Which is the reference 16S rRNA?

Which is the reference 16S rRNA?

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Recently, I've stumbled upon a fact, which hasn't bothered me for many years. The fact is that all universal 16S primers are written as "[FR][0-9]+" (in regex notation), that is they have a position with respect to a reference. I've read through many papers, wherein these primers were introduced, and most of the time the authors say nothing but "E. coli 16S". Anyway, in one case I've found that it is in fact the reference K12 E. coli. But the problem is that it has 7 distinct rRNA operons: rrnA, rrnB, rrnC, rrnD, rrnE, rrnF, rrnG. Do you have a reference showing a particular operon used for the 16S position notation?


Figure 2. Hypervariable regions within the 16S rRNA gene in Pseudomonas . The plotted line reflects fluctuations in variability amongst aligned 16S rRNA gene sequences of 79 Pseudomonas type strains… (Bodilis et al., 2012)

As you correctly point out, designing an optimal primer pair for 16S-rRNA sequencing is a tricky affair because even the less-variable regions are not same between different strains and species. Sambo et al (2018) have even developed a bioinformatics software for optimal design of primers for 16S-rRNA sequencing for multiple bacteria.

We propose here a computational method for optimizing the choice of primer sets, based on multi-objective optimization, which simultaneously: 1) maximizes efficiency and specificity of target amplification; 2) maximizes the number of different bacterial 16S sequences matched by at least one primer; 3) minimizes the differences in the number of primers matching each bacterial 16S sequence. Our algorithm can be applied to any desired amplicon length without affecting computational performance.

There is diversity in the 16S-rRNA genes within the same species i.e. the different copies are not exact duplicates (Větrovský & Baldrian, 2013).

Větrovský & Baldrian (2013)

Interestingly, the different rRNA operons in E.coli have different promoters and are even differentially expressed during stress conditions (Kurylo et al., 2018).

However, Kitahara et al. (2012) found that 16S-rRNA genes isolated from soil samples, in place of the original E.coli gene, could support its growth. In other words E.coli is highly robust to mutations in its 16S-rRNA.

After counterselection, ∼200 clones of KT103-derivatives (carrying pRB103 whose 16S rRNA gene was substituted with foreign genes) were obtained, from which 33 nonredundant 16S rRNA genes (A01-H03) were identified. Through multiple alignment of E. coli 16S rRNA and our metagenomically retrieved 16S rRNA sequences, it was found that at least 628 (40.7%) of the 1,542 nucleotides were variable, indicating marked mutational robustness of the 16S rRNA. Strikingly, the functional 16S rRNA sequences (except A10 and F02, which were 99.0% identical to E. coli 16S rRNA) obtained in this study showed only 80.9-89.3% identity to E. coli 16S rRNA, which was well below the value reported thus far (Proteus vulgaris 16S rRNA, 94% identity to E. coli 16S rRNA)

For your question

But the problem is that it has 7 distinct rRNA operons: rrnA, rrnB, rrnC, rrnD, rrnE, rrnF, rrnG. Do you have a reference showing a particular operon used for the 16S position notation?

I don't think any one of them is considered the reference. The complete E.coli 16S-rRNA sequence reported in NCBI seems to consider a "consensus" of different reported sequences:

[5], [7] contain updated sequence data for the original work by the same laboratory [4]. There were too many discrepancies between [4] and [5], [7] to list each revision in our sites table. The sequence shown is from [7]. [4], [5], [7] point to a number of cistron heterogeneities. There is uncertainty, however, with regard to assigning these various heterogeneities to specific cistrons. The RNA method used by [4], [5], [7] gives the average of all the cistrons present in the cell [7]. The heterogeneities are classified by their relative proportions into major, minor and undetermined species. The sequence shown corresponds to the major species. The heterogeneities were annotated as variations in the sites table. It is not known which of the residues 'c' (base 633) or 'a' (base 641) undergoes a deletion, giving rise to the minor component 'atctg'. [7] suggests the existence of one or two mutated cistrons among the known seven cistrons of ribosomal RNA. With the exception of a single base deletion, this sequence is identical to the current 16S rDNA sequence for the E.coli rRNB gene.

