Severity: Warning
Message: file_get_contents(https://...@pubfacts.com&api_key=b8daa3ad693db53b1410957c26c9a51b4908&a=1): Failed to open stream: HTTP request failed! HTTP/1.1 429 Too Many Requests
Filename: helpers/my_audit_helper.php
Line Number: 176
Backtrace:
File: /var/www/html/application/helpers/my_audit_helper.php
Line: 176
Function: file_get_contents
File: /var/www/html/application/helpers/my_audit_helper.php
Line: 250
Function: simplexml_load_file_from_url
File: /var/www/html/application/helpers/my_audit_helper.php
Line: 3122
Function: getPubMedXML
File: /var/www/html/application/controllers/Detail.php
Line: 575
Function: pubMedSearch_Global
File: /var/www/html/application/controllers/Detail.php
Line: 489
Function: pubMedGetRelatedKeyword
File: /var/www/html/index.php
Line: 316
Function: require_once
Lettuce ( L.) is an important vegetable grown and consumed across the world, including South Africa and its rhizosphere constitutes a dynamic community of root associated microbes. Dataset of the microbial community profile of the lettuce rhizospheric soils obtained from Talton, Gauteng Province of South Africa was subjected to metagenomic evaluation using the shotgun approach. The whole DNA isolated from the community was sequenced using NovaSeq 6000 system (Illumina). The raw data obtained consists 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 Centre 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 (4.85%), (3.41%), (2.79%), (1.93%), (1.65%), (1.51%) and (1.31%). Annotation using Cluster of Orthologous Group (COG) showed 23.91% of the sequenced data were for metabolic function, 33.08% for chemical process and signaling while 6.42% were poorly characterized. 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.
Download full-text PDF |
Source |
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http://www.ncbi.nlm.nih.gov/pmc/articles/PMC10205420 | PMC |
http://dx.doi.org/10.1016/j.dib.2023.109214 | DOI Listing |
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