Practical Information about DNA 16S rRNACommunity Analysis Sequencing

What is 16S rRNA 

16S rRNA sequencing is a powerful technique used to identify and classify bacteria and archaea based on their genetic material. It targets the 16S ribosomal RNA gene, which is present in all prokaryotes and contains both conserved and variable regions. 

Common Methodologies 

a. Sanger Sequencing

• Read length: ~700–1,000 bp
• Coverage: Usually, the full-length 16S rRNA gene
• Use case: High-quality, low-throughput; useful for identifying single isolates or pure cultures.
• Pros: Accurate and well-established
• Cons: Not practical for complex or mixed microbial communities

*This is commonly used in pure culture applications rather than community analysis (activated sludge etc.) 

b. Next-generation Sequencing (NGS)

• Platforms: Illumina (most common), Ion Torrent
• Read length: 150–300 bp (paired-end reads can reach 600 bp)
• Use case: Microbiome studies, environmental samples
• Pros: High throughput, cost-effective
• Cons: Shorter reads may limit taxonomic resolution

Common target regions for Illumina NGS:

• V1–V3
• V3–V4 (most popular)
• V4
• V4–V5

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c. Third-Generation (Long-Read) Sequencing

• Platforms: PacBio SMRT, Oxford Nanopore
• Read length: Full-length 16S (~1,500 bp)
• Use case: Improved taxonomic resolution down to species level
• Pros: Long reads, full-gene coverage
• Cons: Higher error rates (being improved), costlier than NGS

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Practical Takeaways

• Not all DNA testing is the same 
• Different methodologies often produce different results
• It is important to know what method you are using if comparing results 
• Separate testing is commonly used for bacteria vs. archaea
  • For example, in anaerobic systems methanogens are archaea and both are recommended 

What is actually measured in rRNA testing? 

Abundance? 

• A useful analogy is that “bugs” have varying numbers of “fingers” with rRNA as a useful analogy for counting “fingers”.
  • Therefore, DNA read% is not always a strong correlation to actual organism abundance 
  • Based on hundreds of DNA/ microscopy split samples, it appears common for as low as 2-3% DNA reads from a genus such as Microthrix to correlate with abundant filaments, while genera such as Thauera (commonly with zoogloea morphology) may often register around 20% with corresponding “zoogloea” bacteria type abundance at common-very common using Jenkins 1-6 ranking scale. 
• In some, but not all instances, trends may be useful, however DNA read % and microorganism abundance are not “apples to apples” comparison based on the variability of rRNA per given microbe. 

Other Potential Factors 

• The faster a microbe grows, the more likely it is to have a higher DNA read%.
• Microbes with higher polysaccharide tend to generally have higher DNA read%. 
• Slow growing filaments, such as filaments within the Chloroflexi phylum, may produce low DNA read% despite high abundance as viewed under the microscope
  • For example, it is common for around 1% of Kouleothrix genus reads to correlate with very common-abundant type 1851 filaments 

Practical Takeaways 

• Correlation with genus reads is likely more representative as a “who’s there” rather than a detailed expression of abundance. 
• Microscopy is a useful tool for “morphotype” (what something looks like) abundance. DNA can compliment this in some respects by confirming “who’s there” in a more detailed/advanced manner at various taxonomic levels.
  • Morphotype/cause correlations have generally held up strongly for over 40 years of operational practice on a large-scale basis, and when taking into account the surrounding microbes viewed within a sample.

DNA: Functional Groups and Morphology 

Microbe Functionality 

The above screenshot is taken from www.midasfieldguide.org/

Interpretation/ Practical Take-Aways 

• When referencing a genus using the MIDAS field guide, there are highlighted regions for positive, variable, negative, and unknown functionalities
  • Many genus have variable potential functionalities, so therefore just because a microbe is capable of doing something (often at bench scale vs. in situ), this does not positively confirm the role it is actually serving in biological wastewater treatment 
  • Most genus capable of filamentous form are labeled “variable” as they may or may not have filamentous morphology as seen under the microscope
  • Many genus have overlapping potential functionality such as PAOs that may also be GAOs etc. (i.e., Ca Accumulibacter)
    • Complexities may be further amplified as many geniuses are capable of oxidation and/or fermentation reactions and even in instances such as enhanced biological phosphorous removal, many of these genus also have the possibility for functionalities such as denitrification.
• DNA is an emerging and growing field of research, and some genus have more available recognized information than others at the time of this writing (2025). 
• In practice, it is common that varying % of reads do not reach low taxonomic levels (this will be discussed in further detail later within this article). 

