Batch effects in a multi-year sequencing study: false biological trends due to changes in read lengths

High-throughput sequencing is a powerful tool, but suffers biases and errors that must be accounted for to prevent false biological conclusions. Such errors include batch effects, technical errors only present in subsets of data due to procedural changes within a study. If overlooked and multiple batches of data are combined, spurious biological signals can arise, particularly if batches of data are correlated with biological variables. Batch effects can be minimized through randomisation of sample groups across batches. However, in long-term or multi-year studies where data are added incrementally, full randomisation is impossible and batch effects may be a common feature. Here we present a case study where false signals of selection were detected due to a batch effect in a multi-year study of Alpine ibex (Capra ibex). The batch effect arose because sequencing read length changed over the course of the project and populations were added incrementally to the study, resulting in non-rand...高通量测序(High-throughput sequencing)是一项极具应用价值的研究工具，但其存在偏倚与误差，若未加以校正则会得出错误的生物学结论。此类误差包含批次效应（batch effect），以及因研究内部流程变更仅出现在部分数据子集内的技术误差。若忽略此类误差并合并多批次数据，则可能产生虚假的生物学信号，尤其当数据批次与生物学变量存在关联时。可通过在各批次间随机分配样本组来弱化批次效应。但在需逐步新增数据的长期或多年期研究中，无法实现完全随机化，此时批次效应可能成为普遍存在的问题。本研究呈现一则案例分析：在一项针对阿尔卑斯羱羊（Capra ibex）的多年期研究中，因批次效应检测到了虚假的选择信号。此次批次效应的产生源于测序读长随项目推进发生变化，且研究种群是逐步纳入本次研究的，最终导致了非随机……