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In order to test for the differential abundance of taxa that may drive the differences observed between inferred microbial communities derived from the different DNA isolation procedures, we performed DESeq2 analyses. Here we provide an example for such an analysis from human fecal specimen, examined using 16S rRNA gene profiling. This workflow relates to the article: Berith E. Knudsen, Lasse Bergmark, Patrick Munk, Oksana Lukjancenko, Anders Priemé, Frank M. Aarestrup, Sünje J. Pamp (2016) Impact of Sample Type and DNA Isolation Procedure on Genomic Inference of Microbiome Composition. mSystems Oct 2016, 1 (5) e00095-16; DOI: 10.1128/mSystems.00095-16
Amplicon sequencing utilizing next-generation platforms has significantly transformed how research is conducted, specifically microbial ecology. However, primer and sequencing platform biases can confound or change the way scientists interpret these data. The Pacific Biosciences RSII instrument may also preferentially load smaller fragments, which may also be a function of PCR product exhaustion during sequencing. To further examine theses biases, data is provided from 16S rRNA rumen community analyses. Specifically, data from the relative phylum-level abundances for the ruminal bacterial community are provided to determine between-sample variability. Direct sequencing of metagenomic DNA was conducted to circumvent primer-associated biases in 16S rRNA reads and rarefaction curves were generated to demonstrate adequate coverage of each amplicon. PCR products were also subjected to reduced amplification and pooling to reduce the likelihood of PCR product exhaustion during sequencing on the Pacific Biosciences platform. The taxonomic profiles for the relative phylum-level and genus-level abundance of rumen microbiota as a function of PCR pooling for sequencing on the Pacific Biosciences RSII platform were provided. Data is within this article and raw ruminal MiSeq sequence data is available from the NCBI Sequence Read Archive (SRA Accession SRP047292). Additional descriptive information is associated with NCBI BioProject PRJNA261425. http://www.ncbi.nlm.nih.gov/bioproject/PRJNA261425/ Resources in this dataset:Resource Title: NCBI Sequence Read Archive (SRA Accession SRP047292). File Name: Web Page, url: https://www.ncbi.nlm.nih.gov/sra/SRX704260 1 ILLUMINA (Illumina MiSeq) run: 978,195 spots, 532.9M bases, 311.6Mb downloads.
Archival DNA samples collected and analysed for a range of research and applied questions have accumulated in the laboratories of universities, government agencies, and commercial service providers for decades. These DNA archives represent a valuable, yet largely untapped repository of genomic information. With lowering costs of, and increasing access to, high-throughput sequencing, we predict an increase in retrospective research to explore the wealth of information that resides in these archival samples. However, for this to occur, we need confidence in the integrity of the DNA samples, often stored under sub-optimal conditions and their fitness of purpose for downstream genomic analysis. Here, we borrow from a well-established concept in ancient DNA to evaluate sample integrity, defined as loss of information content in recovered amplicons, of frozen DNA samples and based on the ratio of ⠺-diversity of short and long-read 16S rRNA gene sequences. The 16S rRNA variable region of eight..., Data analysis The Pacific Biosciences Nextflow pipeline (https://github.com/PacificBiosciences/pb-16S-nf) was followed for initial data processing. Raw reads were processed, including demultiplexing by “q2-demux†in QIIME2, and quality control was assessed with q2-cutadapt. Quantitative Insights Into Microbial Ecology 2 (QIIME2 v. 2018.11) software was used to analyse the trimmed reorientated sequences (Bolyen et al., 2019). The DADA2 denoising option (Callahan et al., 2016) was selected to pick up the representative reads for generating an amplicon sequence variants (ASVs) table. ASVs generated from DADA2 were classified using the Naive Bayes classifier and SILVA reference database version 138.1 (Quast et al., 2013). For analysis between the platforms the feature table of each platform was merged, as were the representative sequences post-DADA2 denoising with QIIME2 before building the phylogenetic tree and assigning taxonomy. Taxonomic diversity analysis All analysis was conducted wit..., , # A novel method to assess the integrity of frozen archival DNA samples: Alpha-diversity ratios of short and long-read 16S rRNA gene sequences
https://doi.org/10.5061/dryad.v9s4mw73t
We utilized DNA extracted from various agricultural soils that were stored at -20°C in a gene bank freezer room over 20 years by the South Australian Research and Development Institute (SARDI). This DNA was collected through the PREDICTA® B DNA-based soil disease testing service for broadacre farming (PREDICTA® B). We selected 87 soil DNA extracts from three Australian states (regions), spanning 10 distinct time bins between 2001 and 2020. Our primary concern was the potential DNA degradation in the oldest samples. Therefore, we included samples from the first four years (2001-2004) and selected samples more sporadically from subsequent years (2005 onwards). Alpha-diversity ratios, using Shannon's diversity index, were calculated to determine if there was a d...
