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A Brief Review of Earth Microbiomes and Applications
Published in Nwadiuto (Diuto) Esiobu, James Chukwuma Ogbonna, Charles Oluwaseun Adetunji, Olawole O. Obembe, Ifeoma Maureen Ezeonu, Abdulrazak B. Ibrahim, Benjamin Ewa Ubi, Microbiomes and Emerging Applications, 2022
Toochukwu E. Ogbulie, Nwadiuto (Diuto) Esiobu, Ifeoma Enweani-Nwokelo
For earth microbiome project, identity of species can be retrieved at 97% lower limit from clustered OTUs using the UCLUST algorithm. Novel sequences discovered in certain situation and which cannot be mapped to existing taxonomy can deduce its taxa from the new sequences and collections of closely related known sequences by creating a phylogenetic tree. Alpha and beta diversity analysis in this case can be done using varying phylogenetic software packages such as Phyloseq package, PHYLIP, phyloT, etc. To obtain comparative gene families, functional prediction profiling of microbiome from the earth using 16S rRNA datasets is carried out using the PICRUSt and KEGG (Kyoto Encyclopaedia of Genes and Genomes) software.
Microbial community dynamics during anaerobic co-digestion of corn stover and swine manure at different solid content, carbon to nitrogen ratio and effluent volumetric percentages
Published in Journal of Environmental Science and Health, Part A, 2020
Gail Joseph, Bo Zhang, Scott H. Harrison, Joseph L. Graves, Misty D. Thomas, Renuka Panchagavi, Jude Akamu J. Ewunkem, Lijun Wang
At the end of the sequencing, the metadata mapping file was available in the FASTQ file format. The sequencing data were processed and analyzed using bioinformatics pipeline prepared in the Quantitative Insights into Microbial Ecology (QIIME and QIIME 2) software package.[32] The pipeline involved steps such as joining of the forward and reverse reads, checking mapping files for errors, demultiplexing and quality filtering sequences, and operational taxonomic units (OTUs) picking based on 97% sequence similarity. Operational Taxonomic Unit (OUT) picking was carried out by searching reads against the Greengenes database.[33] OTU picking strategy used was closed reference. Bray–Curtis index was used as a metric for Beta-diversity. The alignment was carried out using the UCLUST algorithm. The Greengenes reference taxonomy that was used for the analysis was the August 2013 release of gg_13_8_99 and contained 202,421 bacterial and archaeal sequences. The archaeal and bacterial taxonomy was assigned from phylum to genus level.
Removal of toluene vapor in the absence and presence of a quorum-sensing molecule in a biotrickling filter and microbial composition shift
Published in Journal of Environmental Science and Health, Part A, 2020
Chih-Yu Chen, Guey-Horng Wang, Cheng-Ta Tsai, Teh-Hua Tsai, Ying-Chien Chung
Cell-laden GAC was collected from the sp2 of the BTF. Cell lysis, DNA extraction, polymerase chain reaction amplification, and 454 pyrosequencing were performed based on the processes described in Wu et al. (2017).[17] DNA was extracted using a FastDNA® SPIN Kit (MP Biomedicals, USA) according to the manufacturer protocol. The PCR was performed to amplify the V3-V4 region of 16S rRNA by using the following procedure: 95 °C for 10 min, 30 cycles at 94 °C for 5 min, 55 °C for 30 s, 72 °C for 30 s, and a final extension at 72 °C for 10 min. All partial 16S rRNA gene sequences were preprocessed using the Illumina MiSeq platform based on the methods described in Wu et al. (2017).[17] A raw sequence analysis was quality-filtered using the QIIME software package. The processed sequences were clustered into operational taxonomic units (OTUs), which had a minimum sequence similarity of 97% with the UCLUST algorithm. Representative OTUs were selected based on the most abundant sequences, and a taxonomic assignment was conducted using a ribosomal database project classifier.
Microbial consortia adaptation to substrate changes in anaerobic digestion
Published in Preparative Biochemistry & Biotechnology, 2022
Priyanka S. Dargode, Pooja P. More, Suhas S. Gore, Bhupal R. Asodekar, Manju B. Sharma, Arvind M. Lali
The raw sequence reads were filtered first to remove artifacts made during the PCR process using the Usearch61 algorithm. Further flashed/stitched sequences were used for Operational Taxonomic Unit (OTU) pick. Similar sequences were clustered, i.e., sequences coming from the same genus, together into one representative taxonomic unit called OTU. The basis of this sequence clustering was a minimum of 97% sequence similarity as implemented through the UCLUST algorithm. In the next step, a representative sequence for each of these OTUs was picked and taxonomic names to these sequences at 90% sequence similarity were assigned using the UCLUST algorithm.