The NCBI page also lists different polymorphisms and base modifications in the 16S-rRNA within and between different strains/"species".

NCBI has several partial sequences as well. When I checked for P.aeruginosa and B.subtilis, I could find only one or two complete sequences (rest were partial). Moreover, each entry indicated the strain that it was obtained from. Therefore I assume there is no single reference sequence.

I guess that people do consider the different variants while doing phylogenetic analysis (or simply just look for conserved "signature" sequences for a given species). I am sure that there are computational algorithms for doing the classification in an optimal manner (see Chatellier et al., 2014). Since, I don't have an expertise in this area I cannot say anything conclusively about the routine practice. In fact there is still ongoing research in improving the analysis (for example, Yang et al., 2016 and Sambo et al., 2018).

  • This method is effective in identifying general group differences of pathogens.
  • 16S rRNA gene sequences contain hypervariable regions that can provide species -specific signature sequences useful for bacterial identification.
  • This method can also be fine-tuned to identify pathogens at the species level.
  • ribosomes: Large and complex molecular machine, found within all living cells, that serves as the primary site of biological protein synthesis.

Sixteen S ribosomal RNA (or 16S rRNA) is a component of the 30S small subunit of prokaryotic ribosomes. It is approximately 1.5kb (or 1500 nucleotides) in length. The genes coding for it are referred to as 16S rDNA, and are used in reconstructing phylogenies. Multiple sequences of 16S rRNA can exist within a single bacterium.

Figure: Ribosomal RNA: Structure and shape of the E.coli 70S ribosome. The large 50S ribosomal subunit (red) and small 30S ribosomal subunit (blue) are shown with a 200 Ångstrom (20 nm) scale bar. For the 50S subunit, the 23S (dark red) and 5S (orange red) rRNAs and the ribosomal proteins (pink) are shown. For the 30S subunit, the 16S rRNA (dark blue) and the ribosomal proteins (light blue) are shown.

The 16SrRNA gene is used for phylogenetic studies, as it is highly conserved between different species of bacteria and archaea. Carl Woese pioneered this use of 16S rRNA. In addition, mitochondrial and chloroplastic rRNA are also amplified. Unfortunately, while primers can be defined to amplify this gene from single genomes, this method is not accurate enough to estimate the diversity of microbial communities from their environments. Principal limits are the lack of real universal primers DNA amplification biases and reference database selection impact the annotation of reads.

Paradoxically, methodological denial is now a rule in published articles that use 16S rRNA gene amplicon surveys to study unknown microbial communities. In these articles, one pair of primers (although many of them are designed, and provide different results) is used to amplify a region of the 16S rRNA gene. In addition to highly conserved primer binding sites, 16S rRNA gene sequences contain hypervariable regions that can provide species-specific signature sequences useful for bacterial identification. As a result, 16S rRNA gene sequencing has become prevalent in medical microbiology as a rapid and cheap (while inaccurate) alternative to phenotypic methods of bacterial identification. Although it was originally used to identify bacteria, 16S sequencing was subsequently found to be capable of reclassifying bacteria into completely new species, or even genera. It has also been used to describe new species that have never been successfully cultured.

What is 16S rRNA

16S rRNA is a type of rRNA responsible for making up the small subunit of the prokaryotic ribosome. Significantly, the size of the 16S rRNA is 1542 nt. Generally, it has a structural role similar to the rRNA in the large subunit to scaffold ribosomal proteins in defined positions. Moreover, it facilitates the binding of the small subunit to the large subunit by interacting with the 23S rRNA in the large subunit.

Figure 1: 16S rRNA Structure

Furthermore, the three-dimensional structure of the 16S rRNA consists of four domains. Also, the 3′ end of the 16S rRNA contains an ti- Shine-Dalgarno sequence , which can bind to the Shine-Dalgarno sequence of the mRNA to be translated by the ribosome. Generally, on the mRNA, this sequence occurs around the eight bases upstream of the start codon, AUG. On the other hand, 16S rRNA is responsible for stabilizing the pairing of codon and anticodon in the A-site of the ribosome.

