Tandem repeats (TRs) play important roles in genomic variation and disease risk in humans. Long-read sequencing allows for the accurate characterization of TRs; however, the underlying bioinformatics perspectives remain challenging. We present otter and TREAT: otter is a fast targeted local assembler, cross-compatible across different sequencing platforms. It is integrated in TREAT, an end-to-end workflow for TR characterization, visualization, and analysis across multiple genomes. In a comparison with existing tools based on long-read sequencing data from both Oxford Nanopore Technology (ONT, Simplex and Duplex) and Pacific Bioscience (PacBio, Sequel II and Revio), otter and TREAT achieve state-of-the-art genotyping and motif characterization accuracy. Applied to clinically relevant TRs, TREAT/otter significantly identify individuals with pathogenic TR expansions. When applied to a case-control setting, we replicate previously reported associations of TRs with Alzheimer's disease, including those near or within APOC1 (P = 2.63 × 10⁻⁹), SPI1 (P = 6.5 × 10⁻³), and ABCA7 (P = 0.04) genes. Finally, we use TREAT/otter to systematically evaluate potential biases when genotyping TRs using diverse ONT and PacBio long-read sequencing data sets. We show that, in rare cases (0.06%), long-read sequencing from coverage drops in TRs, including the disease-associated TRs in ABCA7 and RFC1 genes. Such coverage drops can lead to TR misgenotyping, hampering the accurate characterization of TR alleles. Taken together, our tools can accurately genotype TRs across different sequencing technologies and with minimal requirements, allowing end-to-end analysis and comparisons of TRs in human genomes, with broad applications in research and clinical fields.

中文翻译：

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使用 TREAT 和 otter 在长读长测序平台上表征串联重复复杂性


串联重复序列 （TRs） 在人类基因组变异和疾病风险中起重要作用。长读长测序可准确表征 TR;然而，潜在的生物信息学观点仍然具有挑战性。我们介绍 otter 和 TREAT：otter 是一种快速靶向的本地组装器，可在不同的测序平台上交叉兼容。它集成在 TREAT 中，TREAT 是一种端到端工作流程，用于跨多个基因组进行 TR 表征、可视化和分析。与基于 Oxford Nanopore Technology（ONT、Simplex 和 Duplex）和 Pacific Bioscience（PacBio、Sequel II 和 Revio）的长读长测序数据的现有工具相比，otter 和 TREAT 实现了最先进的基因分型和基序表征准确性。应用于临床相关的 TRs，TREAT/otter 可显著识别具有致病性 TR 扩增的个体。当应用于病例对照设置时，我们复制了先前报道的 TRs 与阿尔茨海默病的关联，包括接近或位于 APOC1 （P = 2.63 × ^10-9）、SPI1 （P = 6.5 × ^10-3） 和 ABCA7 （P = 0.04） 基因的关联。最后，我们使用 TREAT/otter 系统地评估使用不同的 ONT 和 PacBio 长读长测序数据集对 TRs 进行基因分型时的潜在偏差。我们表明，在极少数情况下 （0.06%），来自覆盖度的长读长测序在 TRs 中下降，包括 ABCA7 和 RFC1 基因中与疾病相关的 TRs。这种覆盖度下降会导致 TR 基因分型错误，从而阻碍 TR 等位基因的准确表征。 综上所述，我们的工具可以在不同的测序技术中以最低的要求准确地对 TR 进行基因分型，从而允许对人类基因组中的 TR 进行端到端分析和比较，在研究和临床领域具有广泛的应用。