buerkle CA. 2021. Characterizing microbiomes via sequencing of marker loci: techniques to improve throughput, account for cross-contamination, and reduce cost. mSystems 6:e00294-21. https://doi.org/10.1128/mSystems.00294-21.

                Author information
                https://orcid.org/0000-0003-2524-0273
                https://orcid.org/0000-0003-4222-8858
                Article
                mSystems00294-21
                10.1128/mSystems.00294-21
                8409480
                34254828
                ef70ec7a-a2b3-4a59-9714-5c1f5754661a
                Copyright © 2021 Harrison et al.

                This is an open-access article distributed under the terms of the Creative Commons Attribution 4.0 International license.

                History
                : 10 March 2021
                : 7 June 2021
                Page count
                supplementary-material: 7, Figures: 4, Tables: 0, Equations: 0, References: 128, Pages: 24, Words: 16163
                Funding
                Funded by: National Science Foundation (NSF), FundRef https://doi.org/10.13039/100000001;
                Award ID: EPS-1655726
                Award Recipient :
                Categories
                Methods and Protocols
                open-peer-review, Open Peer Review
                ecology, Ecology
                Custom metadata
                July/August 2021

                microbiome,high throughput,next-generation sequencing,spike in,internal standard,library preparation,pcr,automation,multiplexing,metabarcoding

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