Long-Read 16S rRNA Amplicon and Metagenomic Data of Swine Feed-Additive Probiotics Product.
Ontology highlight
ABSTRACT:
SUBMITTER: Sudjai A
PROVIDER: S-EPMC9476918 | biostudies-literature | 2022 Sep
REPOSITORIES: biostudies-literature
Publications
Long-Read 16S rRNA Amplicon and Metagenomic Data of Swine Feed-Additive Probiotics Product.
Sudjai Aunthikarn A Suriye Kantgamon K Kruasuwan Worarat W Jenjaroenpun Piroon P Foongladda Suporn S Wongsurawat Thidathip T
Microbiology resource announcements 20220822 9
Swine feed-additive probiotics products play a major role in swine performance and welfare by promoting gut health. Here, we present two types of data, including a full-length 16S rRNA amplicon sequence data and a long-read metagenomic sequence data obtained from the same commercial probiotic product. ...[more]
Similar Datasets
Project description:Analysis of feed supplements can highlight microbial diversity and the prevalence of antimicrobial resistance (AMR), allowing users to monitor the safety of their animals. The 16S amplicon and metagenomic data generated by nanopore sequencing revealed that Bacillus was the dominant prokaryote, and AMR genes were detected in the animal probiotic products.
| S-EPMC9584310 | biostudies-literature
Project description:IntroductionBiological communities present in soil are essential to sustainable and productive agricultural practices; however, an accurate determination of the ecological status of agricultural soils remains to date an elusive task. An ideal indicator should be pervasive, play a relevant role in the ecosystem, show a rapid and proportional answer to external perturbations and be easily and economically measurable. Rhizobacteria play a major role in determining soil properties, becoming an attractive candidate for the detection of ecological indicators. The application of massive sequencing technologies to metagenomic analysis is providing an increasingly more precise view of the structure and composition of soil communities. In this work, we analyse soil rhizobacterial composition under various stress levels to search for potential ecological indicators.General biodiversity indicatorsOur results suggest that the Shannon index requires observation of a relatively large number of individuals to be representative of the true population diversity, and that the Simpson index may underestimate rare taxa in rhizobacterial environments.Taxonomical classification methodsDetection of indicator taxa requires comparison of taxonomical classification of sequences. We have compared RDP classifier, RTAX and similarity-based taxonomical classification and selected the latter for taxonomical assignment because it provides larger detail.Taxonomy-based ecological indicatorsThe study of significant variations in common, clearly identified, taxa, using paired datasets allows minimization of non-treatment effects and avoidance of false positives. We have identified taxa associated to specific perturbations as well as taxa generally affected in treated soils. Changes in these taxa, or combinations of them, may be used as ecological indicators of soil health. The overall number and magnitude of changes detected in taxonomic groups does also increase with stress. These changes constitute an alternative indicator to measuring specific taxa, although their determination requires large sample sizes, better obtained by massive sequencing.SummaryThe main ecological indicators available are the Shannon index, OTU counts and estimators, overall detection of the number and proportion of changes, and changes of specific indicator taxa. Massive sequencing remains the most accurate tool to measure rhizobacterial ecological indicators. When massive sequencing is not an option, various cultivable taxonomic groups, such as specific groups in the Actinobacteria tree, are attractive as potential indicators of large disruptions to the rhizobiome.
