Diversity and functional annotation of microorganisms in anaerobic chamber treating nitrate-rich wastewater.

The vertical flow constructed wetlands (VFCW) for the treatment of domestic wastewater has become a conventional and cost effective treatment system with one of the major disadvantage of elevated nitrate concentrations. The present study makes an effort in providing a new design of anaerobic denitrification unit termed as anaerobic chamber (AC) which was introduced after two-stage VFCW to remove nitrates from the treated wastewater (WW). The AC provided all the essential conditions of effective denitrification such as anaerobic environment with enough carbon and nitrogen source. To understand the pollutant removal mechanism in AC, microbial diversity and functional annotation was studied by metagenomic analysis of sequences obtained from biofilm formed in AC. The efficiency of AC was measured with respect to physicochemical wastewater quality parameters. The removal efficiencies were 88, 65, 43 and 27% for total nitrogen, nitrate (NO3), ammoniacal-nitrogen (NH4) and ortho-phosphate respectively. The microbial flora was much more diverse and unique pertaining to anaerobic microbes in AC compared to WW with total of 954 and 1191 genuses respectively with minimum abundance of 10 hits. The metagenomes exhibited 188% more Archaea in the AC than WW where Crenarchaeota, Euryarchaeota, Korarchaeota, Nanoarchaeota and Thaumarchaeota were major phyla with 60 genuses. The nitrogen metabolism was reported in terms of assimilatory nitrate reductase. As the class, Proteobacteria, Actinobacteria were prominent in WW, whereas Proteobacteria, Chloroflexi in AC were abundant. From functional annotation of sequences, the microbial flora in AC has the potential of removal of pollutants present in the form of carbon, nitrogen, and phosphorus.

Microbial Composition and Genes for Key Metabolic Attributes in the Gut Digesta of Sea Urchins Lytechinus variegatus and Strongylocentrotus purpuratus Using Shotgun Metagenomics.

This paper describes the microbial community composition and genes for key metabolic genes, particularly the nitrogen fixation of the mucous-enveloped gut digesta of green (Lytechinus variegatus) and purple (Strongylocentrotus purpuratus) sea urchins by using the shotgun metagenomics approach. Both green and purple urchins showed high relative abundances of Gammaproteobacteria at 30% and 60%, respectively. However, Alphaproteobacteria in the green urchins had higher relative abundances (20%) than the purple urchins (2%). At the genus level, Vibrio was dominant in both green (~9%) and purple (~10%) urchins, whereas Psychromonas was prevalent only in purple urchins (~24%). An enrichment of Roseobacter and Ruegeria was found in the green urchins, whereas purple urchins revealed a higher abundance of Shewanella, Photobacterium, and Bacteroides (q-value < 0.01). Analysis of key metabolic genes at the KEGG-Level-2 categories revealed genes for amino acids (~20%), nucleotides (~5%), cofactors and vitamins (~6%), energy (~5%), carbohydrates (~13%) metabolisms, and an abundance of genes for assimilatory nitrogen reduction pathway in both urchins. Overall, the results from this study revealed the differences in the microbial community and genes designated for the metabolic processes in the nutrient-rich sea urchin gut digesta, suggesting their likely importance to the host and their environment.

A comparative study of metatranscriptomic assessment methods to characterize Microcystis blooms.

Harmful algal blooms are increasing in duration and severity globally, resulting in increased research interest. The use of genetic sequencing technologies has provided a wealth of opportunity to advance knowledge, but also poses a risk to that knowledge if handled incorrectly. The vast numbers of sequence processing tools and protocols provide a method to test nearly every hypothesis, but each method has inherent strengths and weaknesses. Here, we tested six methods to classify and quantify metatranscriptomic activity from a harmful algal bloom dominated by Microcystis spp. Three online tools were evaluated (Kaiju, MG-RAST, and GhostKOALA) in addition to three local tools that included a command line BLASTx approach, recruitment of reads to individual Microcystis genomes, and recruitment to a combined Microcystis composite genome generated from sequenced isolates with complete, closed genomes. Based on the analysis of each tool presented in this study, two recommendations are made that are dependent on the hypothesis to be tested. For researchers only interested in the function and physiology of Microcystis spp., read recruitments to the composite genome, referred to as "Frankenstein's Microcystis", provided the highest total estimates of transcript expression. However, for researchers interested in the entire bloom microbiome, the online GhostKOALA annotation tool, followed by subsequent read recruitments, provided functional and taxonomic characterization, in addition to transcript expression estimates. This study highlights the critical need for careful evaluation of methods before data analysis.

