%0 Journal Article
%A Lopez-Lopez, Daniel
%A Loucera, Carlos
%A Carmona, Rosario
%A Aquino, Virginia
%A Salgado, Josefa
%A Pasalodos, Sara
%A Miranda, María
%A Alonso, Ángel
%A Dopazo, Joaquín
%T SMN1 copy-number and sequence variant analysis from next-generation sequencing data.
%D 2020
%U http://hdl.handle.net/10668/16425
%X 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.
%K SMA
%K next generation sequencing
%K pipeline
%~