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

http://msystems.asm.org/content/1/5/e00095-16

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.

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.

Complete, fully reproducible phyloseq workflow for 16S rRNA microbiome analysis of the Bocas del Toro hypoxic event. Included are the HTML and Rmarkdown (.Rmd) files plus the output of the DADA2 pipeline in Rdata format which is the main input file for the phyloseq workflow.Sequences were filtered and dereplicated. Sequence variants were called and putative chimeras identified. Sequence variants were classified using SILVA and GreenGenes databases. File contains a sequence table and taxonomy table. We imported this file directly into phyloseq for community analysis.The MANUAL_INPUT.zip directory contains all of the additional input files (mainly tables) necessary to run this workflow. Please note script will create a directory called R_OUTPUT wherever the .Rmd file was called from. This is where the script will store output files.

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.

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 trimmed 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)

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...

16S rRNA sequencing data

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].

Improvements in high-throughput sequencing makes targeted amplicon analysis an ideal method for the study of human and environmental microbiomes by undergraduates. Multiple bioinformatics programs are available to process and interpret raw microbial diversity datasets, and the choice of programs to use in curricula is largely determined by student learning goals. Many of the most commonly used microbiome bioinformatics platforms offer end-to-end data processing and data analysis using a command line interface (CLI), but the downside for novice microbiome researchers is the steep learning curve often required. Alternatively, some sequencing providers include processing of raw data and taxonomy assignments as part of their pipelines. This, when coupled with available web-based or graphical user interface (GUI) analysis and visualization tools, eliminates the need for students or instructors to have extensive CLI experience. However, lack of universal data formats can make integration of these tools challenging. For example, tools for upstream and downstream analyses frequently use multiple different data formats which then require writing custom scripts or hours of manual work to make the files compatible. Here, we describe a microbial ecology bioinformatics curriculum that focuses on data analysis, visualization, and statistical reasoning by taking advantage of existing web-based and GUI tools. We created the Program for Unifying Microbiome Analysis Applications (PUMAA), which solves the problem of inconsistent files by formatting the output files from several raw data processing programs to seamlessly transition to a suite of GUI programs for analysis and visualization of microbiome taxonomic and inferred functional profiles. Additionally, we created a series of tutorials to accompany each of the microbiome analysis curricular modules. From pre- and post-course surveys, students in this curriculum self-reported conceptual and confidence gains in bioinformatics and data analysis skills. Students also demonstrated gains in biologically relevant statistical reasoning based on rubric-guided evaluations of open-ended survey questions and the Statistical Reasoning in Biology Concept Inventory. The PUMAA program and associated analysis tutorials enable students and researchers with no computational experience to effectively analyze real microbiome datasets to investigate real-world research questions.

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.

Groundwater samples for eDNA analysis were collected in June 2015 approximately 18 months after the formal commissioning of the Pawsey Centre GWC system located in suburban Perth, Western Australia, in November 2013. DNA was extracted from filtered bore water samples from Production bores and monitoring bores from the Pawsey bores and Water Corporation bores. Lineage: Groundwater was filtered on 0.1 µm Durapore® membrane filters using a peristaltic pump. Cells were harvested on the on 0.1 µm Durapore® membrane filters and 0.2 g biomass from the filters was used for extracting DNA the Powersoil DNA isolation kit with extended incubation steps and an extra ethanol wash before the final elution in 100 µL of C6 (elution buffer). DNA samples were amplified using EMP 16S rRNA primers to analyse bacteria communities and EMP 18S v4 rRNA primers for eukaryote community analysis. Bacterial and archaeal 16S rRNA genes were amplified using the standard Earth Microbiome project (EMP) 16S Illumina amplicon protocol (http://press.igsb.anl.gov/earthmicrobiome/protocols-and-standards/16s/) with primers: 515F (GTGCCAGCMGCCGCGGTAA) and 806R (GGACTACHVGGGTWTCTAAT) (Caporaso et al., 2011). Eukaryotic 18S rRNA genes were amplified using EMP 18S Illumina amplicon protocol (http://press.igsb.anl.gov/earthmicrobiome/protocols-and-standards/18s/) with primers Euk_1391f (GTACACACCGCCCGTC) and EukBr (TGATCCTTCTGCAGGTTCACCTAC) (Amaral-Zettler et al. 2009). Amplicon libraries were prepared with Illumina Nextera kit. Next generation sequencing (NGS) was carried out using the Illumina MiSeq platform (Illumina, Inc., San Diego, USA), 2x250 bp with paired reads, and performed according to manufacturer’s directions at the Ramaciotti Centre for Genomics (UNSW Sydney, Australia). The 18S and 16S rRNA gene sequence data were processed using a custom pipeline Greenfield Hybrid Amplicon Pipeline (GHAP) which is based around USEARCH tools (Edgar, 2013).