| S-EPMC5079562 | biostudies-literature
Project description:BackgroundHigh-throughput bacterial 16S rRNA gene sequencing followed by clustering of short sequences into operational taxonomic units (OTUs) is widely used for microbiome profiling. However, clustering of short 16S rRNA gene reads into biologically meaningful OTUs is challenging, in part because nucleotide variation along the 16S rRNA gene is only partially captured by short reads. The recent emergence of long-read platforms, such as single-molecule real-time (SMRT) sequencing from Pacific Biosciences, offers the potential for improved taxonomic and phylogenetic profiling. Here, we evaluate the performance of long- and short-read 16S rRNA gene sequencing using simulated and experimental data, followed by OTU inference using computational pipelines based on heuristic and complete-linkage hierarchical clustering.ResultsIn simulated data, long-read sequencing was shown to improve OTU quality and decrease variance. We then profiled 40 human gut microbiome samples using a combination of Illumina MiSeq and Blautia-specific SMRT sequencing, further supporting the notion that long reads can identify additional OTUs. We implemented a complete-linkage hierarchical clustering strategy using a flexible computational pipeline, tailored specifically for PacBio circular consensus sequencing (CCS) data that outperforms heuristic methods in most settings: https://github.com/oscar-franzen/oclust/ .ConclusionOur data demonstrate that long reads can improve OTU inference; however, the choice of clustering algorithm and associated clustering thresholds has significant impact on performance.
| S-EPMC4593230 | biostudies-literature
Project description:Traditionally slurry is used as source of nitrogen, phosphorous, and potassium in bio fertilizers to improve crop production. However, poorly managed slurry causes a hazardous effect to the environment by producing greenhouse gases, causing the eutrophication of water bodies, and polluting the groundwater. It has been largely reported that the microbial presence in slurry causing a diverse effect on its storage and disposal system. However, the diversity of bacterial populations in pig slurries remains largely unexplored. Here we report the bacterial diversity present in the slurry from slurry pits, and the effect of storage time on bacterial population. We collected 42 samples from three different pig slurry pits, as three replicates from each one until the 14th week. We used the 16S rRNA, Quantitative Insights Into Microbial Ecology (QIIME) and Phylogenetic Investigation of Communities by Reconstruction of Unobserved States (PICRUSt) protocols for the metagenomic downstream analysis. Taxonomic annotation using the Greengenes metagenomic database indicated that on an average 76.2% Firmicutes, 14.4% Bacteroidetes, 4.9% Proteobacteria, etc. microbial populations were present. Comparative microbial analysis showed that the population of Firmicutes decreased from the first to the 14th week, whereas the population of Bacteroidetes increased from the first to the 14th week. Through principal coordinate analysis (PCoA), (linear discriminant analysis effect size (LEfSe), and Pearson's correlation analysis, we found microbial biomarkers according to the storage time point. All bacterial populations were well clustered according to the early, middle, and last weeks of storage. LEfSe showed that Actinobacteria, Lachnospiraceae, Ruminococcaceae, and Bacteroidia are dominantly present in first, seventh, ninth, and 14th week, respectively. Lachnospiraceae and Ruminococcaceae are ubiquitous gastrointestinal non-pathogenic bacteria. KEGG pathways, such as membrane transport, carbohydrate and amino acid metabolism, genetic replication and repair, were significant among all samples. Such a KEGG pathway may indicate the association between the host organism's metabolic activity and the microbes present in the gastro intestinal tract (GIT).
| S-EPMC7460454 | biostudies-literature
Project description:Rice is a major staple food across the globe. Its growth and productivity is highly dependent on the rhizobiome where crosstalk takes place between plant and the microbial community. Such interactions lead to selective enrichment of plant beneficial microbes which ultimately defines the crop health and productivity. In this study, rhizobiome modulation is documented throughout the development of rice plant. Based on 16S rRNA gene affiliation at genus level, abundance, and diversity of plant growth promoting bacteria increased during the growth stages. The observed α diversity and rhizobiome complexity increased significantly (p < 0.05) during plantation. PCoA indicates that different geographical locations shared similar rhizobiome diversity but exerted differential enrichment (p < 0.001). Diversity of enriched genera represented a sigmoid curve and subsequently declined after harvest. A major proportion of dominant enriched genera (p < 0.05, abundance > 0.1%), based on 16S rRNA gene, were plant growth promoting bacteria that produces siderophore, indole-3-acetic acid, aminocyclopropane-1-carboxylic acid, and antimicrobials. Hydrogenotrophic methanogens dominated throughout cultivation. Type I methanotrophs (n = 12) had higher diversity than type II methanotrophs (n = 6). However, the later had significantly higher abundance (p = 0.003). Strong enrichment pattern was also observed in type I methanotrophs being enriched during water logged stages. Ammonia oxidizing Archaea were several folds more abundant than ammonia oxidizing bacteria. K-strategists Nitrosospira and Nitrospira dominated ammonia and nitrite oxidizing bacteria, respectively. The study clarifies the modulation of rhizobiome according to the rice developmental stages, thereby opening up the possibilities of bio-fertilizer treatment based on each cultivation stages.
