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
ISSN1098-1004
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.

DOI10.1002/humu.24120
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 /