Evidence map›Paper›PMID 29373999›Full record

ArticleMicrobiome2018

Combining 16S rRNA gene variable regions enables high-resolution microbial community profiling.

Garold Fuks, Michael Elgart, Amnon Amir, Amit Zeisel, Peter J Turnbaugh, Yoav Soen, Noam Shental

Open access · goldAbstract read
In one paragraph

Article in Microbiome, 2018. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 139 papers, 1 of them a synthesis that pooled it.

0numbers the graph read from it
0cells of the map it votes in
139citing papers in PubMed, 1 pooled it
13.8field-weighted citation impact, top 1% of its field
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

139 citing papers in PubMed, 1 synthesis or guideline pooled it, 264 citations in OpenAlex.

  1. Pooled it
  2. Article
  3. Review
  4. Article
  5. Formic acid released by intratumoralActa pharmaceutica Sinica. B · 2026
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  12. Maternal PMParticle and fibre toxicology · 2026
    Article
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  18. Article
  19. IntratumoralFrontiers in immunology · 2026
    Article
  20. Article

79 more citing papers are in PubMed but not listed here.

4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

7 authors at 5 institutions in 3 countries.

Garold FuksDepartments of Physics of Complex Systems, Weizmann Institute of Science, 7610001, Rehovot, Israel.
Michael ElgartDepartment of Biomolecular Sciences, Weizmann Institute of Science, 7610001, Rehovot, Israel.
Amnon AmirDepartment of Pediatrics, University of California San Diego, La Jolla, CA, 92093, USA.
Amit ZeiselDivision of Molecular Neurobiology, Department of Medical Biochemistry and Biophysics, 10 Karolinska Institutet, S-171 77, Stockholm, Sweden.
Peter J TurnbaughDepartment of Microbiology and Immunology, University of California San Francisco, San Francisco, CA, 94143, USA.
Yoav SoenDepartment of Biomolecular Sciences, Weizmann Institute of Science, 7610001, Rehovot, Israel.
Noam ShentalDepartment of Computer Science, The Open University of Israel, 43107, Ra'anana, Israel. shental@openu.ac.il.
Weizmann Institute of Science · ILKarolinska Institutet · SEOpen University of Israel · ILUniversity of California, San Diego · USUniversity of California, San Francisco · US

Funding

Predicting and preventing drug metabolism by the human gut microbiomeR01HL122593 · NHLBI · UNIVERSITY OF CALIFORNIA, SAN FRANCISCO · PI Peter James Turnbaugh · 2016 to 2026
$5.7M
Ministry of Science and Technology, Israel 3-11174NHLBI NIH HHS R01 HL122593
6 · The paper itself

Abstract

backgroundMost 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.

resultsThis 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.

conclusionsSMURF 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).

Indexed as

AlgorithmsAnimalsBacteriaComputer SimulationDNA, BacterialDNA ProbesDrosophila melanogasterMicrobiotaPhylogenyPolymerase Chain ReactionRNA, Ribosomal, 16SSequence Analysis, DNADNA, BacterialDNA ProbesRNA, Ribosomal, 16S16S rRNA geneHigh resolutionMicrobial profilingMicrobiome

Identifiers

PMID29373999
PMCPMC5787238
OpenAlexW2952837153

What OpenQuestion holds

Textmetadata
LicenceCC BY
Read underepoch 390

Registered trials

None linked

Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.