| S-EPMC6764247 | biostudies-literature
Project description:Amplicon sequencing of the 16S rRNA gene is the predominant method to quantify microbial compositions and to discover novel lineages. However, traditional short amplicons often do not contain enough information to confidently resolve their phylogeny. Here we present a cost-effective protocol that amplifies a large part of the rRNA operon and sequences the amplicons with PacBio technology. We tested our method on a mock community and developed a read-curation pipeline that reduces the overall read error rate to 0.18%. Applying our method on four environmental samples, we captured near full-length rRNA operon amplicons from a large diversity of prokaryotes. The method operated at moderately high-throughput (22286-37,850 raw ccs reads) and generated a large amount of putative novel archaeal 23S rRNA gene sequences compared to the archaeal SILVA database. These long amplicons allowed for higher resolution during taxonomic classification by means of long (∼1000 bp) 16S rRNA gene fragments and for substantially more confident phylogenies by means of combined near full-length 16S and 23S rRNA gene sequences, compared to shorter traditional amplicons (250 bp of the 16S rRNA gene). We recommend our method to those who wish to cost-effectively and confidently estimate the phylogenetic diversity of prokaryotes in environmental samples at high throughput.
| S-EPMC6849856 | biostudies-literature
Project description:The gastrointestinal tract, the largest human microbial reservoir, is highly dynamic. The gut microbes play essential roles in causing colorectal diseases. In the present study, we explored potential keystone taxa during the development of colorectal diseases in central China. Fecal samples of some patients were collected and were allocated to the adenoma (Group A), colorectal cancer (Group C), and hemorrhoid (Group H) groups. The 16S rRNA amplicon and shallow metagenomic sequencing (SMS) strategies were used to recover the gut microbiota. Microbial diversities obtained from 16S rRNA amplicon and SMS data were similar. Group C had the highest diversity, although no significant difference in diversity was observed among the groups. The most dominant phyla in the gut microbiota of patients with colorectal diseases were Bacteroidetes, Firmicutes, and Proteobacteria, accounting for >95% of microbes in the samples. The most abundant genera in the samples were Bacteroides, Prevotella, and Escherichia/Shigella, and further species-level and network analyses identified certain potential keystone taxa in each group. Some of the dominant species, such as Prevotella copri, Bacteroides dorei, and Bacteroides vulgatus, could be responsible for causing colorectal diseases. The SMS data recovered diverse antibiotic resistance genes of tetracycline, macrolide, and beta-lactam, which could be a result of antibiotic overuse. This study explored the gut microbiota of patients with three different types of colorectal diseases, and the microbial diversity results obtained from 16S rRNA amplicon sequencing and SMS data were found to be similar. However, the findings of this study are based on a limited sample size, which warrants further large-scale studies. The recovery of gut microbiota profiles in patients with colorectal diseases could be beneficial for future diagnosis and treatment with modulation of the gut microbiota. Moreover, SMS data can provide accurate species- and gene-level information, and it is economical. It can therefore be widely applied in future clinical metagenomic studies.
