BACKGROUND Whilst much sequencing effort has focused on key mammalian model organisms such as mouse and human, little is known about the relationship between genome sequencing techniques for non-model mammals and genome assembly quality. This is especially relevant to non-model mammals, where the samples to be sequenced are often degraded and of low quality. A key aspect when planning a genome project is the choice of sequencing data to generate. This decision is driven by several factors, including the biological questions being asked, the quality of DNA available, and the availability of funds. Cutting-edge sequencing technologies now make it possible to achieve highly contiguous, chromosome-level genome assemblies, but rely on high-quality high molecular weight DNA. However, funding is often insufficient for many independent research groups to use these techniques. Here we use a range of different genomic technologies generated from a roadkill European polecat (Mustela putorius) to assess various assembly techniques on this low-quality sample. We evaluated different approaches for de novo assemblies and discuss their value in relation to biological analyses. RESULTS Generally, assemblies containing more data types achieved better scores in our ranking system. However, when accounting for misassemblies, this was not always the case for Bionano and low-coverage 10x Genomics (for scaffolding only). We also find that the extra cost associated with combining multiple data types is not necessarily associated with better genome assemblies. CONCLUSIONS The high degree of variability between each de novo assembly method (assessed from the 7 key metrics) highlights the importance of carefully devising the sequencing strategy to be able to carry out the desired analysis. Adding more data to genome assemblies does not always result in better assemblies, so it is important to understand the nuances of genomic data integration explained here, in order to obtain cost-effective value for money when sequencing genomes.

中文翻译：

---

智能测序：非模型哺乳动物的从头测序和组装方法。

背景虽然许多测序工作都集中在关键的哺乳动物模式生物，例如小鼠和人类，但对于非模式哺乳动物的基因组测序技术与基因组组装质量之间的关系知之甚少。这与非模型哺乳动物尤其相关，在这些哺乳动物中，待测序的样本通常会降解且质量低下。计划基因组项目时的一个关键方面是选择要生成的测序数据。这一决定是由几个因素驱动的，包括所提出的生物学问题、可用 DNA 的质量以及资金的可用性。尖端测序技术现在可以实现高度连续的染色体级基因组组装，但依赖于高质量的高分子量 DNA。然而，许多独立研究小组的资金往往不足以使用这些技术。在这里，我们使用从欧洲臭鼬 (Mustela putorius) 产生的一系列不同基因组技术来评估这种低质量样本的各种组装技术。我们评估了从头组装的不同方法，并讨论了它们在生物分析方面的价值。结果 通常，包含更多数据类型的程序集在我们的排名系统中获得了更好的分数。然而，在考虑装配错误时，Bionano 和低覆盖率 10x Genomics（仅适用于脚手架）并非总是如此。我们还发现，与组合多种数据类型相关的额外成本不一定与更好的基因组组装相关。结论 每种从头组装方法（从 7 个关键指标评估）之间的高度可变性突出了仔细设计测序策略以进行所需分析的重要性。向基因组组装中添加更多数据并不总能产生更好的组装，因此了解此处解释的基因组数据集成的细微差别非常重要，以便在对基因组进行测序时获得具有成本效益的物有所值。