We used long read sequencing data generated from Knightia excelsa, a nectar-producing Proteaceae tree endemic to Aotearoa (New Zealand), to explore how sequencing data type, volume and workflows can impact final assembly accuracy and chromosome reconstruction. Establishing a high-quality genome for this species has specific cultural importance to Māori and commercial importance to honey producers in Aotearoa. Assemblies were produced by five long read assemblers using data subsampled based on read lengths, two polishing strategies and two Hi-C mapping methods. Our results from subsampling the data by read length showed that each assembler tested performed differently depending on the coverage and the read length of the data. Subsampling highlighted that input data with longer read lengths but perhaps lower coverage constructed more contiguous, kmers and gene-complete assemblies than short read length input data with higher coverage. The final genome assembly was constructed into 14 pseudochromosomes using an initial flye long read assembly, a racon/medaka/pilon combined polishing strategy, salsa2 and allhic scaffolding, juicebox curation, and Macadamia linkage map validation. We highlighted the importance of developing assembly workflows based on the volume and read length of sequencing data and established a robust set of quality metrics for generating high-quality assemblies. Scaffolding analyses highlighted that problems found in the initial assemblies could not be resolved accurately by Hi-C data and that assembly scaffolding was more successful when the underlying contig assembly was of higher accuracy. These findings provide insight into how quality assessment tools can be implemented throughout genome assembly pipelines to inform the de novo reconstruction of a high-quality genome assembly for nonmodel organisms.

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关于 rewarewa (Knightia excelsa) 基因组准确性的组装策略和质量指标的探索

我们使用了从Knightia excelsa生成的长读长测序数据，一种原产于 Aotearoa（新西兰）的产花蜜 Proteaceae 树，探索测序数据类型、数量和工作流程如何影响最终组装的准确性和染色体重建。为该物种建立一个高质量的基因组对毛利人具有特殊的文化重要性，对 Aotearoa 的蜂蜜生产商具有商业重要性。组装由五个长读取组装器使用基于读取长度、两种抛光策略和两种 Hi-C 映射方法进行二次采样的数据生成。我们通过读取长度对数据进行二次采样的结果表明，每个测试的汇编器的性能都不同，具体取决于数据的覆盖范围和读取长度。子采样强调具有更长读取长度但可能更低覆盖率的输入数据构造得更连续，kmers 和基因完整组装比具有更高覆盖率的短读长输入数据。最终的基因组组装被构建成 14 个假染色体，使用初始flye长读组件、racon / medaka / pilon组合抛光策略、salsa2和allhic脚手架、juicebox管理和澳洲坚果连锁图验证。我们强调了基于测序数据量和读长开发组装工作流程的重要性，并建立了一套强大的质量指标来生成高质量的组装。脚手架分析强调，在初始组装中发现的问题无法通过 Hi-C 数据准确解决，当底层重叠群组装精度更高时，组装脚手架更成功。这些发现让我们深入了解如何在整个基因组组装管道中实施质量评估工具，为非模式生物高质量基因组组装的从头重建提供信息。