Applying PCR cycle autonormalization to improve PacBio full-length 16S rRNA sequencing

The bacterial 16S rRNA gene is widely used to characterize host-associated and environmental microbiomes, most commonly through sequencing short hypervariable regions. Recent improvements in PacBio sequencing chemistry and concatenation approaches can now enable high-throughput, full-length 16S rRNA gene sequencing with high accuracy and depth. However, PCR-induced errors remain a major limitation, particularly for full-length amplicons where error accumulation may be elevated due to a five-fold increase in sequence lengths. These challenges are exacerbated when samples vary widely in microbial biomass, making it difficult to select a single optimal number of PCR cycles. Here, we evaluated the effects of PCR cycle autonormalization on PacBio Kinnex full-length 16S rRNA gene sequencing across seven agriculturally relevant specimen types. We compared conventional PCR protocols (20, 24, and 30 cycles) with an autonormalization approach in which individual reactions were terminated during exponential amplification based on real-time fluorescence thresholds. Autonormalized reactions consistently retained a higher proportion of sequences following denoising and chimera removal, exhibited fewer substitution-based errors, and produced more even read distributions across samples. Meanwhile, overamplified reactions (30 cycles) showed elevated error rates and higher sequence removal, particularly in samples with greatest microbial biodiversity. Importantly, PCR protocol had minimal effects on overall community composition relative to specimen types. These results demonstrate that PCR cycle autonormalization improves data yield for full-length 16S rRNA sequencing while maintaining robust inferences, while facilitating streamlined workflows for heterogeneous samples. ImportanceAmplicon-based sequencing of the 16S rRNA gene is foundational tool in microbiome research, yet PCR amplification remains a major source of technical error. This challenge is magnified for full-length 16S rRNA sequencing and for workflows that process specimen types with widely varying microbial biomass. Selecting a single PCR cycle number can result in under-amplification of samples with low 16S rRNA copies or over-amplification and error accumulation in high-titer samples. Here, we show that PCR cycle autonormalization, implemented using iconPCR, substantially improves PacBio full-length 16S rRNA sequencing by reducing PCR-induced substitution errors and chimeras while maintaining consistent biological inference. Autonormalization also enables blind pooling of amplicons without post-PCR quantification or equimolar normalization, reducing hands-on time and sample loss. These benefits make cycle autonormalization particularly valuable for high-throughput and production-scale sequencing applications handling diverse specimen types.