Long-read amplicon sequencing approaches have enabled microbiome researchers to generate high-quality amplicon sequencing data covering full-length genetic regions such as the entire 16S rRNA gene, 18S rRNA, or ITS regions. This is achieved through long read sequencing platforms like Oxford Nanopore Technologies or PacBio sequencing, which directly analyse genomic DNA to produce reads that span entire amplicons without fragmentation. These methods are especially valuable for characterising microbial communities from complex or novel environments.

The approach supports amplicon sequencing analysis from illumina amplicon data or other short-read datasets through hybrid assemblies, improving resolution and downstream applications. By including accurate primer sequences during library preparation, researchers can reduce sequencing errors and improve the quality of sequencing data used for downstream analysis.

Compared to short-read strategies, long read amplicon sequencing delivers improved phylogenetic resolution, enabling precise taxonomic assignment for operational taxonomic units and minimising misclassification. Because it spans the full 16S rRNA, it provides more accurate insights into microbial diversity and supports robust genetic variation studies by linking variants to complete gene contexts.

The technique reduces the influence of primer bias, allowing more reliable quantification of amplicon sequencing data and better estimation of microbial relative abundance. It is also sensitive enough for low-biomass samples, overcoming host DNA interference. These strengths make it suitable for studies in environmental microbiology, clinical microbiome profiling, and detection of rare or structurally unique organisms.

In addition, long-read data can be aligned to a reference genome for detection of structural variants or novel alleles, supporting investigations into functional roles and evolutionary relationships. Researchers often combine nucleic acids res methods with long-read sequencing to optimize yield and purity of input DNA.

Short-read workflows, such as those generating illumina amplicon data, typically rely on partial 16S rRNA gene regions. While this can be effective for broad profiling, it may lead to taxonomic ambiguity and underestimation of diversity. In contrast, long-read strategies generate complete amplicons, improving classification accuracy and enabling confident identification at the species or strain level.

When integrated into a bioinformatics workflow that includes quality filtering, chimera removal, and amplicon sequencing analysis, long-read datasets can yield highly detailed profiles of microbial communities, revealing niche populations that might be missed by short-read approaches.