SMN1 copy-number and sequence variant analysis from next-generation sequencing data
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Updated: Dec 5, 2025

Detection of Copy Number Alterations Using Single Cell Sequencing
Published on: February 17, 2017
Daniel Lopez-Lopez1,2, Carlos Loucera1,2, Rosario Carmona1
1Clinical Bioinformatics Area, Fundación Progreso y Salud (FPS), CDCA, Hospital Virgen del Rocio, Sevilla, Spain.
Abstract:
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.
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