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Culture independent techniques, such as shotgun metagenomics and 16S rRNA amplicon sequencing have dramatically changed the way we can examine microbial communities. Recently, changes in microbial community structure and dynamics have been associated with a growing list of human diseases. The identification and comparison of bacteria driving those changes requires the development of sound statistical tools, especially if microbial biomarkers are to be used in a clinical setting. We present mixMC, a novel multivariate data analysis framework for metagenomic biomarker discovery. mixMC accounts for the compositional nature of 16S data and enables detection of subtle differences when high inter-subject variability is present due to microbial sampling performed repeatedly on the same subjects, but in multiple habitats. Through data dimension reduction the multivariate methods provide insightful graphical visualisations to characterise each type of environment in a detailed manner. We applied mixMC to 16S microbiome studies focusing on multiple body sites in healthy individuals, compared our results with existing statistical tools and illustrated added value of using multivariate methodologies to fully characterise and compare microbial communities.
We considered microbial associations in a total of 5,026 samples from the Human Microbiome Project (HMP) comprising 18 body sites in 239 individuals recruited at two clinical centers (Baylor College of Medicine, Houston, TX and Washington University at St. Louis, MO), which in total contained 726 reliably detectable bacterial phylotypes. For details of HMP samples and data processing, see [29].
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This repository contains raw data for the manuscript "Optimization of the 16S rRNA sequencing analysis pipeline for studying in vitro communities of gut commensals". While microbial communities inhabit a wide variety of complex natural environments, in vitro culturing enables highly controlled conditions and high-throughput interrogation for generating mechanistic insights. In vitro assemblies of gut commensals have recently been introduced as models for the intestinal microbiota, which plays fundamental roles in host health. However, a protocol for 16S rRNA sequencing and analysis of in vitro samples that optimizes financial cost, time/effort, and accuracy/reproducibility has yet to be established. Here, we systematically identify protocol elements that have significant impact, introduce bias, and/or can be simplified. Our results indicate that community diversity and composition are generally unaffected by substantial protocol streamlining. Additionally, we demonstrate that a strictly aerobic halophile is an effective spike-in for estimating absolute abundances in communities of anaerobic gut commensals. This time- and money-saving protocol should accelerate discovery by increasing 16S rRNA data reliability and comparability and through the incorporation of absolute abundance estimates.
Soils along a salinity gradient from a coastal forested floodplain (Beaver Creek in Western Washington) were analyzed for their microbial community composition and community assembly processes. These files contain processed 16S rRNA amplicon sequences into OTU table, the corresponding tree file, metadata, community assembly process analysis and results, microbial phyla abundances, and codes evaluating BNTI indices and community assembly process estimates.
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The Global Metagenomic Sequencing Market Size Was Worth $1.99 Billion in 2023 and Is Expected To Reach $6.21 Billion by 2032, At a CAGR of 13.50%.
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Raw data of the 16S rRNA gene analysis of the gut microbiota composition of healthy subjects and ICU patients, determined using the HITChip (Human Intestinal Tract Chip).
Data include microbial count data (CFUs), 16S-rRNA copy number data (qPCR), and microbial community (microbiome) data from the guts of the invasive tephritid fruit flies, melon fly (Zeugodacus cucurbitae) and medfly (Ceratitis capitata). Resources in this dataset: Resource Title: R code for dada2 processing and stacked bar charts of control microbiomes File Name: Control_Processing.zip Resource Description: Data showing performance of known controls (purchased from Zymo Research) using in-house DNA extraction and PCR methods for 16S-rRNA gene amplification and sequencing. Resource Title: Data processing of 16S amplicon data File Name: 16S SSU rRNA Microbiome Data Processing and Analysis.zip Resource Description: Raw data and accompanying R scripts for analysis of figure and generation of figures and tables. Data files include both amplicon sequence variant (ASV) count data matrix and accompanying ASV sequence files and taxonomies. Analysis and figure generation are made through independent R files. Resource Title: Data and analysis of fly culturable titers File Name: CFU titers.zip Resource Description: Colony forming units (CFUs) of fruit flies at different ages and the R code for figure generation and analysis. Resource Title: qPCR of 16S rRNA of Tephritid fruit flies at different ages File Name: 16S qPCR Titers.zip Resource Description: Raw data and R code of 16S rRNA copy numbers associated with medfly and melon fly gut tissues.
