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, 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 advantages and downsides 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，在过去30年间，研究者提出了大量快速启发式方法以缓解计算压力。基因组序列比对是绝大多数基因组分析中最基础的计算步骤。由于其高计算复杂度，优化的精确算法与启发式算法仍在持续发展中。我们发现这些方法对底层数据、数据质量及各类超参数高度敏感。尽管这些方法被广泛使用，但尚未开展深入分析，这可能导致基因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增加。我们首次对这种异质性进行了分析与基准测试，提供了11种最广泛使用的最新基因组序列比对方法的可操作综述。通过使用来自三大主流测序平台（Illumina、ONT与PacBio）的真实数据集进行系统性实验评估，我们还向读者阐明这些方法的优势与不足。SequenceLab开源代码已发布于 https://github.com/CMU-SAFARI/SequenceLab。