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      Bacterial community structure of the sunflower ( Helianthus annuus) endosphere

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      Plant Signaling & Behavior
      Taylor & Francis
      Agricultural sustainability, bacterial diversity, culture-independent techniques, amplicon sequencing

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          ABSTRACT

          Agrochemical applications on farmland aim to enhance crop yield; however, the consequence of biodiversity loss has caused a reduction in ecological functions. The positive endosphere interactions and crop rotation systems may function in restoring a stable ecosystem. Employing culture-independent techniques will help access the total bacteria community in the sunflower endosphere. Limited information is available on the bacteria diversity in sunflower plants cultivated under different agricultural practices. Hence, this study was designed to investigate the endophytic bacterial community structure of sunflower at the growing stage. Plant root and stem samples were sourced from two locations (Itsoseng and Lichtenburg), for DNA extraction and sequenced on the Illumina Miseq platform. The sequence dataset was analyzed using online bioinformatics tools. Saccharibacteria and Acidobacteria were dominant in plant roots, while the stem is dominated by Proteobacteria, Bacteriodetes, and Gemmatimonadetes across the sites. Bacterial genera, Acidovorax, Flavobacterium, Hydrogenophaga, and Burkholderia-Paraburkhoderia were found dominant in the root, while the stem is dominated by Streptomyces. The diverse bacterial community structure at phyla and class levels were significantly different in plant organs across the sites. The influence of soil physical and chemical parameters analyzed was observed to induce bacterial distribution across the sites. This study provides information on the dominant bacteria community structure in sunflowers at the growing stage and their predictive functions, which suggest their future exploration as bioinoculants for improved agricultural yields.

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          Most cited references67

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          QIIME allows analysis of high-throughput community sequencing data.

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            Naive Bayesian classifier for rapid assignment of rRNA sequences into the new bacterial taxonomy.

            The Ribosomal Database Project (RDP) Classifier, a naïve Bayesian classifier, can rapidly and accurately classify bacterial 16S rRNA sequences into the new higher-order taxonomy proposed in Bergey's Taxonomic Outline of the Prokaryotes (2nd ed., release 5.0, Springer-Verlag, New York, NY, 2004). It provides taxonomic assignments from domain to genus, with confidence estimates for each assignment. The majority of classifications (98%) were of high estimated confidence (> or = 95%) and high accuracy (98%). In addition to being tested with the corpus of 5,014 type strain sequences from Bergey's outline, the RDP Classifier was tested with a corpus of 23,095 rRNA sequences as assigned by the NCBI into their alternative higher-order taxonomy. The results from leave-one-out testing on both corpora show that the overall accuracies at all levels of confidence for near-full-length and 400-base segments were 89% or above down to the genus level, and the majority of the classification errors appear to be due to anomalies in the current taxonomies. For shorter rRNA segments, such as those that might be generated by pyrosequencing, the error rate varied greatly over the length of the 16S rRNA gene, with segments around the V2 and V4 variable regions giving the lowest error rates. The RDP Classifier is suitable both for the analysis of single rRNA sequences and for the analysis of libraries of thousands of sequences. Another related tool, RDP Library Compare, was developed to facilitate microbial-community comparison based on 16S rRNA gene sequence libraries. It combines the RDP Classifier with a statistical test to flag taxa differentially represented between samples. The RDP Classifier and RDP Library Compare are available online at http://rdp.cme.msu.edu/.
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              UCHIME improves sensitivity and speed of chimera detection

              Motivation: Chimeric DNA sequences often form during polymerase chain reaction amplification, especially when sequencing single regions (e.g. 16S rRNA or fungal Internal Transcribed Spacer) to assess diversity or compare populations. Undetected chimeras may be misinterpreted as novel species, causing inflated estimates of diversity and spurious inferences of differences between populations. Detection and removal of chimeras is therefore of critical importance in such experiments. Results: We describe UCHIME, a new program that detects chimeric sequences with two or more segments. UCHIME either uses a database of chimera-free sequences or detects chimeras de novo by exploiting abundance data. UCHIME has better sensitivity than ChimeraSlayer (previously the most sensitive database method), especially with short, noisy sequences. In testing on artificial bacterial communities with known composition, UCHIME de novo sensitivity is shown to be comparable to Perseus. UCHIME is >100× faster than Perseus and >1000× faster than ChimeraSlayer. Contact: robert@drive5.com Availability: Source, binaries and data: http://drive5.com/uchime. Supplementary information: Supplementary data are available at Bioinformatics online.
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                Author and article information

                Journal
                Plant Signal Behav
                Plant Signal Behav
                Plant Signaling & Behavior
                Taylor & Francis
                1559-2316
                1559-2324
                30 September 2021
                2021
                30 September 2021
                : 16
                : 12
                : 1974217
                Affiliations
                [0001]Food Security and Safety Niche Area, Faculty of Natural and Agricultural Sciences, North-West University; , Mmabatho, South Africa
                Author notes
                CONTACT Olubukola Oluranti Babalola olubukola.babalola@ 123456nwu.ac.za Food Security and Safety Niche Area, Faculty of Natural and Agricultural Sciences, North-West University; , Private Bag X2046, Mmabatho 2735, South Africa
                Author information
                https://orcid.org/0000-0001-7707-8816
                https://orcid.org/0000-0002-3220-1873
                https://orcid.org/0000-0003-4344-1909
                Article
                1974217
                10.1080/15592324.2021.1974217
                9208795
                34590546
                2c1ba74c-aa98-400f-aa52-3d318f448d3c
                © 2021 The Author(s). Published with license by Taylor & Francis Group, LLC.

                This is an Open Access article distributed under the terms of the Creative Commons Attribution License ( http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

                History
                Page count
                Figures: 9, Tables: 4, References: 67, Pages: 13
                Categories
                Research Article
                Research Paper

                Plant science & Botany
                agricultural sustainability,bacterial diversity,culture-independent techniques,amplicon sequencing

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