Microbial Community Analysis Using Amplicon Sequencing

Amplicon sequencing has revolutionised the study of microbial communities by enabling high‐throughput, targeted interrogation of taxonomically informative genetic markers such as 16S rRNA, ITS and other functional gene regions. By amplifying a defined genomic locus across all organisms in a sample, researchers can generate millions of reads that, after quality filtering and error correction, reveal the composition, diversity and relative abundance of bacteria, archaea and fungi. Computational workflows typically encompass merging of paired‐end reads, chimera detection, clustering or denoising to define operational units, and taxonomic assignment against curated reference databases. Recent advances in primer design, molecular barcoding and bioinformatic algorithms have greatly reduced PCR and sequencing biases, improved resolution to single‐nucleotide variants and allowed absolute quantification of template molecules. Applications span environmental microbiology, clinical diagnostics, agricultural science and biotechnological monitoring, providing insights into ecosystem function, host–microbe interactions and the distribution of rare or uncultured taxa. Ongoing challenges include standardising protocols across platforms, integrating multi‐omic data and extending approaches to RNA, viral and eukaryotic communities.

Research from Nature Portfolio

Innovations in molecular tagging have addressed long-standing PCR amplification biases. A new design for unique molecular identifiers (UMIs), synthesised using homotrimeric nucleotide blocks, provides internal error correction that enables absolute counting of original molecules in both bulk and single-cell amplicon libraries. This strategy substantially reduces false diversity arising from sequencing errors and PCR duplicates, yielding more accurate assessments of community structure and transcript abundance. In parallel, a novel linear-time clustering algorithm has been introduced, which sorts sequences by relatedness to achieve scalability to millions of reads without sacrificing the accuracy of cluster boundaries. Comparative benchmarks demonstrate that this approach matches or exceeds the performance of established tools while offering near-linear computational complexity, making it feasible to analyse expansive amplicon datasets on standard computing resources.

Microbial Community Analysis Using Amplicon Sequencing publication trend

The graph below shows the total number of articles in microbial community analysis using amplicon sequencing across all publications each year (not limited to Nature Index journals).

Technical terms

Amplicon sequencing: Targeted high-throughput sequencing of a specific, PCR‐amplified genomic region across diverse organisms.

Unique molecular identifier (UMI): A random oligonucleotide tag appended to each template molecule to track and correct PCR duplicates and errors.

Operational taxonomic unit (OTU): A cluster of similar sequences grouped by a defined similarity threshold to approximate taxonomic entities.

Amplicon sequence variant (ASV): An exact sequence inferred from raw reads after denoising that represents a unique biological variant.

Chimera: An artefactual sequence formed by the fusion of two or more parent templates during PCR amplification.

Denoising: Computational removal of sequencing and PCR errors to distinguish true biological variants from noise.

References

1. Correcting PCR amplification errors in unique molecular identifiers to generate accurate numbers of sequencing molecules. Nature Methods (2024).
2. Accurately clustering biological sequences in linear time by relatedness sorting. Nature Communications (2024).
3. EasyAmplicon: An easy‐to‐use, open‐source, reproducible, and community‐based pipeline for amplicon data analysis in microbiome research. iMeta (2023).
4. Nonparametric richness estimators Chao1 and ACE must not be used with amplicon sequence variant data. The ISME Journal: Multidisciplinary Journal of Microbial Ecology (2024).
5. VSEARCH: a versatile open source tool for metagenomics. PeerJ (2016).