New ribosomal RNA BLAST databases available on the web BLAST service and for download

We have a curated set of ribosomal RNA (rRNA) reference sequences (Targeted Loci) with verifiable organism sources and current names. This set is critical for correctly identifying and classifying prokaryotic (bacteria and archaea) and fungal samples (Table 1). To provide easy access to these sequences, we recently added a separate rRNA/ITS databases section on the nucleotide BLAST page for these targeted sequences that makes it convenient to quickly identify source organisms (Figure 1)

Table 1. NCBI curated targeted rRNA sequences now available as BLAST databases.

Figure 1. The database selection menu on the nucleotide-nucleotide BLAST page with the rRNA/ITS database radio button selected.

Using these databases for identification will speed up your searches and provide you the most informative results. If you want to expand your search to include non-curated 16S rRNA sequences, change the to the Nucleotide collection (nr/nt) database. You may also want to set the Organism filter to your taxonomic group of interest.

You can also download these new databases from the BLAST db FTP directory for use in local BLAST searches.

Next Generation 16S rRNA Sequencing

Many sites of the human body are colonized by complex communities of microbes (the "human microbiome") in both health and various disease states. Highly diverse, polymicrobial specimens are often difficult, or even impossible, to fully characterize by techniques in common clinical use:

Figure 1. Examples of conventional 16S rRNA gene sequencing results from a bacterial isolate and a polymicrobial specimen. For the bacterial isolate (top), Sanger sequence data produces a clean electropherogram that can be used to provide a species-level taxonomic classification. For the polymicrobial sample (bottom), Sanger sequencing generates a different electropherogram for each species present, resulting in mixed signal which is uninterpretable.
  • Culture-based identification relies upon the ability of organisms to grow and replicate in vitro. Therefore, detection of fastidious or slow-growing organisms, or those rendered inviable due to processing (such as in formalin-fixed paraffin embedded tissue specimens) or during storage (such as anaerobes which have been exposed to oxygen) is limited. Furthermore, only a limited number of species can be practically classified by this approach.

Figure 2. High-powered magnification of a next-generation sequencing run. Each fluorescent spot represents an individual DNA molecule undergoing sequencing. The color of the spot indicates the identity of the nucleotide being interrogated during the current sequencing cycle. Image from Shendure, Porreca et al. Science (2005).

In contrast to conventional approaches, next-generation DNA sequencing (alternatively termed "NGS", "high-throughput sequencing", "massively parallel sequencing", or "deep sequencing") provides independent sequence data from millions of individual DNA molecules (Figure 2), allowing each fragment to be classified independently.

This unique ability extends upon the advantages of current molecular methods by allowing us to catalog the organisms present within even very complex polymicrobial bacterial communities, directly from patient specimens.

Information on Available Assays

Our lab currently offers high-fidelity Illumina next-generation DNA sequencing of clinical specimens which contain multiple bacterial DNA templates. Methods are validated for the purpose of clinical molecular diagnosis and patient care. Research services are also available - please contact us for additional information.

This test is available as reflex testing for specimens which are expected to be polymicrobial based on broad range bacterial PCR.

Contact [email protected] for details or questions.

Clinical Reporting

Upon completion of testing, a report is issued describing the results of 16S next generation sequencing. To view a sample report, click here or the thumbnail at left.

For additional information on how to submit a request and recieve a report, please contact us!

Which is the reference 16S rRNA? - Biology

This training will start with the presentation of a 16S pipeline ( in a Galaxy environment.

This will enable the preprocessing of the data going from raw reads to taxonomic tables and phylogenetic trees.

The 2nd part of the training will give an overview of numerical ecology and takes part entirely in R.

Processing and statistical analysis of 16S rRNA metagenomic experiments using R & Lotus pipeline.
The following analytical steps from raw data to communicable results will be on the program:

  • Metagenomic experiment planning, execution and analysis
  • Analyzing amplicon sequencing data with up-to-date tools and how to increase the quality of ASVs and OTUs, when to use what technique
  • Post-clustering of ASVs to avoid intra-genomic ASV false-positives
  • Working with low-biomass experiments, removing exogenous contamination in several scenarios
  • Numerical ecology focused analysis workflow for both metagenomic and amplicon sequencing based studies
  • Analysis of alpha/beta diversity
  • Univariate statistics to identify species enriched in sample groups
  • Multivariate statistics and visualization to identify clusters of samples
  • Assessing microbial communities based on ecological principles and species distributions

The training is intended for people who have some experience with R. If you have no experience with R, you can follow our R introduction training first.


