GARFIELD-NGS: Genomic vARiants FIltering by dEep Learning moDels in NGS

Viola Ravasio, Marco Ritelli, Andrea Legati, Edoardo Giacopuzzi

Research output: Contribution to journalArticlepeer-review


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 twocolour 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

Original languageEnglish
Pages (from-to)3038-3040
Number of pages3
Issue number17
Publication statusPublished - Sep 1 2018

ASJC Scopus subject areas

  • Statistics and Probability
  • Biochemistry
  • Molecular Biology
  • Computer Science Applications
  • Computational Theory and Mathematics
  • Computational Mathematics


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