Core of the saliva microbiome: an analysis of the MG-RAST data.

BACKGROUND: Oral microbiota is considered as the second most complex in the human body and its dysbiosis can be responsible for oral diseases. Interactions between the microorganism communities and the host allow establishing the microbiological proles. Identifying the core microbiome is essential to predicting diseases and changes in environmental behavior from microorganisms. METHODS: Projects containing the term "SALIVA", deposited between 2014 and 2019 were recovered on the MG-RAST portal. Quality (Failed), taxonomic prediction (Unknown and Predicted), species richness (Rarefaction), and species diversity (Alpha) were analyzed according to sequencing approaches (Amplicon sequencing and Shotgun metagenomics). All data were checked for normality and homoscedasticity. Metagenomic projects were compared using the Mann-Whitney U test and Spearman's correlation. Microbiome cores were inferred by Principal Component Analysis. For all statistical tests, p < 0.05 was used. RESULTS: The study was performed with 3 projects, involving 245 Amplicon and 164 Shotgun metagenome datasets. All comparisons of variables, according to the type of sequencing, showed significant differences, except for the Predicted. In Shotgun metagenomics datasets the highest correlation was between Rarefaction and Failed (r = - 0.78) and the lowest between Alpha and Unknown (r = - 0.12). In Amplicon sequencing datasets, the variables Rarefaction and Unknown (r = 0.63) had the highest correlation and the lowest was between Alpha and Predicted (r = - 0.03). Shotgun metagenomics datasets showed a greater number of genera than Amplicon. Propionibacterium, Lactobacillus, and Prevotella were the most representative genera in Amplicon sequencing. In Shotgun metagenomics, the most representative genera were Escherichia, Chitinophaga, and Acinetobacter. CONCLUSIONS: Core of the salivary microbiome and genera diversity are dependent on the sequencing approaches. Available data suggest that Shotgun metagenomics and Amplicon sequencing have similar sensitivities to detect the taxonomic level investigated, although Shotgun metagenomics allows a deeper analysis of the microorganism diversity. Microbiome studies must consider characteristics and limitations of the sequencing approaches. Were identified 20 genera in the core of saliva microbiome, regardless of the health condition of the host. Some bacteria of the core need further study to better understand their role in the oral cavity.

Understanding the alteration in rumen microbiome and CAZymes profile with diet and host through comparative metagenomic approach.