| S-EPMC8299945 | biostudies-literature
Project description:UnlabelledMetagenomic data, which contains sequenced DNA reads of uncultured microbial species from environmental samples, provide a unique opportunity to thoroughly analyze microbial species that have never been identified before. Reconstructing 16S ribosomal RNA, a phylogenetic marker gene, is usually required to analyze the composition of the metagenomic data. However, massive volume of dataset, high sequence similarity between related species, skewed microbial abundance and lack of reference genes make 16S rRNA reconstruction difficult. Generic de novo assembly tools are not optimized for assembling 16S rRNA genes. In this work, we introduce a targeted rRNA assembly tool, REAGO (REconstruct 16S ribosomal RNA Genes from metagenOmic data). It addresses the above challenges by combining secondary structure-aware homology search, zproperties of rRNA genes and de novo assembly. Our experimental results show that our tool can correctly recover more rRNA genes than several popular generic metagenomic assembly tools and specially designed rRNA construction tools.Availability and implementationThe source code of REAGO is freely available at https://github.com/chengyuan/reago.
| S-EPMC4765874 | biostudies-literature
Project description:BackgroundColorectal cancer (CRC) is among the five leading causes of cancer incidence and mortality. During the past decade, the role of the gut microbiota and its dysbiosis in colorectal tumorigenesis has been emphasized. Metagenomics and amplicon-based microbiome profiling provided insights into the potential role of microbial dysbiosis in the development of CRC.AimTo address the scarcity of information on differential microbiome composition of tumor tissue in comparison to adenomas and the lack of such data from Egyptian patients with CRC.Materials and methodsLong-read nanopore sequencing of 16S rRNA amplicons was used to profile the colonic microbiota from fresh colonoscopic biopsy samples of Egyptian patients with CRC and patients with colonic polyps.ResultsSpecies richness of CRC lesions was significantly higher than that in colonic polyps (p-value = 0.0078), while evenness of the CRC group was significantly lower than the colonic polyps group (p-value = 0.0055). Both species richness and Shannon diversity index of the late onset CRC samples were significantly higher than those of the early onset ones. The Firmicutes-to-Bacteroidetes (F/B) ratio was significantly higher in the CRC group than in the colonic polyps group (p-value = 0.0054), and significantly higher in samples from early-onset CRC. The Enterococcus spp. were significantly overabundant in patients with rectal cancer and early-onset CRC, while Staphylococcus spp. were significantly higher in patients with sigmoid cancer and late-onset CRC. In addition, the relative abundance of Fusobacterium nucleatum was significantly higher in CRC patients.ConclusionDifferentiating trends were identified at phylum, genus, and species levels, despite the inter-individual differences. In summary, this study addressed the microbial dysbiosis associated with CRC and colonic polyps groups, paving the way for a better understanding of the pathogenesis of early and late-onset CRC in Egyptian patients.
| S-EPMC11789410 | biostudies-literature
Project description:Microbial amplicon sequencing studies are an important tool in biological and biomedical research. Widespread 16S rRNA gene microbial surveys have shed light on the structure of many ecosystems inhabited by bacteria, including the human body. However, specialized software and algorithms are needed to convert raw sequencing data into biologically meaningful information (i.e. tables of bacterial counts). While different bioinformatic pipelines are available in a rapidly changing and improving field, users are often unaware of limitations and biases associated with individual pipelines and there is a lack of agreement regarding best practices. Here, we compared six bioinformatic pipelines for the analysis of amplicon sequence data: three OTU-level flows (QIIME-uclust, MOTHUR, and USEARCH-UPARSE) and three ASV-level (DADA2, Qiime2-Deblur, and USEARCH-UNOISE3). We tested workflows with different quality control options, clustering algorithms, and cutoff parameters on a mock community as well as on a large (N = 2170) recently published fecal sample dataset from the multi-ethnic HELIUS study. We assessed the sensitivity, specificity, and degree of consensus of the different outputs. DADA2 offered the best sensitivity, at the expense of decreased specificity compared to USEARCH-UNOISE3 and Qiime2-Deblur. USEARCH-UNOISE3 showed the best balance between resolution and specificity. OTU-level USEARCH-UPARSE and MOTHUR performed well, but with lower specificity than ASV-level pipelines. QIIME-uclust produced large number of spurious OTUs as well as inflated alpha-diversity measures and should be avoided in future studies. This study provides guidance for researchers using amplicon sequencing to gain biological insights.
| S-EPMC6964864 | biostudies-literature