SMN1 copy-number and sequence variant analysis from next-generation sequencing data.

TitleSMN1 copy-number and sequence variant analysis from next-generation sequencing data.
Publication TypeJournal Article
Year of Publication2020
AuthorsLópez-López, D, Loucera, C, Carmona, R, Aquino, V, Salgado, J, Pasalodos, S, Miranda, M, Alonso, Á, Dopazo, J
JournalHum Mutat
Date Published2020 Oct 14

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

Alternate JournalHum Mutat
PubMed ID33058415
Grant List676559 / / H2020 Health /
PT17/0009/0006 / / Ministerio de Economía y Competitividad /
SAF2017-88908-R / / Ministerio de Economía y Competitividad /
813533 / / H2020 Marie Sklodowska-Curie Actions /