Falk Hildebrand

Falk Hildebrand is a bioinformatician with a passion for microbial ecosystems, bacterial evolution and developing computational systems to tackle these subjects in a combined perspective. Falk joined Earlham Institute in early 2019, where his new Hildebrand Group (also at Quadram Institute) will be developing metagenomic tools for tracking bacterial strains at high resolutions, to predict their genomic capabilities and explore their associations to diseases.

During his early career, Falk was working at the University of Constance (Germany) and the University of Sussex (UK) on bacterial evolution and tracking outbreaks of pathogens through following changes in their genomes. Moving from a genome centric view to a metagenomic view of whole microbial ecosystems, he worked during his PhD at the University of Brussels (Belgium) on bacterial associations to complex diseases such as IBD, obesity and Diabetes. For this, Falk developed bioinformatic pipelines to process 16S data (LotuS) as well as the statistical tools.

During his postdoc at EMBL Heidelberg (Germany), Falk continued his research on association to the human microbiome of complex diseases such as Diabetes and Parkinson’s disease, among others. His interests soon developed to track strains in the metagenomic datasets, as well as de novo assembling genomes from metagenomes. These techniques are mostly applied to the gut microbiome of mammals. Another interest of his is environmental metagenomics. For example, in 2018 he proved that in soils more fungi are usually connected to more antibiotic resistance in bacteria, and this is true on a local but also global scale.

Ezgi Özkurt

Ezgi was born in Istanbul, Turkey. She studied Molecular Biology and Genetics at the Middle East Technical University, Ankara and received her B.Sc. in 2014. She continued her education at the faculty of Biology in the same university where she obtained her M.Sc. diploma in 2015.

Ezgi followed her interest for evolutionary biology and continued her studies at the Max Planck Institute for Evolutionary Biology in Germany, working within the Environmental Genomics group as PhD student. She obtained her PhD degree in 2020.

Ezgi is currently working as a PostDoc in Hildebrand group. Her main interests are metagenomics data analysis and vertical transmission of microbial communities.

Joachim Fritscher

Joachim Fritscher studied Bioinformatics in his BSc and MSc. He got into metagenomics at the end of his MSc and wrote his thesis about a k-mer based taxonomic classifier for metagenomic reads using a protein reference.

Now Joachim is trying to use the k-mer based approach as well as other methods to improve the resolution of taxonomic classification and SNP calling to strain level. He's also striving to learn and apply new methods on biological data and making them performant with C++.

Stefano Romano

Stefano Romano studied at the “Sapienza” University of Rome and completed his PhD at the Max Planck Institute for Marine Microbiology in Bremen, Germany. Before moving to the Quadram Institute Bioscience, he worked at the University College Cork in Ireland and at the University of Vienna, Austria.

Stefano's career path follows the view of the Chinese philosopher Lao Tzu, who said “Life is a series of natural and spontaneous changes”. He has been involved in several projects investigating marine and freshwater microorganisms, free-living and symbiotic bacteria, basic microbiological aspects and biotechnological applications. All these topics fell under his main interest of understanding the mechanisms bacteria use to interact with their host and its microbiota in a changing environment.

At the Quadram Institute Stefano Romano studies the role of the human-microbiota in aging, focusing particularly on the microbiota-brain-gut axis to understand the interaction mechanisms underpinning host-microbiota interaction and to translate these findings into improved therapeutic interventions. He's also the manager of the Fecal Microbiota Transplant facility in collaboration between the Quadram Institute Bioscience and Norfolk and Norwich University Hospital.

Ángela Del Castillo Izquierdo

Ángela Del Castillo Izquierdo is a MSc by Research post-graduate student within the Hildebrand group as part of the Gut Microbes and Health ISP. She's investigating the microbial composition from a multikingdom perspective across different diseases. Her research interests include microbial ecology and evolution, population genetics and bacterial pathogenesis.

