Ribosomal Rna
Ribosomal RNA (rRNA) is the structural and catalytic core of ribosomes, the molecular machines that translate messenger RNA into protein. This guide is for molecular biologists, bioinformaticians, and clinical diagnosticians who need a rigorous, source based understanding of rRNA: its biology, its use as a phylogenetic marker, analytical workflows, and common pitfalls. Whether you are designing a 16S amplicon study or troubleshooting rRNA contamination in RNA seq, this practical framework will help you make informed decisions.
Ribosomal RNA accounts for 80,90% of total cellular RNA and is encoded by highly conserved genes that contain variable regions. These features make rRNA both a nuisance in transcriptomics (where it must be depleted) and a powerful tool for taxonomic profiling NCBI Bookshelf. The analysis of rRNA sequences, particularly the 16S rRNA gene in prokaryotes and 18S/ITS regions in eukaryotes, has become a standard approach for characterizing microbial communities without cultivation EMBL-EBI Training.
At a Glance
| Aspect | Key Point |
|---|---|
| Definition | Non coding RNA that forms ribosome subunits (small and large) |
| Core function | Peptide bond formation and mRNA decoding |
| Key molecular features | Conserved regions (universal primers) and hypervariable regions (taxonomic resolution) |
| Primary applications | Phylogenetics, microbiome profiling, evolutionary biology |
| Common analysis method | 16S/18S/ITS amplicon sequencing |
| Major challenge | High abundance obscuring low abundance transcripts in RNA seq |
| Quality metric | Percent of reads classified as rRNA after depletion or enrichment |
Decision Criteria: When to Work with rRNA
Choosing to analyze rRNA directly or to remove it depends entirely on your biological question.
Use rRNA sequencing (amplicon) when:
- You need to profile bacterial or archaeal community composition from environmental, clinical, or host associated samples.
- You are performing taxonomic identification of unknown microbes.
- You have limited sample DNA or low microbial biomass, as PCR amplification of rRNA genes is highly sensitive.
Deplete or exclude rRNA when:
- Your goal is transcriptome sequencing (RNA seq) to study gene expression. Ribosomal RNA must be removed to avoid wasting sequencing reads on abundant, uninformative molecules.
- You are doing metatranscriptomics and want to focus on functional genes rather than taxonomic marker genes.
A critical nuance: 16S rRNA gene sequencing targets DNA, not the RNA molecule itself. If you need to measure active microbial transcription, consider rRNA sequencing from RNA (cDNA) or metatranscriptomics. The decision also hinges on resolution. Full length 16S rRNA genes (about 1500 bp) provide better taxonomic classification than shorter variable regions, but short read platforms often use V3 V4 regions for cost effectiveness Galaxy Training Network.
Practical Workflow for rRNA Based Analysis
The following workflow assumes you are performing 16S rRNA amplicon sequencing. Adapt steps for 18S or ITS as needed.
Step 1: Sample Collection and Nucleic Acid Extraction
Use protocols that minimize host DNA contamination (for microbiome studies) and avoid inhibitors. Include a blank extraction control. For RNA based rRNA analysis, use RNase free conditions and reverse transcribe immediately. Document extraction yields and purity.
Step 2: Primer Selection and PCR Amplification
Choose primers targeting your region of interest. For bacteria, the 27F/1492R pair amplifies nearly full length 16S rDNA. For high throughput short read platforms, use region specific primers (e.g., 341F/805R for V3 V4). Validate primers against the SILVA or Greengenes databases to ensure coverage of your expected taxa. Use high fidelity polymerase and limited cycles to reduce PCR bias NCBI Sequence Read Archive. Always include a negative PCR control.
Step 3: Library Preparation and Sequencing
Index your amplicons for multiplexing. Use a sequencing platform that produces sufficient read length for your amplicon size. For 250 bp paired end reads, target amplicons under 500 bp to allow overlap. Sequence on Illumina MiSeq or NovaSeq. Consider using the SRA to deposit your raw data.
Step 4: Bioinformatic Processing
Quality filtering: Remove low quality bases, adapter contamination, and chimeric sequences. Use tools like DADA2 or QIIME2 with default parameters. DADA2 resolves amplicon sequence variants (ASVs) rather than operational taxonomic units (OTUs), providing higher resolution Bioconductor.
Taxonomic assignment: Classify ASVs using a reference database such as SILVA, Greengenes, or GTDB. Use a naive Bayes classifier for speed and accuracy. Assign taxonomy at genus level typically, species level is unreliable for short reads.
Phylogenetic placement: For enhanced accuracy, place ASVs into a reference tree using tools like EPA ng or SEPP. This improves classification of novel or divergent sequences.
Step 5: Statistical Analysis and Visualization
Calculate alpha diversity (Shannon, Chao1) and beta diversity (Bray Curtis, UniFrac). Use rarefaction to account for uneven sequencing depth. Perform differential abundance testing with tools like DESeq2 or ANCOM BC. Visualize results with PCoA plots, heatmaps, and bar charts.
Step 6: Quality Checks Throughout
- Check amplification efficiency via gel electrophoresis before sequencing.
- Monitor read quality scores. Median quality above 30 is desirable.
- After processing, verify that the number of ASVs is reasonable for your sample type (e.g., 100,1000 for human gut).
