2017
DOI: 10.1007/s11356-017-0078-z
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Prokaryotic community profiling of local algae wastewaters using advanced 16S rRNA gene sequencing

Abstract: Algae biomass-fed wastewaters are a promising source of lipid and bioenergy manufacture, revealing substantial end-product investment returns. However, wastewaters would contain lytic pathogens carrying drug resistance detrimental to algae yield and environmental safety. This study was conducted to simultaneously decipher through high-throughput advanced Illumina 16S ribosomal RNA (rRNA) gene sequencing, the cultivable and uncultivable bacterial community profile found in a single sample that was directly reco… Show more

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Cited by 9 publications
(3 citation statements)
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“…The Pseudomonas species were prevalent in the influent wastewater sample however the abundance was largely reduced in the effluent sample (Table 3). This result was in accordance with our previous study with municipal WW (Limayem et al, 2018). Genus Acinetobacter shares same phylum, class and order as Pseudomonas spp.…”
Section: Discussionsupporting
confidence: 94%
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“…The Pseudomonas species were prevalent in the influent wastewater sample however the abundance was largely reduced in the effluent sample (Table 3). This result was in accordance with our previous study with municipal WW (Limayem et al, 2018). Genus Acinetobacter shares same phylum, class and order as Pseudomonas spp.…”
Section: Discussionsupporting
confidence: 94%
“…Typically, the conventional 16S rRNA gene sequencing for bacterial identification is a low resolution technique that have prevented comprehensive characterization and requires analysis of a substantial number of samples for isolating and culturing individual species (Ma et al, 2015). Moreover, this process demands intensive labor with significant capital and time investments (Limayem et al, 2018). Using a rapid and less laborious advanced multiplexing system, namely the high throughput Illumina 16S rRNA sequencing of the entire cultivable and uncultivable bacterial community can simultaneously be deciphered in one single sample (Limayem et al, 2018).…”
Section: Introductionmentioning
confidence: 99%
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