Abstract
16S rRNA gene sequencing is the gold standard for identifying microbial diversity in environmental communities. The Illumina short-read platform is widely used in marine environment studies due to its cost-effectiveness and high accuracy, but its limited read length restricts taxonomic identification mainly to genus or family levels. Recently, the PacBio long-read sequencing platform was developed. This method has exceptional base-level resolution exceeding 99%, thereby effectively mitigating the challenges associated with high error rates commonly observed in long-read sequencing technologies. However, few studies have compared the PacBio long-read and Illumina short-read platforms in marine deep-sea sediments. Here, the PacBio long-read and Illumina short-read platforms were compared with samples collected from the deep-sea surface sediments from the cold seep in the Shenhu area of the South China Sea offshore Pearl River Estuary. Comparisons revealed a more comprehensive taxonomic identification, α-diversity, and β-diversity by PacBio long-reads. The PacBio long-read platform exhibited higher classified rates and classified taxonomy at all levels, particularly at the species level. The PacBio long-read platform was also more accurate at capturing fine spatial-scale variations in microbial communities in sediments. Our studies will facilitate the selection of 16S rRNA sequencing platforms for investigating fine spatial-scale patterns in microbial communities in deep-sea surface sediments and serve as a crucial methodological reference for future studies on microbial diversity. IMPORTANCE: The PacBio long-read platform, with its exceptional base-level resolution exceeding 99%, has advanced our comprehension of deep-sea microbial diversity. By comparing microbial community analyses conducted using the Illumina short-read and PacBio long-read sequencing platforms, we have provided an enhanced understanding of fine spatial-scale patterns in microbial community diversity with depth across a deep-sea sediment core, as well as methodological insights that will be valuable for future research in this field.