Structural variations are the greatest source of genetic variation, but they remain poorly understood because of technological limitations. Single-molecule long-read sequencing has the potential to dramatically advance the field, although high error rates are a challenge with existing methods. Addressing this need, we introduce open-source methods for long-read alignment (NGMLR; https://github.com/philres/ngmlr) and structural variant identification (Sniffles; https://github.com/fritzsedlazeck/Sniffles) that provide unprecedented sensitivity and precision for variant detection, even in repeat-rich regions and for complex nested events that can have substantial effects on human health. In several long-read datasets, including healthy and cancerous human genomes, we discovered thousands of novel variants and categorized systematic errors in short-read approaches. NGMLR and Sniffles can automatically filter false events and operate on low-coverage data, thereby reducing the high costs that have hindered the application of long reads in clinical and research settings.

结构变异是遗传变异的最大来源，但由于技术限制，人们对它们仍然知之甚少。尽管高错误率是现有方法的一个挑战，但单分子长读长测序有可能极大地推进该领域的发展。为了满足这一需求，我们引入了长读比对的开源方法（NGMLR；https://github.com/philres/ngmlr）和结构变异识别（Sniffles；https://github.com/fritzsedlazeck/Sniffles）为变异检测提供前所未有的灵敏度和精度，即使是在重复丰富的区域以及可能对人类健康产生重大影响的复杂嵌套事件。在几个长读数据集中，包括健康和癌症人类基因组，我们发现了数千种新的变异，并对短读方法中的系统错误进行了分类。 NGMLR 和 Sniffles 可以自动过滤错误事件并对低覆盖率数据进行操作，从而降低阻碍长读取在临床和研究环境中应用的高成本。