Summary:Exome sequencing approach is extensively used in research and diagnostic laboratories to discover pathological variants and study genetic architecture of human diseases. However, a significant proportion of identified genetic variants are actually false positive calls, and this pose serious challenge for variants interpretation. Here, we propose a new tool named Genomic vARiants FIltering by dEep Learning moDels in NGS (GARFIELD-NGS), which rely on deep learning models to dissect false and true variants in exome sequencing experiments performed with Illumina or ION platforms. GARFIELD-NGS showed strong performances for both SNP and INDEL variants (AUC 0.71-0.98) and outperformed established hard filters. The method is robust also at low coverage down to 30X and can be applied on data generated with the recent Illumina two-colour chemistry. GARFIELD-NGS processes standard VCF file and produces a regular VCF output. Thus, it can be easily integrated in existing analysis pipeline, allowing application of different thresholds based on desired level of sensitivity and specificity. Availability and implementation:GARFIELD-NGS available at https://github.com/gedoardo83/GARFIELD-NGS. Supplementary information:Supplementary data are available at Bioinformatics online.

Original publication

DOI

10.1093/bioinformatics/bty303

Type

Journal article

Journal

Bioinformatics (Oxford, England)

Publication Date

09/2018

Volume

34

Pages

3038 - 3040

Addresses

Department of Molecular and Translational Medicine, University of Brescia, Brescia, Italy.

Keywords

Sequence Analysis, DNA, Genomics, Polymorphism, Single Nucleotide, INDEL Mutation, High-Throughput Nucleotide Sequencing, Deep Learning