VarAFT: a variant annotation and filtration system for human next generation sequencing data

Jean-Pierre Desvignes (1) , Marc Bartoli (1) , Valérie Delague (1) , Martin Krahn (1, 2) , Morgane Miltgen (1) , Christophe Béroud (1, 2) , David Salgado (1)

1 MMG - Marseille medical genetics - Centre de génétique médicale de Marseille
2 Département de génétique médicale [Hôpital de la Timone - APHM]

With the rapidly developing high-throughput sequencing technologies known as next generation sequencing or NGS, our approach to gene hunting and diagnosis has drastically changed. In <10 years, these technologies have moved from gene panel to whole genome sequencing and from an exclusively research context to clinical practice. Today, the limit is not the sequencing of one, many or all genes but rather the data analysis. Consequently, the challenge is to rapidly and efficiently identify disease-causing mutations within millions of variants. To do so, we developed the VarAFT software to annotate and pinpoint human disease-causing mutations through access to multiple layers of information. VarAFT was designed both for research and clinical contexts and is accessible to all scientists, regardless of bioinformatics training. Data from multiple samples may be combined to address all Mendelian inheritance modes, cancers or population genetics. Optimized filtration parameters can be stored and re-applied to large datasets. In addition to classical annotations from dbNSFP, VarAFT contains unique features at the disease (OMIM), phenotypic (HPO), gene (Gene Ontology, pathways) and variation levels (predictions from UMD-Predictor and Human Splicing Finder) that can be combined to optimally select candidate pathogenic mutations. VarAFT is freely available at: http://varaft.eu.

Domains: Life Sciences [q-bio]; Genetics; Human genetics

Jean-Pierre Desvignes, Marc Bartoli, Valérie Delague, Martin Krahn, Morgane Miltgen, et al.. VarAFT: a variant annotation and filtration system for human next generation sequencing data. Nucleic Acids Research, 2018, 46 (W1), pp.W545-W553. ⟨10.1093/nar/gky471⟩. ⟨hal-01852493⟩