Lopez-Lopez, DanielLoucera, CarlosCarmona, RosarioAquino, VirginiaSalgado, JosefaPasalodos, SaraMiranda, MaríaAlonso, ÁngelDopazo, Joaquín2023-02-092023-02-092020-10-14http://hdl.handle.net/10668/16425Spinal 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.enAttribution 4.0 Internationalhttp://creativecommons.org/licenses/by/4.0/SMAnext generation sequencingpipelineBase SequenceDNA Copy Number VariationsHigh-Throughput Nucleotide SequencingHumansReproducibility of ResultsSoftwareSurvival of Motor Neuron 1 Proteinresearch article33058415open access10.1002/humu.241201098-1004PMC7756735https://onlinelibrary.wiley.com/doi/pdfdirect/10.1002/humu.24120https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7756735/pdf