Evidence map›Paper›PMID 37504730›Full record

ArticleJournal of fungi (Basel, Switzerland)2023

A Custom Regional DNA Barcode Reference Library for Lichen-Forming Fungi of the Intermountain West, USA, Increases Successful Specimen Identification.

Michael Kerr, Steven D Leavitt

Open access · goldAbstract read
In one paragraph

Article in Journal of fungi (Basel, Switzerland), 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 3 papers.

0numbers the graph read from it
0cells of the map it votes in
3citing papers in PubMed
4.8field-weighted citation impact, top 6% 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

3 citing papers in PubMed, 8 citations in OpenAlex.

  1. Review
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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 at 1 institution in 1 country.

Michael KerrDepartment of Biology, Brigham Young University, Provo, UT 84602, USA.ORCID 0000-0001-9563-9373
Steven D LeavittM.L. Bean Life Science Museum and Department of Biology, Brigham Young University, Provo, UT 84602, USA.ORCID 0000-0002-5034-9724
Brigham Young University · US

Funding

Brigham Young University NA
6 · The paper itself

Abstract

DNA barcoding approaches provide powerful tools for characterizing fungal diversity. However, DNA barcoding is limited by poor representation of species-level diversity in fungal sequence databases. Can the development of custom, regionally focused DNA reference libraries improve species-level identification rates for lichen-forming fungi? To explore this question, we created a regional ITS database for lichen-forming fungi (LFF) in the Intermountain West of the United States. The custom database comprised over 4800 sequences and represented over 600 formally described and provisional species. Lichen communities were sampled at 11 sites throughout the Intermountain West, and LFF diversity was characterized using high-throughput ITS2 amplicon sequencing. We compared the species-level identification success rates from our bulk community samples using our regional ITS database and the widely used UNITE database. The custom regional database resulted in significantly higher species-level assignments (72.3%) of candidate species than the UNITE database (28.3-34.2%). Within each site, identification of candidate species ranged from 72.3-82.1% using the custom database; and 31.5-55.4% using the UNITE database. These results highlight that developing regional databases may accelerate a wide range of LFF research by improving our ability to characterize species-level diversity using DNA barcoding.

Indexed as

Illuminainternal transcribed spacer (ITS)metabarcodingOTUsspecies hypothesistaxonomic assignmentUNITE

Identifiers

PMID37504730
PMCPMC10381598
OpenAlexW4384131618

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.