Prior to joining QIB she received my BSc (Hons) Biology from the University of York in 2019, where she undertook a project investigating the DNA segregation factors involved in plasmid segregation and carry out a dissertation studying the mechanism responsible for the segregation of a low copy number plasmid in Sulfolobus solfataricus archaeon at Barillà group

Rebecca Ansorge

Rebecca Ansorge studied at the University of Bremen, Germany where she did a BSc in Biology, and a MSc in Microbiology at the International Max Planck Research School of Marine Microbiology. She obtained her PhD from the Max Planck Institute for Marine Microbiology in 2019, studying animal-microbe symbioses mostly using metagenomics. She is fascinated by strain diversity, and its implications in host-microbe systems and interactions.

Rebecca is currently a PostDoc in the Hildebrand group at Quadram Institute Bioscience, deepening her research on microbial strain-level variation and microbial pangenome diversity in the human gut microbiome. She is particularly interested in illuminating the relevance of this fine-scale diversity in health and disease

Nicola Soranzo

Nicola Soranzo works at the Earlham Institute (EI) since 2014 as part of the Data Infrastructure and Algorithms group. He manages a Galaxy web server used to run large-scale bioinformatics analyses in an accessible, reproducible and transparent way. Nicola has been collaborating for many years on the open source development of the Galaxy platform and of Galaxy tools and workflows.
He is co-author of over 25 peer-reviewed journal papers and regularly delivers training for the Carpentries and the Galaxy Training Network.
Since 2018 Nicola is also Technical Coordinator for ELIXIR-UK, the UK Node of ELIXIR (a pan-European organization coordinating life science resources into a single research infrastructure). Additionally, Nicola is one of the leaders of the Galaxy Community in ELIXIR.
Prior to joining EI, Nicola completed a master's degree in Computer Science at the University of Udine and a PhD in Functional and Structural Genomics at SISSA, Trieste, then has worked for 5 years on Systems Biology and Galaxy at CRS4, Italy.

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Author information


Carl R. Woese Institute for Genomic Biology, University of Illinois at Urbana-Champaign, Champaign, IL, USA

Nam-Phuong Nguyen, Tandy Warnow & Bryan White

Department of Bioengineering, University of Illinois at Urbana-Champaign, Champaign, IL, USA

Department of Computer Science, University of Illinois at Urbana-Champaign, Champaign, IL, USA

Center for Bioinformatics and Computational Biology, University of Maryland, College Park, College Park, MD, USA

Much of microbial taxonomy and metagenomic analyses nowadays are based on studies of the bacterial 16S ribosomal RNA gene (16S). 16S rRNA is a component of the 30S small subunit of the prokaryotic ribosome, which is an essential gene in all bacteria and archaea. Its corresponding gene is about 1500 bases in length, and it has both highly conserved and variable regions, which makes it an ideal taxonomic marker. Usually, primers that are designed to match conserved regions in the 16S gene are used to determine the complete DNA sequence via PCR amplification. The sequence of variable regions between the primers provides sufficient taxonomic information to match each amplified DNA fragment to a database of known 16S sequences and provides a putative identification for the organism that contained that DNA fragment in its genome. Most, if not all, bacteria can be differentiated by sequencing amplicons derived from one of the following variable regions: V1-V3, V3-V4 or V3-V5.

For study of metagenomics, 16S sequencing has great strength, since this method automatically selects only bacterial DNA for amplification and sequencing and it provides an extremely efficient taxonomically informative small marker sequence to measure the abundance of each taxon. Several tools have been developed for determining 16S metagenome sequences classification, such as SINA built into SILVA, Classifier in RDP and QIIME. The later QIIME wraps many packages which can be used to handle multiple processes and therefore has become the dominant tool.

16S rRNA Sequencing Services at Creative Biogene

Creative Biogene offers sequencing service for 16S rRNA gene from your bacterial or fungal colonies rapidly and effectively. To identify the microbial community in your sample, we provide one-stop service performing DNA extraction, PCR, sequencing and assembly to save your time at the bench.

Creative Biogene also provides services of advanced bioinformatic analysis, including i) 16S rRNA metagenome analysis ii) Taxonomical assignment and read abundance estimation for all OTUs down to species level iii) Abundance estimation of bacterial and archaeal OTUs considering lineage-specific copy numbers of marker genes. As an additional service, Creative Biogene will also isolate and purify the high molecular weight DNA from various starting material.

