Table 4_PRONAME: a user-friendly pipeline to process long-read nanopore metabarcoding data by generating high-quality consensus sequences.xlsx

BackgroundThe study of sample taxonomic composition has evolved from direct observations and labor-intensive morphological studies to different DNA sequencing methodologies. Most of these studies leverage the metabarcoding approach, which involves the amplification of a small taxonomically-informative portion of the genome and its subsequent high-throughput sequencing. Recent advances in sequencing technology brought by Oxford Nanopore Technologies have revolutionized the field, enabling portability, affordable cost and long-read sequencing, therefore leading to a significant increase in taxonomic resolution. However, Nanopore sequencing data exhibit a particular profile, with a higher error rate compared with Illumina sequencing, and existing bioinformatics pipelines for the analysis of such data are scarce and often insufficient, requiring specialized tools to accurately process long-read sequences.
ResultsWe present PRONAME (PROcessing NAnopore MEtabarcoding data), an open-source, user-friendly pipeline optimized for processing raw Nanopore sequencing data. PRONAME includes precompiled databases for complete 16S sequences (Silva138 and Greengenes2) and a newly developed and curated database dedicated to bacterial 16S-ITS-23S operon sequences. The user can also provide a custom database if desired, therefore enabling the analysis of metabarcoding data for any domain of life. The pipeline significantly improves sequence accuracy, implementing innovative error-correction strategies and taking advantage of the new sequencing chemistry to produce high-quality duplex reads. Evaluations using a mock community have shown that PRONAME delivers consensus sequences demonstrating at least 99.5% accuracy with standard settings (and up to 99.7%), making it a robust tool for genomic analysis of complex multi-species communities.
ConclusionPRONAME meets the challenges of long-read Nanopore data processing, offering greater accuracy and versatility than existing pipelines. By integrating Nanopore-specific quality filtering, clustering and error correction, PRONAME produces high-precision consensus sequences. This brings the accuracy of Nanopore sequencing close to that of Illumina sequencing, while taking advantage of the benefits of long-read technologies.

研究背景
样本分类组成研究已从直接观测与劳动密集型形态学研究，逐步演进至各类DNA测序技术方法。此类研究大多采用元条形码（metabarcoding）技术路线，即扩增基因组中一段具备分类学信息的短小片段，随后对其进行高通量测序。牛津纳米孔科技公司（Oxford Nanopore Technologies）带来的测序技术最新进展，彻底革新了该领域：该技术具备便携性、成本低廉的优势，且可实现长读长测序，由此大幅提升了分类学分辨率。然而，纳米孔测序数据存在独特的特征：相较于Illumina测序，其错误率更高；且目前用于分析此类数据的生物信息学流程十分稀缺，往往难以满足需求，亟需专用工具来精准处理长读长序列。
研究结果
本研究推出PRONAME（全称PROcessing NAnopore MEtabarcoding data，即纳米孔元条形码数据处理流程），这是一款开源且操作友好的流程，专为处理原始纳米孔测序数据优化设计。PRONAME内置完整16S序列的预编译数据库（Silva138与Greengenes2），以及全新开发并经过人工整理的、针对细菌16S-ITS-23S操纵子序列的专用数据库。用户亦可根据需求自定义数据库，由此可针对所有生命域的元条形码数据开展分析。该流程采用创新性的错误校正策略，并借助新型测序化学技术生成高质量双链读段，可显著提升序列准确性。通过模拟群落开展的评估结果显示，在标准参数设置下，PRONAME生成的一致序列准确率至少可达99.5%（最高可达99.7%），足以作为分析复杂多物种群落基因组学的可靠工具。
研究结论
PRONAME应对了长读长纳米孔数据处理的诸多挑战，相较于现有流程具备更高的准确性与通用性。通过集成针对纳米孔测序的质量过滤、聚类与错误校正模块，PRONAME可生成高精度的一致序列。这使得纳米孔测序的准确率得以接近Illumina测序，同时兼顾了长读长技术的各项优势。