SMN1 copy‐number and sequence variant analysis from next‐generation sequencing data. Issue 12 (14th October 2020)

• Record Type: Journal Article
• Authors: Lopez‐Lopez, Daniel; Loucera, Carlos; Carmona, Rosario; Aquino, Virginia; Salgado, Josefa; Pasalodos, Sara; Miranda, María; Alonso, Ángel; Dopazo, Joaquín
• Is Part Of: Human mutation. Volume 41:Issue 12(2020)
• Page Start: 2073
• Page End: 2077
• Publication Date: 2020-10-14
• Subjects: next generation sequencing -- pipeline -- SMA; Human chromosome abnormalities -- Periodicals; Mutation (Biology) -- Periodicals; 616.04205
• DOI: 10.1002/humu.24120
• Languages: English
• ISSNs: 1059-7794

Abstract

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 .