Evidence map›Paper›PMID 27861494›Full record

ArticlePloS one2016

DNA Barcoding for Efficient Species- and Pathovar-Level Identification of the Quarantine Plant Pathogen Xanthomonas.

Qian Tian, Wenjun Zhao, Songyu Lu, Shuifang Zhu, Shidong Li

Abstract read
In one paragraph

Article in PloS one, 2016. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 4 papers.

0numbers the graph read from it
0cells of the map it votes in
4citing papers in PubMed
–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

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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

4 citing papers in PubMed.

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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

5 authors.

Qian TianInstitute of Plant Protection, Chinese Academy of Agricultural Sciences, Beijing, China.
Wenjun ZhaoInstitute of Plant Quarantine Research, Chinese Academy of Inspection and Quarantine, Beijing, China.
Songyu LuCollege of Plant Protection, Nanjing Agricultural University, Nanjing, China.
Shuifang ZhuInstitute of Plant Quarantine Research, Chinese Academy of Inspection and Quarantine, Beijing, China.
Shidong LiInstitute of Plant Protection, Chinese Academy of Agricultural Sciences, Beijing, China.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Genus Xanthomonas comprises many economically important plant pathogens that affect a wide range of hosts. Indeed, fourteen Xanthomonas species/pathovars have been regarded as official quarantine bacteria for imports in China. To date, however, a rapid and accurate method capable of identifying all of the quarantine species/pathovars has yet to be developed. In this study, we therefore evaluated the capacity of DNA barcoding as a digital identification method for discriminating quarantine species/pathovars of Xanthomonas. For these analyses, 327 isolates, representing 45 Xanthomonas species/pathovars, as well as five additional species/pathovars from GenBank (50 species/pathovars total), were utilized to test the efficacy of four DNA barcode candidate genes (16S rRNA gene, cpn60, gyrB, and avrBs2). Of these candidate genes, cpn60 displayed the highest rate of PCR amplification and sequencing success. The tree-building (Neighbor-joining), 'best close match', and barcode gap methods were subsequently employed to assess the species- and pathovar-level resolution of each gene. Notably, all isolates of each quarantine species/pathovars formed a monophyletic group in the neighbor-joining tree constructed using the cpn60 sequences. Moreover, cpn60 also demonstrated the most satisfactory results in both barcoding gap analysis and the 'best close match' test. Thus, compared with the other markers tested, cpn60 proved to be a powerful DNA barcode, providing a reliable and effective means for the species- and pathovar-level identification of the quarantine plant pathogen Xanthomonas.

Indexed as

DNA Barcoding, TaxonomicEvolution, MolecularGenes, BacterialGenetic MarkersPhylogenyPlantsRNA, Ribosomal, 16SSequence Analysis, DNAXanthomonasGenetic MarkersRNA, Ribosomal, 16S

Identifiers

PMID27861494
PMCPMC5115671

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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.