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This event has been computationally inferred from an event that has been demonstrated in another species.
The inference is based on the homology mapping from PANTHER. Briefly, reactions for which all involved PhysicalEntities (in input, output and catalyst) have a mapped orthologue/paralogue (for complexes at least 75% of components must have a mapping) are inferred to the other species. High level events are also inferred for these events to allow for easier navigation.
More details and caveats of the event inference in Reactome. For details on PANTHER see also: http://www.pantherdb.org/about.jsp
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16S rRNA sequencing data
This data set list the distribution of microbial taxa from three sets of sampling campaigns from unit operations in two large desalination facilities in the US conducted between March and May 2021. The desalination plants include the Claude "Bud" Lewis Carlsbad Desalination Plant in Carlsbad, California and the Seater Desalination facility in Tampa Bay, Florida.
Output files from the No 2. Data Preparation Workflow page of the Bocas Hypoxia study.
File names and descriptions:
***seq_table.txt, *_tax_table.txt, _asv.fasta:* Sequence tables, taxonomy tables, and ASV fasta files from the full phyloseq object (before removing contaminants) and the trim med dataset (after removing contaminants).
16s-data_prep.rdata: contains all variables and phyloseq objects from the
data prep pipeline. To see the Objects , in R run
_load("16s-data_prep.rdata", verbose=TRUE)
_
A database which provides ribosome related data services to the scientific community, including online data analysis, rRNA derived phylogenetic trees, and aligned and annotated rRNA sequences. It specifically contains information on quality-controlled, aligned and annotated bacterial and archaean 16S rRNA sequences, fungal 28S rRNA sequences, and a suite of analysis tools for the scientific community. Most of the RDP tools are now available as open source packages for users to incorporate in their local workflow.
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The global human microbiome analysis market is experiencing robust growth, driven by increasing awareness of the gut-brain axis and the microbiome's role in health and disease. Advances in sequencing technologies, such as 16S rRNA sequencing and shotgun metagenomics, are enabling deeper understanding of microbial communities and their functional implications. This has fueled the development of personalized medicine approaches, including microbiome-based diagnostics and therapeutics, particularly in areas like inflammatory bowel disease, diabetes, and mental health. The market is segmented by application (hospitals, research institutes, pharmaceutical companies) and by type of sequencing technology (16S rRNA, shotgun metagenomics, metatranscriptomics, and others). Key players like Illumina, Qiagen, and others are driving innovation through the development of advanced sequencing platforms and bioinformatics tools. While the market faces challenges related to high cost and data interpretation complexity, the long-term outlook remains positive due to ongoing research, increasing clinical applications, and expanding collaborations between academia, industry, and healthcare providers. The substantial growth in the market is projected to continue through 2033, fueled by ongoing research, technological advances, and a growing understanding of the microbiome’s significance in human health. Regional growth is expected to be strong across North America, Europe, and Asia Pacific, driven by increased healthcare spending and early adoption of microbiome-based solutions. The market's growth is projected to be fueled by several factors, including the increasing prevalence of chronic diseases linked to gut microbiome imbalances, the development of novel therapeutic interventions targeting the microbiome, and the growing adoption of personalized medicine approaches. However, challenges remain in standardizing data analysis methods and ensuring regulatory approval for microbiome-based products. Nonetheless, significant investment in research and development, coupled with the potential for lucrative applications in disease prevention and treatment, indicates a substantial and sustained market expansion in the coming years. The growing demand for personalized diagnostics and therapeutics, alongside the continuous development of advanced sequencing technologies, is expected to be a key driver of this growth, leading to a market that is both innovative and rapidly expanding.
Used datasets: Dataset 16S rRNA Region Control (n) Adenoma (n) CRC (n) Available metadata Baxter V4 171 198 120 Gender, age, weight, height, BMI, country, race Zackular V4 30 30 30 Gender, age, weight, height, BMI, country, race, FOBT, medication Zeller V4 50 38 41 Gender, age, BMI, country, FOBT TOTAL V4 251 266 191 All of the above Data processing & sharing All datasets were processed using qiime2 pipeline with DADA2 for Sequence quality control and feature table construction and SILVA database for taxonomic assignment, and then a phyloseq object was constructed. Abundance table at genus level is in file genus.csv (Sample counts with NO filtering). Clean metadata is in metadata.csv file (Countries: CA - Canada. USA - United States of America. FRA - France.) Phyloseq object is in file physeq.RDS (Saved as an RDS object in R) More information is here.