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.

In humans, a 47S precursor rRNA (pre-rRNA) is transcribed by RNA polymerase I from rRNA-encoding genes (rDNA) at the boundary of the fibrillar center and the dense fibrillar components of the nucleolus (Stanek et al. 2001). The 47S precursor is processed over the course of about 5-8 minutes (Popov et al. 2013) by endoribonucleases and exoribonucleases to yield the 28S rRNA and 5.8S rRNA of the 60S subunit and the 18S rRNA of the 40S subunit (reviewed in Mullineus and Lafontaine 2012, Henras et al. 2015). As the pre-rRNA is being transcribed, a large protein complex, the small subunit (SSU) processome, assembles in the region of the 18S rRNA sequence, forming terminal knobs on the pre-rRNA (reviewed in Phipps et al. 2011, inferred from yeast in Dragon et al. 2002). The SSU processome contains both ribosomal proteins of the small subunit and processing factors which process the pre-rRNA and modify nucleotides. Through addition of subunits the SSU processome appears to be converted into the larger 90S pre-ribosome (inferred from yeast in Grandi et al. 2002). An analogous large subunit processome (LSU) assembles in the region of the 28S rRNA, however the LSU is less well characterized (inferred from yeast in McCann et al. 2015).
Following cleavage of the pre-rRNA within internal transcribed spacer 1 (ITS1), the pre-ribosomal particle separates into a pre-60S subunit and a pre-40S subunit in the nucleolus (reviewed in Hernandez-Verdun et al. 2010, Phipps et al. 2011). The pre-60S and pre-40S ribosomal particles are then exported from the nucleus to the cytoplasm where the processing factors dissociate and recycle back to the nucleus
Nuclease digestions of the 47S pre-rRNA can follow several paths. In the major pathway, the ends of the 47S pre-rRNA are trimmed to yield the 45S pre-rRNA. Digestion at site 2 (also called site 2b in mouse, see Henras et al. 2015 for nomenclature) cleaves the 45S pre-rRNA to yield the 30S pre-rRNA containing the 18S rRNA of the small subunit and the 32S pre-rRNA containing the 5.8S rRNA and the 28S rRNA of the large subunit. The 32S pre-rRNA is digested in the nucleus to yield the 5.8S rRNA and the 28S rRNA while the 30S pre-rRNA is digested in the nucleus to yield the 18SE pre-rRNA which is then processed in the nucleus and cytosol to yield the 18S rRNA. At least 286 human proteins, 74 of which have no yeast homolog, are required for efficient processing of pre-rRNA in the nucleus (Tafforeau et al. 2013)

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.

Rapid and reliable identification of bacterial pathogens directly from patient samples is required for optimizing antimicrobial therapy. Although Sanger sequencing of the 16S ribosomal RNA (rRNA) gene is used as a molecular method, species identification and discrimination is not always achievable for bacteria as their 16S rRNA genes have sometimes high sequence homology. Recently, next generation sequencing (NGS) of the 16S–23S rRNA encoding region has been proposed for reliable identification of pathogens directly from patient samples. However, data analysis is laborious and time-consuming and a database for the complete 16S–23S rRNA encoding region is not available. Therefore, a better, faster, and stronger approach is needed for NGS data analysis of the 16S–23S rRNA encoding region. We compared speed and diagnostic accuracy of different data analysis approaches: de novo assembly followed by Basic Local Alignment Search Tool (BLAST), operational taxonomic unit (OTU) clustering, or mapping using an in-house developed 16S–23S rRNA encoding region database for the identification of bacterial species. De novo assembly followed by BLAST using the in-house database was superior to the other methods, resulting in the shortest turnaround time (2 h and 5 min), approximately 2 h less than OTU clustering and 4.5 h less than mapping, and a sensitivity of 80%. Mapping was the slowest and most laborious data analysis approach with a sensitivity of 60%, whereas OTU clustering was the least laborious approach with 70% sensitivity. Although the in-house database requires more sequence entries to improve the sensitivity, the combination of de novo assembly and BLAST currently appears to be the optimal approach for data analysis.

