Evidence map›Paper›PMID 23377949›Full record

ArticleApplied and environmental microbiology2013

Improved selection of internal transcribed spacer-specific primers enables quantitative, ultra-high-throughput profiling of fungal communities.

Nicholas A Bokulich, David A Mills

Abstract read
In one paragraph

Article in Applied and environmental microbiology, 2013. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 223 papers, 1 of them a synthesis that pooled it.

0numbers the graph read from it
0cells of the map it votes in
223citing papers in PubMed, 1 pooled it
–field-weighted citation impact
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

223 citing papers in PubMed, 1 synthesis or guideline pooled it.

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163 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

2 authors.

Nicholas A BokulichDepartment of Viticulture and Enology, Department of Food Science and Technology, and Foods for Health Institute, University of California, Davis, California, USA.
David A Mills

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Ultra-high-throughput sequencing (HTS) of fungal communities has been restricted by short read lengths and primer amplification bias, slowing the adoption of newer sequencing technologies to fungal community profiling. To address these issues, we evaluated the performance of several common internal transcribed spacer (ITS) primers and designed a novel primer set and work flow for simultaneous quantification and species-level interrogation of fungal consortia. Primer comparison and validation were predicted in silico and by sequencing a "mock community" of mixed yeast species to explore the challenges of amplicon length and amplification bias for reconstructing defined yeast community structures. The amplicon size and distribution of this primer set are smaller than for all preexisting ITS primer sets, maximizing sequencing coverage of hypervariable ITS domains by very-short-amplicon, high-throughput sequencing platforms. This feature also enables the optional integration of quantitative PCR (qPCR) directly into the HTS preparatory work flow by substituting qPCR with these primers for standard PCR, yielding quantification of individual community members. The complete work flow described here, utilizing any of the qualified primer sets evaluated, can rapidly profile mixed fungal communities and capably reconstructed well-characterized beer and wine fermentation fungal communities.

Indexed as

DNA, IntergenicHigh-Throughput Nucleotide SequencingDNA, FungalDNA PrimersFungiNucleic Acid Amplification TechniquesPolymerase Chain ReactionSequence Analysis, DNADNA, FungalDNA, IntergenicDNA Primers

Identifiers

PMID23377949
PMCPMC3623200

What OpenQuestion holds

Textmetadata
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.