• 16S rRNA gene sequencing
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1. Hao, Y., Pei, Z., Brown, S.M. (2017) Bioinformatics in Microbiome Analysis. Methods in Microbiology. 44: 1-18.

Figure 1 Schematic representation of 16S rRNA of Escherichia coli.
(Methods in Microbiology 2017)

Principles and Workflow of 16S/18S/ITS Amplicon Sequencing

This article shows what 16S/18S/ITS amplicon sequencing is and how it works. Let’s get ready to learn.

16S/18S/ITS amplification sequencing uses the next/third generation sequencing platform and performs high throughput sequencing of PCR products from specific regions such as 16S rDNA/18S rDNA/ITS/ functional genes. It overcomes the disadvantage of some microorganisms that is difficult or impossible to culture, and obtains the information of microbial community structure, evolutionary relationships and microbial correlation with environment in environmental samples.

What is 16S rDNA /18S rDNA/ITS?

  • 16s rDNA: 16S rDNA is a DNA sequence encoding small subunit rRNA of prokaryotes with a length of about 1542bp. With a moderate molecular size and low mutation rate, 16S rDNA is the most commonly used marker in the study of bacterial systematics. The 16S rDNA sequence consists of 9 variable regions and 10 conservative regions, the conserved region sequences reflect the genetic relationships between species, while the variable region sequences reflect the difference between species. 16S rDNA sequencing is mainly used to analyze the diversity of bacteria or archaea.

Fig.1 16S rDNA and amplification primers

  • 18S rDNA: 18S rDNA is a DNA sequence encoding small subunit rRNA of eukaryotic ribosomes. Like 16S rDNA, 18S rDNA sequence also consists of conservative regions and variable regions (V1-V9, absence of V6). Among variable regions, V4 has the most complete database information and the best classification effect, it is the mostly used and the best choice for 18S rRNA gene analysis notes. 18S rDNA sequencing reflects the species differences among eukaryotic organisms in given samples.

Fig.2 18S rDNA and amplification primers

  • ITS: ITS (Internal Transcribed Spacer) is part of the non-transcriptional region of the fungal rRNA gene. The ITS sequences used for fungal identification usually include ITS1 and ITS2. Because in fungi, 5.8S, 18S, and 28S rRNA genes are highly conserved, whereas ITS can tolerate more mutations in the evolutionary process due to less natural selection pressure, and exhibits extremely wide sequence polymorphism in most eukaryotes. At the same time, the conservative type of ITS is relatively consistent within species, and the differences between species (or ever stains) are obvious. ITS sequence fragments are small (350 bp and 400 bp in length, respectively) and easy to analyze. They have been widely used in phylogenetic analysis of different fungi.

Fig.3 ITS and amplification primers

What is 16S/18S/ITS amplicon sequencing?

16S/18S/ITS amplicon sequencing uses Illumina or PacBio sequencing to read the PCR products which are amplified with suitable universal primers of one or several regions of 16S/18S/ITS. By detecting the sequence variation and abundance of the target area, the information of species classification and abundance, population structure, phylogenetic evolution and community comparison of environmental samples could be obtained.

How to conduct a 16S/18S/ITS amplicon sequencing?
In short, the main steps of 16S/18S/ITS amplicon sequencing include library construction, sequencing and bioinformatics analysis.

    : We recommend the fusion primer library construction method, that is, the primers fused with the target sequence primers and the adapter, index and other sequences are synthesized in advance, then the genomic DNA targets are directly amplified by PCR. Amplicon libraries are purified and an equimolar pool of the amplicon libraries is prepared. The dilution required for template preparation is determined and followed by sequencing.
  • Sequencing: The current sequencing platforms mainly include Illumina Miseq/HiSeq and third-generation sequencing platform.
    • Illumina NGS (MiSeq/HiSeq2500/HiSeq4000): Due to the limitation of reading length, the NGS platform can only select a single variable region, double variable regions or triple variable regions as the target regions for the sequencing. When sequencing, only the completely sequenced Reads (Tags) can be used for further analysis, so different amplification regions should strictly follow the corresponding sequencing strategy. For example, if you chose V4 for analysis, the PE250 sequencing is needed, but for V1-V3 regions, the sequencing strategy should be PE300. Only in this way can the completeness of sequences be ensured. The original data is filtered out to remove low-quality reads and leave high-quality clean data for later analysis.
    • PacBio SMRT Sequencing: Unlike NGS, the third generation sequencing platform can carry out full-length sequencing for 16S/18S/ITS, and it’s sequence alignment rate and identification accuracy rate are higher than that of the NGS.

