Computational complexity is a key limitation of genomic analyses. Thus, over the last 30 years, researchers have proposed numerous fast heuristic methods that provide computational relief. Comparing genomic sequences is one of the most fundamental computational steps in most genomic analyses. Due to its high computational complexity, new, more optimized exact and heuristic algorithms are still being developed. We find that these methods are highly sensitive to the underlying data, its quality, and various hyperparameters. Despite their wide use, no in-depth analysis has been performed, potentially falsely discarding genetic sequences from further analysis and unnecessarily inflating computational costs. We provide the first analysis and benchmark of this heterogeneity. We deliver an actionable overview of the 11 most widely used state-of-the-art methods for comparing genomic sequences. We also inform readers about their pros and cons using thorough experimental evaluation and different real datasets from all major manufacturers (i.e., Illumina, ONT, and PacBio). SequenceLab is publicly available at https://github.com/CMU-SAFARI/SequenceLab.

翻译：计算复杂度是基因组分析的关键限制因素。因此，过去30年间，研究者提出了大量快速启发式方法来缓解计算压力。基因组序列比较是大多数基因组分析中最基础的计算步骤之一。由于其高计算复杂度，新型且更优化的精确算法与启发式算法仍在不断涌现。我们发现这些方法对底层数据、数据质量及多种超参数具有高度敏感性。尽管这些方法被广泛使用，但尚未有对其进行的深度分析，这可能导致遗传序列被错误地排除在后续分析之外，并造成不必要的计算成本膨胀。我们首次对此异质性进行了分析与基准测试。我们提供了11种最广泛使用的基因组序列比较前沿方法的可操作综述，并通过全面的实验评估及来自所有主流测序平台（即Illumina、ONT与PacBio）的不同真实数据集，向读者阐明其优缺点。SequenceLab开源代码见https://github.com/CMU-SAFARI/SequenceLab。