How does 16s rrna sequencing work

Variable regions of the 16S rRNA gene are frequently used for phylogenetic classification of genus or species in diverse microbial populations.

Amplicon-based next-generation sequencing of the 16S gene offers several advantages over capillary sequencing or PCR-based approaches. Learn how it works with this guide to 16S sequencing methods. Unlike capillary sequencing or PCR-based approaches, next-generation sequencing is a culture-free method that enables analysis of the entire microbial community within a sample. ITS analysis with NGS enables rapid fungal identification to help advance our understanding of the mycobiome.

Furthermore, NGS offers the ability to combine multiple samples in a sequencing run. Using the 16S metagenomics workflow with the iSeq System, you can achieve genus-level sensitivity for surveys of bacterial populations. All the information you need, from library preparation to final data analysis.

Select the best tools for a broad range of microbiology applications for your laboratory. View a demonstrated protocol for analyzing fungal or metagenomic samples that includes primer sequences and provides a recommended data analysis workflow.

Metagenomics is one of the fastest-growing scientific disciplines. This document highlights peer-reviewed publications that apply Illumina sequencing technologies to metagenomics research. All microorganisms, especially unknown samples, should be treated as potential pathogens.

Follow aseptic technique to avoid contaminating the samples, researchers, or the laboratory. Wash hands before and after handling bacteria, use gloves, and wear protective clothing.

Some reagents may be harmful! Pure culture is essential for the 16S rRNA sequencing. Before proceeding to isolation of genomic DNA, make sure the starting material is entirely pure. This can be done by streak plating to isolate individual colonies. These can be further grown streaked on plates individually, or in broth, if needed.

Laboratory equipment required: Thermal cycler for PCR. The function of the thermal cycler is to raise and lower temperature according to a set program. While creating the program you will be asked to enter the temperature and time values for every PCR step as well as total number of cycles. Agarose gel electrophoresis system. It is used to separate DNA fragments based on their size and charge. In this protocol, agarose gel electrophoresis will be used to visualize the quality of isolated genomic DNA and PCR products.

Grow your microorganism on a suitable medium. Both liquid and solid media can be used in this step. Choose conditions that yield the best growth. Isolation of gDNA. If bacteria were grown on solid medium, scrape some cells using a sterile loop and resuspend them in 1 mL of distilled water If bacteria were grown in liquid medium, use approximately 1.

Here, a commercial kit was used to isolate gDNA from 1. Note 1: For some Gram-negative bacteria this step can be omitted and replaced by simple release of DNA from cells by boiling. Note 2: Gram-positive bacterial cells are difficult to disrupt.

It is therefore recommended to choose a gDNA isolation method or kit that is dedicated to isolation from this group of bacteria. Check the quality of the isolated gDNA by agarose gel electrophoresis.

Load a molecular mass standard and run the electrophoresis until the dye front reaches the bottom of the gel. Once the electrophoresis is completed, visualize the gel on a suitable transilluminator either UV or blue light. An example of the gDNA quality check is shown in Figure 3. If the gDNA passes the quality control i. Pipet the whole volume i.

These dilutions will be used as template in the PCR reaction. Optimization of the protocol is required for each polymerase and primer pair.

Thaw all reagents on ice. Prepare the PCR master mix as shown in Table 2. Since the DNA polymerase is active at room temperature, the reaction setup must be performed on ice, i. Prepare one reaction per each gDNA sample and one reaction for negative control.

Negative control is a PCR mix without the gDNA template and is used to ensure that the other components of the reaction are not contaminated. Note: In case of multiple samples, a master mix is commonly prepared. Master mix is a solution containing all the reaction components except the template. It helps to omit repetitive pipetting, avoid pipetting error, and ensures high consistency between the samples. To prepare master mix, multiply the volume of each component except the DNA template by the number of samples tested.

Mix all the components in microcentrifuge tube and pipet the whole volume up and down several times. Set the PCR machine with the program shown in Table 3. Put the tubes in the PCR machine and start the program. Once the program is completed, examine the quality of your PCR product by agarose gel electrophoresis. If other bands i. If a single band of expected size is present, proceed to the next step. Here, the PCR reaction with x diluted gDNA template yielded the best product as it had a sharp band of expected size and lacked unspecific products.

Hence it was chosen to be purified and sent for sequencing. Prior to sequencing, the product must be cleaned up from residual primers, deoxyribonucleotides, polymerase, and buffer which were present in the PCR reaction. The PCR product binds to the column, while other components flow through the column. The column is then washed using washing buffer, and finally, the DNA is eluted in the buffer of choice.

Confirm that the elution buffer that is supplemented with the kit is compatible with sequencing. Follow the guidelines for the submission of sequencing samples at the chosen sequencing facility. For the best sequence coverage, use the PCR amplification primers the same as used in the section 2. For the forward primer, open the chromatogram, and carefully examine the sequence.

An ideal chromatogram for a quality sequence should have evenly spaced peaks and little or no background signals Figure 5 A. If the chromatogram is not high quality, the sequence should be discarded, or the sequence text file should be revised according to the following: The presence of double peaks throughout the chromatogram indicates the presence of multiple DNA templates.

This can be the case if the bacterial culture was not pure. Such a sequence should be discarded Figure 5 B. Divergence and redundancy of 16S rRNA sequences in genomes with multiple rrn operons. Stoddard, S. Pei, A. Diversity of 16S rRNA genes within individual prokaryotic genomes. Freddolino, P. Newly identified genetic variations in common Escherichia coli MG stock cultures. Exact sequence variants should replace operational taxonomic units in marker-gene data analysis.