Rumen microbial community harbors a distinct genetic reservoir of potent carbohydrate-active enzymes (CAZyme) that functions efficiently for the deconstruction of plant biomass. Based on this premise, metagenomics approach was applied to characterize the rumen microbial community and identify carbohydrate-active genes of Bos taurus (cow) and Bubalus bubalis (buffalo) fed on green or dry roughage. Metadata was generated from the samples: green roughage-fed cow (NDC_GR), buffalo (NDB_GR) and dry roughage-fed cow (NDC_DR), buffalo (NDB_DR). Phylogenetic analysis revealed the dominance of Bacteroidetes, Firmicutes, Proteobacteria, Actinobacteria and Fibrobacter in all the four samples, covering 90-96% of the total bacterial population. On finer resolution, higher abundance of bacterial genera Fibrobacter, Bacteroides, Clostridium, Prevotella and Ruminococcus involved in plant biomass hydrolysis was observed in NDB_DR. Functional annotation using dbCAN annotation algorithm identified 28.13%, 8.08% 10.93% and 12.53% of the total contigs as putatively carbohydrate-active against NDC_GR, NDB_GR, NDC_DR and NDB_DR, respectively. Additional profiling of CAZymes revealed an over representation and diversity of putative glycoside hydrolases (GHs) in the animals fed on dry roughage with substantial enrichments of genes encoding GHs from families GH2, GH3, GH13 and GH43. GHs of families GH45, GH12, GH113, GH128, GH54 and GH27 were observed exclusively in NDB_DR metagenome. A higher abundance of cellulases, hemicellulases, debranching and oligosaccharide hydrolyzing enzymes was revealed in NDB_DR metagenome. Accordingly, it can be concluded that buffalo rumen microbiome are more efficient in plant biomass hydrolysis. The present study provides a deep understanding of the shifts in microbial community and plant polysaccharide deconstructing capabilities of rumen microbiome in response to changes in the feed type and host animal. Activity-specific microbial consortia procured from these animals can be used further for efficient plant biomass hydrolysis. The study also establishes the utility of rumen microbiome as a unique resource for mining diverse lignocellulolytic enzymes.

What we can see from very small size sample of metagenomic sequences.

BACKGROUND: Since the analysis of a large number of metagenomic sequences costs heavy computing resources and takes long time, we examined a selected small part of metagenomic sequences as "sample"s of the entire full sequences, both for a mock community and for 10 different existing metagenomics case studies. A mock community with 10 bacterial strains was prepared, and their mixed genome were sequenced by Hiseq. The hits of BLAST search for reference genome of each strain were counted. Each of 176 different small parts selected from these sequences were also searched by BLAST and their hits were also counted, in order to compare them to the original search results from the full sequences. We also prepared small parts of sequences which were selected from 10 publicly downloadable research data of MG-RAST service, and analyzed these samples with MG-RAST. RESULTS: Both the BLAST search tests of the mock community and the results from the publicly downloadable researches of MG-RAST show that sampling an extremely small part from sequence data is useful to estimate brief taxonomic information of the original metagenomic sequences. For 9 cases out of 10, the most annotated classes from the MG-RAST analyses of the selected partial sample sequences are the same as the ones from the originals. CONCLUSIONS: When a researcher wants to estimate brief information of a metagenome's taxonomic distribution with less computing resources and within shorter time, the researcher can analyze a selected small part of metagenomic sequences. With this approach, we can also build a strategy to monitor metagenome samples of wider geographic area, more frequently.

The effect of a high-roughage diet on the metabolism of aromatic compounds by rumen microbes: a metagenomic study using Mehsani buffalo (Bubalus bubalis).

In developing countries, livestock are often fed a high-lignin, low-nutrient diet that is rich in aromatic compounds. It is therefore important to understand the structure of the microbial community responsible for the metabolism of these substances. A metagenomic analysis was therefore carried out to assess the microbial communities associated with the liquid and solid fractions of rumen biomaterial from domestic Mehsani buffalo (Bubalus bubalis) fed with varying proportions of roughage. The experimental design consisted of three feeding regimes (50, 75 and 100 % roughage) and two roughage types (green and dry). Genes associated with aromatic compound degradation were assessed via high-throughput DNA sequencing. A total of 3914.94 Mb data were generated from all treatment groups. Genes coding for functional responses associated with aromatic compound metabolism were more prevalent in the liquid fraction of rumen samples than solid fractions. Statistically significant differences (p < 0.05) were also observed between treatment groups. These differences were dependent on the proportion of roughage fed to the animal, with the type of roughage having little effect. The genes present in the highest abundance in all treatment groups were those related to aromatic compound catabolism. At the phylum level, Bacteroidetes were dominant in all treatments closely followed by the Firmicutes. This study demonstrates the use of feed type to selectively enrich microbial communities capable of metabolizing aromatic compounds in the rumen of domestic buffalo. The results may help to improve nutrient utilization efficiency in livestock and are thus of interest to farming industries, particularly in developing countries, worldwide.

