4.1 Article Data Paper

Dataset of shotgun metagenomic evaluation of lettuce (Lactuta sativa L.) rhizosphere microbiome

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卷 48, 期 -, 页码 -

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DOI: 10.1016/j.dib.2023.109214

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Shotgun approach; Microbial community; Functional genes; Root microbiomes; SEED subsystem; Illumina; Lettuce metagenome; MG-RAST

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This study conducted a metagenomic evaluation of the lettuce rhizospheric soils in Talton, South Africa using the Illumina No-vaSeq 6000 system. The results showed that bacteria, fungi, and archaea were the main components of the microbial community in the lettuce rhizosphere, with bacteria being the predominant group.
Lettuce (Lactuca sativa L.) is an important vegetable grown and consumed across the world, including South Africa and its rhizosphere constitutes a dynamic community of root as-sociated microbes. Dataset of the microbial community pro-file of the lettuce rhizospheric soils obtained from Talton, Gauteng Province of South Africa was subjected to metage-nomic evaluation using the shotgun approach. The whole DNA isolated from the community was sequenced using No-vaSeq 60 0 0 system (Illumina). The raw data obtained con-sists of 129,063,513.33 sequences with an average length of 200 base pairs and 60.6% Guanine + Cytosine content. The metagenome data has been deposited to the National Cen-tre for Biotechnology Information SRA under the bioproject number PRJNA763048. The downstream analysis alongside taxonomical annotation carried out using an online server MG-RAST, showed the community analysis as being made up of archaea (0.95%), eukaryotes (1.36%), viruses (0.04%), while 97.65% of the sequences were classified as bacteria. A sum of 25 bacteria, 20 eukaryotic and 4 archaea phyla were identified. The predominant genera were Acinetobac-ter (4.85%), Pseudomonas (3.41%), Streptomyces (2.79%), Candi-datus solibacter (1.93%), Burkholderia (1.65%), Bradyrhizobium (1.51%) and Mycobacterium (1.31%). Annotation using Cluster of Orthologous Group (COG) showed 23.91% of the se-quenced data were for metabolic function, 33.08% for chemi-cal process and signaling while 6.42% were poorly character-ized. Furthermore, the subsystem annotation method showed that sequences were majorly associated with carbohydrates (12.86%), clustering-based subsystems (12.68%), and genes coding for amino acids and derivatives (10.04%), all of which could serve in growth promotion and plant management.(c) 2023 The Author(s). Published by Elsevier Inc. This is an open access article under the CC BY license ( http://creativecommons.org/licenses/by/4.0/ )

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