This repository contains the RAW sequencing data for the herbivorous reef fish microbiome study. Trimmed reads (with primers removed) were deposited at the European Nucleotide Archive, study accession number PRJEB28397 (ERP110594).Raw fastq data files are named using the root format RunQ_GnSpe000_G, where Q is the run number (1, 2, or 3), GnSpe is the host genus and species, 00 is a unique host ID number, and G is the gut segment (F = foregut; M = midgut; H = hind). So the file name Run1_SpVir11_M_S147_L001_R2_001.fastq corresponds to: the R2 reads; midgut sample; Sparisoma viride; individual 11; Run01. Raw fastq files are deposited here by Run. To process please use the scripts in the Pipeline for 16S rRNA processing using DADA2 directory.DNA Extraction & Sequencing MethodsFor all samples, we homogenized material from each gut segment (fore, mid, hind) separately in 50mL conical tubes for 2 minutes on a Vortex Genie 2. We collected 200 mg (wet weight) of homogenate for DNA extraction following the Human Microbiome Project Core Microbiome Sampling Protocol A (v12.0, HMP Protocol # 07-001) for stool samples. Prior to extraction, we heat treated each sample, first at 65℃ f or 10 minutes, followed by 95℃ for 10 minutes. We then used the PowerSoil® DNA Isolation Kit (MoBio) following the manufacturer's protocol to extract community DNA from each sample. Extracted DNA was sequenced on an Illumina MiSeq by Integrated Microbiome Resource at the Centre for Comparative Genomics and Evolutionary Bioinformatics (Dalhousie University). We targeted the V4-V5 hypervariable region using 515F (5′-GTGYCAGCMGCCGCGGTA) and 926R (5′-CCGYCAATTYMTTTRAGT). We collected 53 individual fish encompassing seven species and three genera. Two species—Sparisoma chrysopterum and Scarus vetula—were only represented by 1 and 2 individuals, respectively. Though we chose to omit these samples from the final analysis, these samples were sequenced and analyzed along with the rest of the samples and made the data available for analysis.We generate sequence data for all 159 sample—three gut segments (fore, mid, and hind) from 53 individuals. Sequencing was conducted across three runs. In the first run (Run01), 144 samples were sequenced and, due to lower than average yield, were re-sequenced (Run02). The remaining 15 samples (5 individuals) were sequenced on a separate run (Run03).
Overview of the datasets used in the study. Sampling and data characteristics of the seven datasets used in the study, A1–A4 for the false positive rate and spike-in retrieval tests and B1–B3 for the beta-diversity optimization tests. (XLSX 5 kb)
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This dataset contains configuration and results files for the proof-of-principle of the dadasnake pipeline. Includes dadasnake output and tables with the composition of ground-truth data or mock-communities.
dadasnake is a user-friendly, one-command Snakemake pipeline that wraps the pre-processing of sequencing reads and the delineation of exact sequence variants by using the favorably benchmarked and widely-used DADA2 algorithm with a taxonomic classification and the post-processing of the resultant tables, including hand-off in standard formats. The suitability of the provided default configurations is demonstrated using mock-community data from bacteria and archaea, as well as fungi. By use of Snakemake, dadasnake makes efficient use of high-performance computing infrastructures. Easy user configuration guarantees flexibility of all steps, including the processing of data from multiple sequencing platforms. dadasnake facilitates easy installation via conda environments. dadasnake is available at https://github.com/a-h-b/dadasnake .
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In order to test for the differential abundance of taxa that may drive the differences observed between inferred microbial communities derived from the different DNA isolation procedures, we performed DESeq2 analyses. Here we provide an example for such an analysis from human fecal specimen, examined using 16S rRNA gene profiling. This workflow relates to the article: Berith E. Knudsen, Lasse Bergmark, Patrick Munk, Oksana Lukjancenko, Anders Priemé, Frank M. Aarestrup, Sünje J. Pamp (2016) Impact of Sample Type and DNA Isolation Procedure on Genomic Inference of Microbiome Composition. mSystems Oct 2016, 1 (5) e00095-16; DOI: 10.1128/mSystems.00095-16