Dataset of amplicon metagenomic assessment of barley rhizosphere bacteria under different fertilization regimes.

The metagenomic dataset profiled in this research is built on bacterial 16S rRNA gene amplicon of DNA mined from barley rhizosphere under chemical (CB) and organic (OB) fertilization. Amplicon-based sequencing was prepared by the Illumina platform, and the raw sequence dataset was examined using Metagenomic Rast Server (MG-RAST). The metagenome comprised sixteen samples that include CB1 (494,583 bp), CB2 (586,532 bp), CB3 (706,685 bp), CB4 (574,606 bp), CB5 (395,460 bp), CB6 (520,822 bp), CB7 (511,729 bp), CB8 (548,074 bp), OB1 (642,794 bp), OB2 (513,767 bp), OB3 (461,293 bp), OB4 (498,241 bp), OB5 (689,497 bp), OB6 (423,436 bp), OB7 (478,657 bp) and OB8 (279,186 bp). Information from the metagenome sequences is accessible under the bioproject numbers PRJNA827679 (CB1), PRJNA827686 (CB2), PRJNA827693 (CB3), PRJNA827699 (CB4), PRJNA827706 (CB5), PRJNA827761 (CB6), PRJNA827780 (CB7), PRJNA827786 (CB8), PRJNA826806 (OB1), PRJNA826824 (OB2), PRJNA826834 (OB3), PRJNA826841 (OB4), PRJNA826853 (OB5), PRJNA827254 (OB6), PRJNA827256 (OB7), and PRJNA827257 (OB8) at NCBI. Actinobacteria dominated the soil samples at the phylum level.

Exploring bacterial diversity in Arctic fjord sediments: a 16S rRNA-based metabarcoding portrait.

The frosty polar environment houses diverse habitats mostly driven by psychrophilic and psychrotolerant microbes. Along with traditional cultivation methods, next-generation sequencing technologies have become common for exploring microbial communities from various extreme environments. Investigations on glaciers, ice sheets, ponds, lakes, etc. have revealed the existence of numerous microorganisms while details of microbial communities in the Arctic fjords remain incomplete. The current study focuses on understanding the bacterial diversity in two Arctic fjord sediments employing the 16S rRNA gene metabarcoding and its comparison with previous studies from various Arctic habitats. The study revealed that Proteobacteria was the dominant phylum from both the fjord samples followed by Bacteroidetes, Planctomycetes, Firmicutes, Actinobacteria, Cyanobacteria, Chloroflexi and Chlamydiae. A significant proportion of unclassified reads derived from bacteria was also detected. Psychrobacter, Pseudomonas, Acinetobacter, Aeromonas, Photobacterium, Flavobacterium, Gramella and Shewanella were the major genera in both the fjord sediments. The above findings were confirmed by the comparative analysis of fjord metadata with the previously reported (secondary metadata) Arctic samples. This study demonstrated the potential of 16S rRNA gene metabarcoding in resolving bacterial composition and diversity thereby providing new in situ insights into Arctic fjord systems.

Metagenomic analysis of soybean endosphere microbiome to reveal signatures of microbes for health and disease.

