Applying PCR cycle autonormalization to PacBio full-length 16S rRNA library preparations: impacts on error rates and sequence distributions.

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, errors introduced during library preparation remain a major limitation, particularly during PCR amplification of full-length amplicons, where error accumulation may be elevated due to longer sequence lengths. These challenges are amplified when samples vary widely in microbial biomass, making it difficult to select a single optimal number of PCR cycles. Here, we evaluated PCR cycle autonormalization for PacBio Kinnex full-length 16S rRNA gene sequencing across seven agriculturally relevant specimen types. We compared conventional fixed-cycle 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. Under the workflow tested here, autonormalized libraries generally retained a high proportion of sequences following denoising and chimera removal, exhibited low residual error rates (<0.005%), and yielded relatively even read distributions across heterogeneous sample inputs. Overamplified reactions (30 cycles) showed elevated residual error rates and greater sequence loss, particularly in samples with higher microbial biodiversity, whereas low-cycle libraries produced more variable read output among specimens. Importantly, the PCR protocol had relatively minor effects on overall community composition compared with specimen type. These results support PCR cycle autonormalization as a useful workflow strategy for heterogeneous full-length 16S library preparation, while also highlighting the importance of library design, pooling strategy, and downstream processing in shaping technical outcomes.IMPORTANCEAmplicon-based sequencing of the 16S rRNA gene is a foundational tool in microbiome research, yet PCR amplification remains a major source of library-preparation 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 underamplify low-biomass samples or overamplify high-titer samples, increasing artifacts and sequence loss during downstream processing. Here, we show that PCR cycle autonormalization can be integrated into a PacBio full-length 16S rRNA workflow and, under the conditions tested, provides low residual error rates and relatively even sample representation across heterogeneous inputs. 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 library preparation applications handling diverse specimen types.