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While whole genome sequencing provides a comprehensive view of all the genetic variations within a sample, amplicon sequencing focuses on sequencing specific genomic regions (like the 16s rrna rRNA gene). This targeted approach makes amplicon sequencing more cost-effective than whole genome sequencing.
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Amplicon | Whole genome | |
|---|---|---|
Dataset size | Very small | Medium to very large |
Computational resources | Small | Medium to very large |
Price | Low | Medium to high |
Taxonomic resolution | Mostly genus | Species or strain |
Functional analysis | Limited | Greater detail |
Database curation | Detailed | Minimal |
Taxonomic coverage | Specific (e.g. 16s = bacteria) | All taxa |
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Analysis of the microbiome: Advantages of whole genome shotgun versus 16S amplicon sequencing
Our study demonstrates that whole genome shotgun sequencing has multiple advantages compared with the 16S amplicon method including enhanced detection of bacterial species, increased detection of diversity and increased prediction of genes. In addition, increased length, either due to longer reads or the assembly of contigs, improved the accuracy of species detection.
Full length amplicon vs hypervariable regions
The 16S rRNA gene is about 1,500 bases in length. Illumina reads are much shorter than this, therefore amplicon analysis typically involves sequencing 2-3 ‘hypervariable’ 16S regions.
https://nanoporetechwww.nature.com/resource-centrearticles/epi2mes41598-16s023-workflow-real-time-identification-bacteria-and-archaea30764-z
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Different groups of bacteria are better represented by specific regions.
With the development of 3rd generation long read sequencing, such as Nanopore and PacBio, the full 16S length can be sequenced. This reduces bias and improves taxonomic resolution.
https://nf-co.re/ampliseq/2.9.0/docs/usage#taxonomic-classification
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www.nature.com/articles/s41598-020-80826-9
NOTE: For eukaryotic (e.g. fungi) metagenomic amplicon sequencing, ITS (Internal transcribed spacer) regions are used.
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https://data.eresearchqut.net/paulw/public/mahsa_manuscript2/index.html
ASV vs OTU
Taxonomic assignments in nfcore/ampliseq are based on Amplicon sequence variants (ASV), inferred using the DADA2 software package by matching the sample sequences to the SILVA ribosomal RNA sequence database.
DADA2 infers sample sequences exactly and resolves differences of as little as 1 nucleotide.
SILVA provides comprehensive, quality checked and regularly updated datasets of aligned small (16S/18S, SSU) and large subunit (23S/28S, LSU) ribosomal RNA (rRNA) sequences for all three domains of life (Bacteria, Archaea and Eukarya).
Traditionally Operational Taxonomic Units (OTUs) have been used in 16S ampicon studies. More recently ASVs have been used, due their improved accuracy in identifying taxa, particularly genus and species. Basically, OTUs utilise a similarity clustering method to identify taxa, whereas ASV are generated by quantifying exact sequence matches to an amplicon database (e.g. Silva or Greengenes) and then statistically adjusting this using confidence thresholds.
The OTU method typically can identify 97% similarity (with any accuracy) whereas the ASV method can identify even single base-pair differences. This enables a finer resolution of taxa down to the genus and species level. Note that there is increasing ‘fuzziness’ toward the lower taxonomic levels, as the diversity within some taxa is greater than the diversity between this and other taxa (in other words, even with ASV, not all taxa can be resolved to lower taxonomic levels and this is highly dependent on the taxonomic group involved).
https://www.zymoresearch.com/blogs/blog/microbiome-informatics-otu-vs-asv
Illumina vs Nanopore sequencing technologies
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It’s important to note that a significant difference between 2nd and 3rd generation technology is accuracy. The error rate (i.e. the number of bases with low sequencing quality scores) of 3nd 3rd gen has been considerably higher than 2nd gen, with typically ~0.1% error rate for Illumina sequences and >5% error rate for Nanopore. The Nanopore error rate has improved dramatically in recent years though, but still is considerably lower than Illumina. This higher error rate can cause issues, such as in metagenomics when identifying species that differ by a small number of base pairs.
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