BACKGROUND: Soil metagenomics is a cultivation-independent molecular strategy for investigating and exploiting the diversity of soil microbial communities. Soil microbial diversity is essential because it is critical to sustaining soil health for agricultural productivity and protection against harmful organisms. This study aimed to perform a metagenomic analysis of the soybean endosphere (all microbial communities found in plant leaves) to reveal signatures of microbes for health and disease. RESULTS: The dataset is based on the National Center for Biotechnology Information (NCBI) Sequence Read Archive (SRA) release "microbial diversity in soybean". The quality control process rejected 21 of the evaluated sequences (0.03% of the total sequences). Dereplication determined that 68,994 sequences were artificial duplicate readings, and removed them from consideration. Ribosomal Ribonucleic acid (RNA) genes were present in 72,747 sequences that successfully passed quality control (QC). Finally, we found that hierarchical classification for taxonomic assignment was conducted using MG-RAST, and the considered dataset of the metagenome domain of bacteria (99.68%) dominated the other groups. In Eukaryotes (0.31%) and unclassified sequence 2 (0.00%) in the taxonomic classification of bacteria in the genus group, Streptomyces, Chryseobacterium, Ppaenibacillus, Bacillus, and Mitsuaria were found. We also found some biological pathways, such as CMP-KDO biosynthesis II (from D-arabinose 5-phosphate), tricarboxylic acid cycle (TCA) cycle (plant), citrate cycle (TCA cycle), fatty acid biosynthesis, and glyoxylate and dicarboxylate metabolism. Gene prediction uncovered 1,180 sequences, 15,172 of which included gene products, with the shortest sequence being 131 bases and maximum length 3829 base pairs. The gene list was additionally annotated using Integrated Microbial Genomes and Microbiomes. The annotation process yielded a total of 240 genes found in 177 bacterial strains. These gene products were found in the genome of strain 7598. Large volumes of data are generated using modern sequencing technology to sample all genes in all species present in a given complex sample. CONCLUSIONS: These data revealed that it is a rich source of potential biomarkers for soybean plants. The results of this study will help us to understand the role of the endosphere microbiome in plant health and identify the microbial signatures of health and disease. The MG-RAST is a public resource for the automated phylogenetic and functional study of metagenomes. This is a powerful tool for investigating the diversity and function of microbial communities.

Corrigendum to "Shotgun metagenomics dataset of Striga hermonthica-infested maize (Zea mays L.) rhizospheric soil microbiome" [Data in Brief, volume 48 (2023) 1-5/109132].

[This corrects the article DOI: 10.1016/j.dib.2023.109132.].

Shotgun metagenomics dataset of Striga hermonthica-infested maize (Zea mays L.) rhizospheric soil microbiome.

This dataset includes shotgun metagenomics sequencing of the rhizosphere microbiome of maize infested with Striga hermonthica from Mbuzini, South Africa, and Eruwa, Nigeria. The sequences were used for microbial taxonomic classification and functional categories in the infested maize rhizosphere. High throughput sequencing of the complete microbial community's DNA was performed using the Illumina NovaSeq 6000 technology. The average base pair count of the sequences were 5,353,206 bp with G+C content of 67%. The raw sequence data used for analysis is available in NCBI under the BioProject accession numbers PRJNA888840 and PRJNA889583. The taxonomic analysis was performed using Metagenomic Rapid Annotations using Subsystems Technology (MG-RAST). Bacteria had the highest taxonomic representation (98.8%), followed by eukaryotes (0.56%), and archaea (0.45%). This metagenome dataset provide valuable information on microbial communities associated with Striga-infested maize rhizosphere and their functionality. It can also be used for further studies on application of microbial resources for sustainable crop production in this region.

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

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 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 Acinetobacter (4.85%), Pseudomonas (3.41%), Streptomyces (2.79%), Candidatus 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 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.

Application of High-Throughput Sequencing (HTS) to Enhance the Well-Being of an Endangered Species (Malayan Tapir): Characterization of Gut Microbiome Using MG-RAST.

