Spinal muscular atrophy (SMA) is a severe neuromuscular autosomal recessive disorder affecting 1/10,000 live births. Most SMA patients present homozygous deletion of SMN1, while the vast majority of SMA carriers present only a single SMN1 copy. The sequence similarity between SMN1 and SMN2, and the complexity of the SMN locus makes the estimation of the SMN1 copy‐number by next‐generation sequencing (NGS) very difficult. Here, we present SMAca, the first python tool to detect SMA carriers and estimate the absolute SMN1 copy‐number using NGS data. Moreover, SMAca takes advantage of the knowledge of certain variants specific to SMN1 duplication to also identify silent carriers. This tool has been validated with a cohort of 326 samples from the Navarra 1000 Genomes Project (NAGEN1000). SMAca was developed with a focus on execution speed and easy installation. This combination makes it especially suitable to be integrated into production NGS pipelines. Source code and documentation are available at https://www.github.com/babelomics/SMAca.

中文翻译：

---

来自下一代测序数据的 SMN1 拷贝数和序列变异分析

脊髓性肌萎缩症 (SMA) 是一种严重的神经肌肉常染色体隐性遗传疾病，影响 1/10,000 活产儿。大多数SMA患者呈现SMN1纯合缺失，而绝大多数SMA携带者仅呈现单个SMN1拷贝。SMN1和SMN2之间的序列相似性以及 SMN 基因座的复杂性使得通过下一代测序 (NGS)估计SMN1拷贝数变得非常困难。在这里，我们展示了 SMAca，这是第一个检测 SMA 携带者并使用 NGS 数据估计绝对 SMN1 拷贝数的 Python 工具。此外，SMAca 利用了特定于SMN1的某些变体的知识重复也可以识别沉默的携带者。该工具已通过来自 Navarra 1000 基因组计划 (NAGEN1000) 的 326 个样本队列进行验证。SMAca 的开发重点是执行速度和易于安装。这种组合使其特别适合集成到生产 NGS 管道中。源代码和文档可从 https://www.github.com/babelomics/SMAca 获得。