Wexler, H. Bacteroides : the god, the bad, and the nitty-gritty. Oligotyping: differentiating between closely related microbial taxa using 16S rRNA gene data. Methods Ecol. Petersen, L. Community characteristics of the gut microbiomes of competitive cyclists. Microbiome 5 , 98 Franzen, O. Microbiome 3 , 43 Mosher, J. Methods , 59—60 PeerJ 4 , e Wagner J. BMC Microbiol.

Earl, J. Species-level bacterial community profiling of the healthy sinonasal microbiome using Pacific Biosciences sequencing of full-length 16S rRNA genes.

Microbiome 6 , Yarza, P. Uniting the classification of cultured and uncultured bacteria and archaea using 16S rRNA gene sequences. Sun, D. Intragenomic heterogeneity of 16S rRNA genes causes overestimation of prokaryotic diversity.

Edgar, R. Bioinformatics 34 , — DeSantis, T. Dewhirst, F. The human oral microbiome. Martin, M. Cutadapt removes adapter sequences from high-throughput sequencing reads. EMBnet J. Article Google Scholar. Introducing mothur: open-source, platform-independent, community-supported software for describing and comparing microbial communities. Methods 10 , Strain-specific colonization patterns and serum modulation of multi-species oral biofilm development.

Anaerobe 18 , — Diaz, P. Using high throughput sequencing to explore the biodiversity in oral bacterial communities. Oral Microbiol. Bolger, A. Trimmomatic: a flexible trimmer for Illumina sequence data. Bioinformatics 30 , — FLASH: fast length adjustment of short reads to improve genome assemblies. Bioinformatics 27 , — Nurk, S. Assembling single-cell genomes and mini-metagenomes from chimeric MDA products. Cole, J. The ribosomal database project: improved alignments and new tools for rRNA analysis.

Nucleic acids Res. Ewing, B. Base-calling of automated sequencer traces using Phred. Error probabilities. Genome Res. Li, H. Bioinformatics 25 , — Sherry, S. Dodt, M. Biology 1 , — Bioinformatics 26 , — CAS Google Scholar. Price, M. FastTree 2 — approximately maximum-likelihood trees for large alignments. Paradis, E. Linking genomics and population genetics with R. Bland, M. Statistics notes: measurement Error. Camacho, C. BMC Bioinformatics 10 , Download references.

Jethro S. Johnson, Daniel J. Leopold, Blake M. Hanson, Hanako O. You can also search for this author in PubMed Google Scholar. Correspondence to Jethro S. Peer Review Information Nature Communications thanks the anonymous reviewers for their contribution to the peer review of this work. Peer reviewer reports are available. Reprints and Permissions. Johnson, J. Evaluation of 16S rRNA gene sequencing for species and strain-level microbiome analysis. Nat Commun 10, Download citation. Received : 09 January Accepted : 16 October Published : 06 November Anyone you share the following link with will be able to read this content:.

Sorry, a shareable link is not currently available for this article. Provided by the Springer Nature SharedIt content-sharing initiative. Maternal Health, Neonatology and Perinatology The effects of freezing on faecal microbiota as determined using miseq sequencing and culture-based investigations.

Fraher, M. Techniques used to characterize the gut microbiota: a guide for the clinician. Gagliani, N. The fire within: microbes inflame tumors. Cell , — Gao, Z. Microbiota disbiosis is associated with colorectal cancer.

Geng, J. Diversified pattern of the human colorectal cancer microbiome. Gut Pathog. Gill, S. Metagenomic analysis of the human distal gut microbiome. PubMed Abstract Google Scholar. Gorzelak, M. Methods for improving human gut microbiome data by reducing variability through sample processing and storage of stool. Hooper, L.

Commensal host-bacterial relationships in the gut. Huse, S. A core human microbiome as viewed through 16S rRNA sequence clusters. Kostic, A. Fusobacterium nucleatum potentiates intestinal tumorigenesis and modulates the tumor-immune microenvironment.

Cell Host Microbe 14, — Genomic analysis identifies association of Fusobacterium with colorectal carcinoma. Genome Res. Liang, Q. Fecal bacteria act as novel biomarkers for noninvasive diagnosis of colorectal cancer. Cancer Res. Loman, N. Performance comparison of benchtop high-throughput sequencing platforms.

Lozupone, C. Meta-analyses of studies of the human microbiota. Marchesi, J. Towards the human colorectal cancer microbiome. Margulies, M. Genome sequencing in microfabricated high-density picolitre reactors. Mira-Pascual, L. Microbial mucosal colonic shifts associated with the development of colorectal cancer reveal the presence of different bacterial and archaeal biomarkers.

Mizrahi-Man, O. Taxonomic classification of bacterial 16S rRNA genes using short sequencing reads: evaluation of effective study designs. Nakatsu, G. Gut mucosal microbiome across stages of colorectal carcinogenesis. Nistal, E. Factors determining colorectal cancer: the role of the intestinal microbiota. Nosho, K. Association of Fusobacterium nucleatum with immunity and molecular alterations in colorectal cancer.

World J. Plummer, E. A comparison of three bioinformatics pipelines for the analysis of preterm gut microbiota using 16S rRNA gene sequencing data. Proteomics Bioinform. Pruesse, E. Qin, J. A human gut microbial gene catalogue established by metagenomic sequencing. Nature , 59— Quast, C. Rubinstein, M. Schloss, P. Introducing mothur: open-source, platform-independent, community-supported software for describing and comparing microbial communities.

Sender, R. Revised estimates for the number of human and bacteria cells in the body. PLoS Biol. Sinha, R. The microbiome quality control project: baseline study design and future directions.

Genome Biol. Fecal microbiota, fecal metabolome, and colorectal cancer interrelations. Sobhani, I.