The Tapirus indicus, also known as Malayan tapir, has been listed as a rapidly declining animal species in the past decades, along with being declared and categorized as an endangered species by the International Union for Conservation of Nature (IUCN) 2016. This tapir species is geographically distributed across several countries in Southeast Asia such as Peninsular Malaysia, Indonesia (Sumatra), South Thailand, and Myanmar. Amongst these countries, the Peninsula Malaysia forest is recorded to contain the highest number of Malayan tapir population. Unfortunately, in the past decades, the population of Malayan tapirs has declined swiftly due to serious deforestation, habitat fragmentation, and heavy vehicle accidents during road crossings at forest routes. Concerned by this predicament, the Department of Wildlife and National Parks (DWNP) Peninsular Malaysia collaborated with a few local universities to conduct various studies aimed at increasing the population number of tapirs in Malaysia. Several studies were conducted with the aim of enhancing the well-being of tapirs in captivity. Veterinarians face problems when it comes to selecting healthy and suitable tapirs for breeding programs at conservation centers. Conventional molecular methods using high-throughput sequencing provides a solution in determining the health condition of Malayan tapirs using the Next-Generation Sequencing (NGS) technology. Unaware by most, gut microbiome plays an important role in determining the health condition of an organism by various aspects: (1) digestion control; (2) benefiting the immune system; and (3) playing a role as a "second brain." Commensal gut bacterial communities (microbiomes) are predicted to influence organism health and disease. Imbalance of unhealthy and healthy microbes in the gut may contribute to weight gain, high blood sugar, high cholesterol, and other disorders. In infancy, neonatal gut microbiomes are colonized with maternal and environmental flora, and mature toward a stable composition in two to three years. Interactions between the microorganism communities and the host allow for the establishment of microbiological roles. Identifying the core microbiome(s) are essential in the prediction of diseases and changes in environmental behavior of microorganisms. The dataset of 16S rRNA amplicon sequencing of Malayan tapir was deposited in the MG-RAST portal. Parameters such as quality control, taxonomic prediction (unknown and predicted), diversity (rarefaction), and diversity (alpha) were analyzed using sequencing approaches (Amplicon sequencing). Comparisons of parameters, according to the type of sequencing, showed significant differences, except for the prediction variable. In the Amplicon sequencing datasets, the parameters Rarefaction and Unknown had the highest correlation, while Alpha and Predicted had the lowest. Firmicutes, Bacteroidetes, Proteobacteria, Bacilli, and Bacteroidia were the most representative genera in Malayan tapir amplicon sequences, which indicated that most of the tapirs were healthy. However, continuous assessment to maintain the well-being of tapir for long term is still required. This chapter focuses on the introduction of 16S rRNA amplicon metagenomics in analyzing Malayan tapir gut microbiome dataset.

Datasets of shotgun metagenomics evaluation of rumen microbiota of South African mutton merino sheep.

The rumen microbial consortium plays a crucial role in the production performance and health of the ruminant animal. They are responsible for breaking down complex plant materials such as cellulose and hemicellulose to release usable energy by the host animal. Rumen microbial diversity manipulation through dietary strategies can be used to achieve several goals such as improved feed efficiency, reduced environmental impact or better utilization of low-quality forages. The dataset, deposited in the National Centre for Biotechnology Information SRA with project number PRJNA775821, comprises sequenced DNA extracted from the rumen content of 16 South African Merino sheep supplemented with different plant extracts. Illumina HiSeq™ 6000 technology was utilised to generate a total of approximately 46.7 Gb in raw nucleotide data. The data consists of 700,318,582 sequences, each with an average length of 184 base pairs. Taxonomic annotation conducted through the MG-RAST server showed the dominant phylum averages are Bacteroidetes (51 %) and Firmicutes (28 %), while Euryarchaeota, Actinobacteria, and Proteobacteria each account for approximately 3 % of the population. This dataset also enables us to identify and profile all expressed genes related to metabolic and chemical processes. The dataset is a valuable tool, offering insights that can lead to enhanced sustainability, profitability and reduced environmental impact within the context of ruminant production process.

Taxonomic annotation of 16S rRNA sequences of pig intestinal samples using MG-RAST and QIIME2 generated different microbiota compositions.

