We are analyzing https://link.springer.com/article/10.1186/s40168-017-0396-x.

Title:
Combining 16S rRNA gene variable regions enables high-resolution microbial community profiling | Microbiome
Description:
Background Most of our knowledge about the remarkable microbial diversity on Earth comes from sequencing the 16S rRNA gene. The use of next-generation sequencing methods has increased sample number and sequencing depth, but the read length of the most widely used sequencing platforms today is quite short, requiring the researcher to choose a subset of the gene to sequence (typically 16–33% of the total length). Thus, many bacteria may share the same amplified region, and the resolution of profiling is inherently limited. Platforms that offer ultra-long read lengths, whole genome shotgun sequencing approaches, and computational frameworks formerly suggested by us and by others all allow different ways to circumvent this problem yet suffer various shortcomings. There is a need for a simple and low-cost 16S rRNA gene-based profiling approach that harnesses the short read length to provide a much larger coverage of the gene to allow for high resolution, even in harsh conditions of low bacterial biomass and fragmented DNA. Results This manuscript suggests Short MUltiple Regions Framework (SMURF), a method to combine sequencing results from different PCR-amplified regions to provide one coherent profiling. The de facto amplicon length is the total length of all amplified regions, thus providing much higher resolution compared to current techniques. Computationally, the method solves a convex optimization problem that allows extremely fast reconstruction and requires only moderate memory. We demonstrate the increase in resolution by in silico simulations and by profiling two mock mixtures and real-world biological samples. Reanalyzing a mock mixture from the Human Microbiome Project achieved about twofold improvement in resolution when combing two independent regions. Using a custom set of six primer pairs spanning about 1200 bp (80%) of the 16S rRNA gene, we were able to achieve ~ 100-fold improvement in resolution compared to a single region, over a mock mixture of common human gut bacterial isolates. Finally, the profiling of a Drosophila melanogaster microbiome using the set of six primer pairs provided a ~ 100-fold increase in resolution and thus enabling efficient downstream analysis. Conclusions SMURF enables the identification of near full-length 16S rRNA gene sequences in microbial communities, having resolution superior compared to current techniques. It may be applied to standard sample preparation protocols with very little modifications. SMURF also paves the way to high-resolution profiling of low-biomass and fragmented DNA, e.g., in the case of formalin-fixed and paraffin-embedded samples, fossil-derived DNA, or DNA exposed to other degrading conditions. The approach is not restricted to combining amplicons of the 16S rRNA gene and may be applied to any set of amplicons, e.g., in multilocus sequence typing (MLST).
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Keywords {🔍}

regions, rrna, resolution, region, gene, sequencing, number, smurf, sequences, primers, profiling, mixture, bacteria, reads, bacterium, results, sequence, article, primer, pubmed, bacterial, mock, ambiguity, dna, set, single, read, pcr, database, google, scholar, reconstruction, data, microbiome, short, pairs, additional, microbial, length, combining, sample, amplified, amplicon, figure, community, increase, preparation, size, file, compared,

Topics {✒️}

frac{q_{\mathrm{ij}}}{\sum \limits_{ }_{\mathrm{ij}}{\pi}_{\mathrm{ human microbiome research high-throughput rrna analysis full access related subjects }_{\mathrm{ij}}=\pr \left full-length ribosomal genes 16s rrna-based studies 16s ribosomal dna-sequencing high-resolution sample inference }}_{\mathrm{ih}}=\pr \left article download pdf microbial mock community 16s rrna gene microbial community reconstruction mock microbial populations—impact short multiple-regions framework microbial community composition published maps open university remarkable microbial diversity high-resolution profiling }}=\frac{\frac{\pi_{\mathrm{ variable regions identifies human microbiome project methodology comparison figure 2a shows efficient downstream analyses amplify bacteria-related sequences highly efficient profiling real-world experiment characterizing �single long-region framework single long-region framework generation sequencing methods cancer microbiome studies bacterial community reconstruction drosophila microbiome” section drosophila microbial composition article fuks real-world biological samples drosophila melanogaster microbiome privacy choices/manage cookies high resolution similar melanogaster microbiome undergoing false positive detections high-resolution identification multiple region profiling bloomington stock center desantis tz

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         headline:Combining 16S rRNA gene variable regions enables high-resolution microbial community profiling
         description:Most of our knowledge about the remarkable microbial diversity on Earth comes from sequencing the 16S rRNA gene. The use of next-generation sequencing methods has increased sample number and sequencing depth, but the read length of the most widely used sequencing platforms today is quite short, requiring the researcher to choose a subset of the gene to sequence (typically 16–33% of the total length). Thus, many bacteria may share the same amplified region, and the resolution of profiling is inherently limited. Platforms that offer ultra-long read lengths, whole genome shotgun sequencing approaches, and computational frameworks formerly suggested by us and by others all allow different ways to circumvent this problem yet suffer various shortcomings. There is a need for a simple and low-cost 16S rRNA gene-based profiling approach that harnesses the short read length to provide a much larger coverage of the gene to allow for high resolution, even in harsh conditions of low bacterial biomass and fragmented DNA. This manuscript suggests Short MUltiple Regions Framework (SMURF), a method to combine sequencing results from different PCR-amplified regions to provide one coherent profiling. The de facto amplicon length is the total length of all amplified regions, thus providing much higher resolution compared to current techniques. Computationally, the method solves a convex optimization problem that allows extremely fast reconstruction and requires only moderate memory. We demonstrate the increase in resolution by in silico simulations and by profiling two mock mixtures and real-world biological samples. Reanalyzing a mock mixture from the Human Microbiome Project achieved about twofold improvement in resolution when combing two independent regions. Using a custom set of six primer pairs spanning about 1200 bp (80%) of the 16S rRNA gene, we were able to achieve ~ 100-fold improvement in resolution compared to a single region, over a mock mixture of common human gut bacterial isolates. Finally, the profiling of a Drosophila melanogaster microbiome using the set of six primer pairs provided a ~ 100-fold increase in resolution and thus enabling efficient downstream analysis. SMURF enables the identification of near full-length 16S rRNA gene sequences in microbial communities, having resolution superior compared to current techniques. It may be applied to standard sample preparation protocols with very little modifications. SMURF also paves the way to high-resolution profiling of low-biomass and fragmented DNA, e.g., in the case of formalin-fixed and paraffin-embedded samples, fossil-derived DNA, or DNA exposed to other degrading conditions. The approach is not restricted to combining amplicons of the 16S rRNA gene and may be applied to any set of amplicons, e.g., in multilocus sequence typing (MLST).
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         16S rRNA gene
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         Medical Microbiology
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         Microbial Genetics and Genomics
         Virology
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      name:Department of Microbiology and Immunology, University of California San Francisco, San Francisco, USA
      name:Department of Biomolecular Sciences, Weizmann Institute of Science, Rehovot, Israel
      name:Department of Computer Science, The Open University of Israel, Ra’anana, Israel

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