    Operational taxonomic units (OTUs) are often used to classify groups of closely related individuals. In general, if the similarity of different 16S rDNA/18S rDNA/ITS sequences is higher than 97%, those sequences can be defined as an OTU. Each OTU corresponds to a different 16S rDNA/18S rDNA/ITS sequence, that is, each OTU corresponds to one species. By OTU analysis, the microbial diversity and the abundance of different microorganisms in the sample can be known.

    Then based on OTU and species annotation results, sample species complexity analysis and species difference analysis are conducted. Species based analysis, LDA effect size analysis and more analysis are provided too.

    Fig.4 The workflow of 16S/18S/ITS amplicon sequencing

    At CD Genomics, our expert team with extensive experience can help you fully understand microbial communities and take advantage of them. In addition to 16S/18S/ITS Amplicon Sequencing, we also provide other microbial genomics services, including:

    1. Michelsen, C. F., Pedas, P., Glaring, M. A., Schjoerring, J. K., & Stougaard, P. (2014) ‘Bacterial diversity in greenlandic soils as affected by potato cropping and inorganic versus organic fertilization’, Polar Biology, 37(1), 61-71.
    2. Edwards, J., Johnson, C., Santosmedellín, C., Lurie, E., Podishetty, N. K., & Bhatnagar, S., et al. (2015) ‘Structure, variation, and assembly of the root-associated microbiomes of rice’, Proceedings of the National Academy of Sciences of the United States of America, 112(8), E911.
    3. Evans, C. C., Lepard, K. J., Kwak, J. W., Stancukas, M. C., Laskowski, S., & Dougherty, J., et al. (2014) ‘Exercise prevents weight gain and alters the gut microbiota in a mouse model of high fat diet-induced obesity’, Plos One, 9(3), e92193.
    4. Shehab, N., Li, D., Amy, G. L., Logan, B. E., & Saikaly, P. E. (2013) ‘Characterization of bacterial and archaeal communities in air-cathode microbial fuel cells, open circuit and sealed-off reactors’, Appl Microbiol Biotechnol, 97(22), 9885-9895.
    5. Man, K. C., Au, C. H., Chu, K. H., Kwan, H. S., & Chong, K. W. (2010) ‘Composition and genetic diversity of picoeukaryotes in subtropical coastal waters as revealed by 454 pyrosequencing’, Isme Journal, 4(8), 1053.
    6. Lie, A. A. Y., Liu, Z., Hu, S. K., Jones, A. C., Kim, D. Y., & Countway, P. D., et al. (2014) ‘Investigating microbial eukaryotic diversity from a global census: insights from a comparison of pyrotag and full-length sequences of 18s rrna genes’, Appl Environ Microbiol, 80(14), 4363-4373.
    7. Lu, L., Yin, S., Liu, X., Zhang, W., Gu, T., & Shen, Q., et al. (2013) ‘Fungal networks in yield-invigorating and -debilitating soils induced by prolonged potato monoculture’, Soil Biology & Biochemistry, 65, 186-194.
    8. Orgiazzi, A., Lumini, E., Nilsson, R. H., Girlanda, M., Vizzini, A., & Bonfante, P., et al. (2012) ‘Unravelling soil fungal communities from different mediterranean land-use backgrounds’, Plos One, 7(4), e34847.
    9. Lakshmanan, V., Ray, P., & Craven, K. D. (2017) ‘Rhizosphere sampling protocols for microbiome (16s/18s/its rrna) library preparation and enrichment for the isolation of drought tolerance-promoting microbes’, Methods Mol Biol, 1631, 349-362.

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    Watch the video: Using a 16S rRNA Sequence to Identify a Bacterial Isolate (December 2022).