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)

Output files from the No 1. DADA2 Workflow page of the Bocas Hypoxia study.

File names and descriptions:

RUN01_read_changes.txt : Tracking changes in read counts (per sample) from the beginning to end of the DADA2 workflow.

RUN02_read_changes.txt : Tracking changes in read counts (per sample) from the beginning to end of the DADA2 workflow.

combo_pipeline.rdata: contains sequence and taxonomy tables from the DADA2 pipeline needed for subsequent analyses. To see the Objects , in R run _load("combo_pipeline.rdata", verbose=TRUE)
_

1) seqtab.1: Sequence table from Run01 before merging with Run02.

2) seqtab.1: Sequence table from Run02 before merging with Run01.

3) st.sum: merged sequence table before removing chimeras

4) st.all: duplicate of st.sum

5) seqtab: merged sequence table after removing chimeras

6) tax_silva: Silva (v132) taxonomy table of seqtab

7) tax_gg: GreenGenes taxonomy table of seqtab

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%.

16S rRNA Sequencing Market Outlook

According to our latest research, the 16S rRNA sequencing market size reached USD 1.82 billion in 2024 globally. The market is experiencing robust expansion, with a recorded CAGR of 13.7% from 2025 to 2033. By 2033, the global market size is forecasted to attain USD 5.11 billion, driven by the increasing adoption of next-generation sequencing technologies and the rising demand for precise microbial identification in clinical diagnostics, environmental testing, and food safety. The surge in research activities and technological advancements in sequencing methods are primary growth factors propelling the market forward.

One of the most significant growth drivers for the 16S rRNA sequencing market is the escalating demand for microbial diversity analysis across multiple sectors. The ability to accurately identify and classify bacteria in complex samples has become indispensable for clinical diagnostics, environmental monitoring, and agricultural research. The expanding applications in human microbiome studies, which are pivotal for understanding disease mechanisms and developing targeted therapeutics, further contribute to market growth. Moreover, the increasing prevalence of infectious diseases and the urgent need for rapid and reliable diagnostic tools have accelerated the adoption of 16S rRNA sequencing in both developed and emerging economies.

Technological advancements in sequencing platforms, particularly the evolution of next-generation sequencing (NGS) and the emergence of third-generation sequencing technologies, have revolutionized the 16S rRNA sequencing market. These innovations have significantly reduced sequencing costs, improved throughput, and enhanced data accuracy, making sequencing more accessible to a broader range of end-users. The integration of advanced bioinformatics tools for data analysis and interpretation has further streamlined the workflow, enabling researchers and clinicians to derive actionable insights from complex microbial communities. As a result, the market is witnessing a paradigm shift from traditional methods to high-throughput sequencing solutions, fueling sustained growth.

Another critical factor driving the market is the growing emphasis on food safety and environmental monitoring. Regulatory authorities worldwide are mandating stringent quality control measures to detect and mitigate microbial contamination in food products and water sources. The application of 16S rRNA sequencing in identifying pathogenic and spoilage microorganisms is gaining traction among food manufacturers, water treatment facilities, and public health agencies. This trend is further amplified by the globalization of food supply chains and the rising awareness of foodborne illnesses, creating lucrative opportunities for market players to expand their offerings and enhance their market presence.

From a regional perspective, North America currently dominates the 16S rRNA sequencing market, owing to the presence of leading sequencing technology providers, advanced healthcare infrastructure, and substantial investments in research and development. However, the Asia Pacific region is poised to exhibit the highest growth rate during the forecast period, attributed to the increasing adoption of sequencing technologies in emerging economies such as China and India. The region's expanding biotechnology sector, rising healthcare expenditure, and supportive government initiatives are expected to drive significant market expansion, making Asia Pacific a key focus area for industry stakeholders.

Sequencing Technology Analysis

The sequencing technology segment is a cornerstone of the 16S rRNA sequencing market, encompassing next-generation sequencing (NGS), Sanger sequencing, and third-generation sequencing platforms. Next-generation sequencing dominates this segment due to its unparalleled throughput, scalability, and cost-effectiveness. NGS platforms have revolutionized microbial genomics by enabling the simultaneous analysis of thousands of samples, making them ideal for large-scale studies in clinical diagnostics, environmental monitoring, and microbial ecology. The continuous advancements in NGS technologies, such as improved read lengths, faster run times, and enhanced data accuracy, have further solidified their position as the preferred choice for 16S rRNA sequencing applications.

Sanger sequenci