- Include positive controls (mock communities) and negative controls in the run. Negative controls should yield very few reads, if not, investigate contamination.
Common Mistakes
Ignoring rRNA depletion efficiency in RNA seq. If you do not remove rRNA, 90% of your reads will be useless for gene expression analysis. Always use a validated depletion kit (e.g., Ribo Zero or rRNA probe based) and check the percent rRNA remaining. A post sequencing quality report from FastQ Screen can reveal contamination EMBL-EBI Training.
Treating all rRNA sequences as equally reliable for taxonomy. Variable region choice affects resolution. For example, the V1 V2 region distinguishes some genera poorly compared to V3 V4. Always validate with a known positive control.
Overlooking host rRNA in host associated microbiome studies. If you sequence 16S from a tissue sample, host mitochondrial and chloroplast rRNA (which are prokaryotic in origin) can amplify. Use blockers or perform host read removal bioinformatically. A recent study on temporomandibular joint disorders highlighted bacterial DNA presence after careful correction for host contamination Front Oral Health.
Using pairwise alignment for phylogenetic inference without considering evolutionary models. rRNA genes evolve at different rates across sites. Use a model (e.g., GTR+G) and a likelihood or Bayesian framework. Simpler distance methods can mislead.
Assuming 16S rRNA sequence identity implies functional equivalence. Two strains with 99% 16S similarity can have different metabolic capabilities due to horizontal gene transfer. rRNA is a phylogenetic marker, not a functional one.
Limits and Uncertainty
Ribosomal RNA analysis has inherent limitations that any practitioner must acknowledge.
Resolution limit. The 16S rRNA gene cannot reliably distinguish species in many genera (e.g., Escherichia and Shigella). Whole genome sequencing is needed for species or strain level identification.
Copy number variation. Prokaryotic genomes contain multiple rRNA operons (1 to 15 copies). High copy number organisms can appear overrepresented in 16S sequencing. No easy normalization exists, though copy number databases help.
PCR bias. Universal primers may not amplify all taxa equally. Rare or divergent microbes can be missed. This is a known issue for some archaea and candidate phyla.
Functional inference is indirect. Ribosomal RNA sequences cannot directly tell you about gene expression, metabolic activity, or pathogenicity. Additional methods like metagenomics or metatranscriptomics are needed.
Noise from dead or dormant cells. DNA based amplicon sequencing detects DNA from both living and dead cells. RNA based rRNA sequencing (cDNA) can indicate viable or active populations, but RNA is less stable.
Context dependent interpretation. A recent study on gut microbiota in type 2 diabetes mellitus used 16S rDNA sequencing but emphasized that metabolomic changes cannot be linked solely to rRNA data J Vis Exp. Always integrate with other omics.
Frequently Asked Questions
1. Can I use 16S rRNA sequencing to detect viruses?
No. Viruses do not possess ribosomal RNA. For viral detection, use metagenomic shotgun sequencing or targeted PCR for viral genes.
2. Why is my RNA seq experiment still showing high rRNA reads even after using a depletion kit?
Possible reasons: incomplete probe binding due to sequence variation, degraded RNA that makes rRNA fragments inaccessible, or high starting rRNA load. Run a quality control using a Bioanalyzer. Consider a different depletion method or combine with poly A selection.
3. What is the difference between 16S and 18S rRNA?
16S rRNA is part of the small ribosomal subunit in prokaryotes (30S). 18S rRNA is the homologous molecule in eukaryotes (40S). Both are used for phylogenetic studies but require different primers and reference databases. 18S is larger (about 1900 bp vs 1500 bp) and has different hypervariable regions.
4. Can rRNA mutations cause disease in humans?
Yes. Mutations in human rRNA genes or in the machinery that processes rRNA can disrupt ribosome biogenesis. For instance, truncated mutant NEK1 proteins form nuclear condensates that impede rRNA biogenesis and are linked to motor dysfunction Nat Commun.
References and Further Reading
- NCBI Bookshelf , Comprehensive textbooks on molecular biology and ribosomal structure.
- EMBL-EBI Training , Practical courses on 16S rRNA analysis and metagenomics.
- Galaxy Training Network , Step by step tutorials for amplicon sequencing using QIIME2 and DADA2.
- Bioconductor , R packages for rRNA data analysis (e.g., dada2, phyloseq, microbiome).
- NCBI Sequence Read Archive , Public repository for 16S amplicon and metagenomic sequence data.
- Nuclear condensates formed by truncated mutant NEK1s impede ribosomal RNA biogenesis and drive motor dysfunction , Nat Commun, illustrates the medical relevance of rRNA processing.
- Gut Microbiota and Metabolomic Changes In Type 2 Diabetes Mellitus: Insights From 16S rDNA Sequencing , J Vis Exp, method example with clinical context.
- Correction: Presence of bacterial DNA in synovial fluid from the temporomandibular joint in patients with temporomandibular joint disorders , Front Oral Health, demonstrates host contamination correction.
- Randomized, Double-Blind, Placebo-Controlled Trial of Probiotic Lactiplantibacillus plantarum for Gut-Vaginal Microbiota Modulation , Mol Nutr Food Res, applied 16S analysis.
- Granulomatous inflammation in lung and lymph node specimens: A molecularly enhanced pathology based algorithm , Ann Diagn Pathol, uses rRNA for pathogen identification.