Environmental microbiome studies rely on fast and accurate bioinformatics tools to characterize the taxonomic composition of samples based on the 16S rRNA gene. MetaGenome Rapid Annotation using Subsystem Technology (MG-RAST) and Quantitative Insights Into Microbial Ecology 2 (QIIME2) are two of the most popular tools available to perform this task. Their underlying algorithms differ in many aspects, and therefore the comparison of the pipelines provides insights into their best use and interpretation of the outcomes. Both of these bioinformatics tools are based on several specialized algorithms pipelined together, but whereas MG-RAST is a user-friendly webserver that clusters rRNA sequences based on their similarity to create Operational Taxonomic Units (OTU), QIIME2 employs DADA2 in the construction of Amplicon Sequence Variants (ASV) by applying an error model that considers the abundance of each sequence and its similarity to other sequences. Taxonomic compositions obtained from the analyses of amplicon sequences of DNA from swine intestinal gut and faecal microbiota samples using MG-RAST and QIIME2 were compared at domain-, phylum-, family- and genus-levels in terms of richness, relative abundance and diversity. We found significant differences between the microbiota profiles obtained from each pipeline. At domain level, bacteria were relatively more abundant using QIIME2 than MG-RAST; at phylum level, seven taxa were identified exclusively by QIIME2; at family level, samples processed in QIIME2 showed higher evenness and richness (assessed by Shannon and Simpson indices). The genus-level compositions obtained from each pipeline were used in partial least squares-discriminant analyses (PLS-DA) to discriminate between sample collection sites (caecum, colon and faeces). The results showed that different genera were found to be significant for the models, based on the Variable Importance in Projection, e.g. when using sequencing data processed by MG-RAST, the three most important genera were Acetitomaculum, Ruminococcus and Methanosphaera, whereas when data was processed using QIIME2, these were Candidatus Methanomethylophilus, Sphaerochaeta and Anaerorhabdus. Furthermore, the application of differential filtering procedures before the PLS-DA revealed higher accuracy when using non-restricted datasets obtained from MG-RAST, whereas datasets obtained from QIIME2 resulted in more accurate discrimination of sample collection sites after removing genera with low relative abundances (<1%) from the datasets. Our results highlight the differences in taxonomic compositions of samples obtained from the two separate pipelines, while underlining the impact on downstream analyses, such as biomarkers identification.

Shotgun metagenomic data of root endophytic microbiome of maize (Zea mays L.).

This dataset represents the root endophytic microbial community profile of maize (Zea mays L.), one of the largest food crops in South Africa, using a shotgun metagenomic approach. To the best of our understanding, this is the first account showcasing the endophytic microbial diversity in maize plants via the shotgun metagenomics approach. High throughput sequencing of the whole DNA from the community was carried out using NovaSeq 6000 system (Illumina). The data obtained consists of 10,915,268 sequences accounting for 261,906,948 bps with an average length of 153 base pairs and 43% Guanine+Cytosine content. The metagenome data can be accessed at the National Centre for Biotechnology Information SRA registered with the accession number PRJNA607664. Community analysis was done using an online server called MG-RAST, which showed that 0.12% of the sequences were archaeal associated, eukaryotes were 15.06%, while 84.77% were classified as bacteria. A sum of 28 bacterial, 22 eukaryotic and 4 archaeal phyla were identified. The predominant genera were Bacillus (16%), Chitinophaga (12%), Flavobacterium (4%), Chryseobacterium (4%), Paenibacillus (4%), Pedobacter (3%) and Alphaproteobacteria (3%). Annotation using Cluster of Orthologous Group (COG) revealed that 41.47% of the sequenced data were for metabolic function, 24.10% for chemical process and signaling, while 17.43% of the sequences were in the poorly characterized group. Annotation using the subsystem method showed that 18% of the sequences were associated with carbohydrates, 9% were for clustering-based subsystems, and 9% contain genes coding for amino acids and derivatives, which might be beneficial in plant growth and health improvement.

Shotgun metagenomic sequencing data of sunflower rhizosphere microbial community in South Africa.