What’s in a Name? 

It is an important consideration that there is not a universally accepted database for microbe taxonomic classification. Personally, the MIDAS field guide Midas field guide – Search Microbes appears to be the most practical resource, however other credible and recognized sources include: 

• NCBI
• LPSN
• GTDB
• SILVA
• RDP
• EzBioCloud
• BacDive

Taxonomy, genus names, and more may differ depending on what database is used so this is important information to keep into context 

Subsequently, on the microscopy side, there is generally broad potential genetic diversity within various recognized morphological description. For example, on the screenshot above there are 46 recognized species of Thiothrix, meaning even within the genus level, they are not always the same. There are also many closely related microbes, that most likely are capable of displaying “Thiothrix” morphology, however, do not actually belong to the Thiothrix genus. 

Basic Information on Taxonomy 

Reads in this instances cover kingdom, phylum, class, order, family, and genus and read as low on the chart as accurately determined by the program. Blank spaces indicate that the margin of error was not within the designated range for confident classification at a particular taxonomic level. 

Practical Takeaways 

• DNA is an evolving field and as more knowledge is obtained occasional changes to classifications will occur.
• As technology is improving the criteria for genus classification in many instances is narrowing. It is important to not only consider the genus reads, but also higher taxonomic orders for instances in which functionality at these higher orders may capture a wider range of the “big picture” conditions. 
• Many DNA reports on functional groups may be based on genus level reads, and it is important to take into consideration that there may be varying amounts of reads that do not reach the genus level. 
• There are some phyla in which general functionalities/morphotypes appear to be consistent (i.e., Chloroflexi as slower growing filamentous bacteria, many capable of storing substrate under anaerobic conditions) and also other phyla and taxonomic levels which may have significantly more diversity amongst functionality/ morphology etc. (i.e., Nitrosomonas and Thiothrix belong to the same phylum.) 

Benefits and Limitations of Current DNA testing 

Benefits

• Repeatable and non-subjective provided known information about testing methods, databases used etc. 
• Databases are growing at a high rate in which more information is being learned about microbes at bench scale, in pure culture, and in situ.
• While read% is not always a strong and reliable comparison to microbe abundance as viewed under the microscope, trends are effective in many instances depending on given objectives.
• Technology is advancing and pricing as well as turn-around time for DNA testing is becoming more practical and logistical. 

Limitations

• DNA testing does not provide reliable context to conditions such as floc structure characteristics (i.e., firm vs. diffuse floc structure), dispersed growth abundance, and bacterial health/viability. 
• Higher life form organisms (i.e., protozoa, metazoan) are other organisms such as Fungi not bacterial and require different methodologies than community analysis for bacteria. 
• There is variable functionality and morphology amongst a high % of genus. DNA is an excellent tool for establishing if a microbe is present, however if morphology (i.e., filamentous vs. non-filamentous) and functionality are variable, other tools such as microscopy and in-house analytical testing may be needed to help take this information into context (for example, Acinetobacter genus is commonly correlated with type 1863 filaments, however, doesn’t always display type 1863 filament morphology under the microscope). 

Practical Takeaways

• Tools such as microscopy, in-house analytical testing, DNA testing, and the intuition of operators familiar with treatment plant behavior are all relevant and useful tools. Gathering information from multiple sources of these “tools” gives us the higher levels of baseline data to use for troubleshooting and operational process control insight.
• Tools and methodologies are only as good as the quality in which they are obtained. It is also important to know the context and the other potential variables for the best chances of practical application for these tools.