This dataset presents shotgun metagenomic sequencing of sunflower rhizosphere microbiome in Bloemhof, South Africa. Data were collected to decipher the structure and function in the sunflower microbial community. Illumina HiSeq platform using next generation sequencing of the DNA was carried out. The metagenome comprised 8,991,566 sequences totaling 1,607,022,279 bp size and 66% GC content. The metagenome was deposited into the NCBI database and can be accessed with the SRA accession number SRR10418054. An online metagenome server (MG RAST) using the subsystem database revealed bacteria had the highest taxonomical representation with 98.47%, eukaryote at 1.23%, and archaea at 0.20%. The most abundant genera were the Conexibacter (17%), Nocardioides (8%), Streptomyces (7%), Geodermatophilus (6%), Methylobacterium (5%), and Burkholderia (4%). MG-RAST assisted analysis also revealed functional annotation based on subsystem, carbohydrates sequence had 13.74%, clustering based subsystem 12.93%, amino acids and derivatives 10.30% coupled with other useful functional traits needed for plant growth and health.

Deciphering the microbiota data from termite mound soil in South Africa using shotgun metagenomics.

We present the metagenomic dataset of the microbial DNA of a termite mound in the North West Province of South Africa. This is the foremost account revealing the microbial diversity of a termite mound soil using the shotgun metagenomics approach in the Province. Next-generation sequencing of the community DNA was carried out on an Illumina Miseq platform. The metagenome comprised of 7,270,818 sequences representing 1,172,099,467 bps with a mean length of 161 bps and 52% G + C content. The sequence data is accessible at the NCBI SRA under the bioproject number PRJNA526912. Metagenomic Rapid Annotations using Subsystems Technology (MG-RAST) was employed for community analysis and it was observed that 0.36% sequences were of archeal origin, 9.51% were eukaryotes and 90.01% were fit to bacteria. A total of 5 archeal, 27 bacterial, and 22 eukaryotic phyla were revealed. Abundant genera were Sphingomonas (6.00%), Streptomyces (5.00%), Sphingobium (4.00%), Sphingopyxis (3.00%), and Mycobacterium (3.00%), representing 19.23% in the metagenome. For functional examination, Cluster-of-Orthologous-Group (COG) based annotation showed that 46.44% sequences were metabolism associated and 17.45% grouped in the poorly characterized category. Subsystem based annotation method indicated that 14.00% sequences were carbohydrates, 13.00% were clustering-based subsystems, and 10.00% genes for amino acids and derivatives together with the presence of useful traits needed in the body of science.

High-throughput sequencing data of soil bacterial communities from Tweefontein indigenous and commercial forests, South Africa.

In this report, the high-throughput sequencing data of soil bacterial communities from indigenous and commercial forests in Tweefontein, South Africa are presented. These data were collected to study the influence of land-use change on soil bacterial diversity and community structure in forests. Illumina Miseq sequencing of 16S rRNA gene amplicon was carried out on soils sampled from Tweefontein commercial (TC) and indigenous (TI) forests in South Africa. The metagenome contained 101,938 sequences with 46,709,377 bp size and 57% G + C content in TI and 91,160 sequences with 41,707,827 bp size and 57% G + C content in TC. Metagenome sequence information are available at NCBI under the Sequence Read Archive (SRA) database with accession numbers SRR8134476 (TI) and SRR8135323 (TC). Taxonomic hits distribution from Metagenomic Rast Server (MG-RAST) analysis of the TI sample revealed the dominance of the phyla Acidobacteria (21.61%), Actinobacteria (18.23%) and Verrucomicrobia (16.78%). Predominant genera were Candidatus Koribacter (12.82%), Candidatus Solibacter (11.74%) and Chthoniobacter (9.36%). MG-RAST assisted analysis of TC sample also detected the dominance of Actinobacteria (23.62%) along with Verrucomicrobia (21.92%) and Acidobacteria (20.74%). Predominant genera were Chthoniobacter (24.94%), Candidatus Solibacter (16.74%) and Candidatus Koribacter (9.39%) which play vital ecological functions in forest ecosystems.