eDiVA-Classification and prioritization of pathogenic variants for clinical diagnostics.

"we therefore performed three separate performance tests for each of the three benchmark sets applying the following criteria (a) using only variants having revel and m-cap scores available (clinvar: 3887 tp and 10494 tn; hgmd/gnomad: 63712 tp and 100000 tn; hgmd: 63712 tp and 1892 tn); (b) random subset of all variants assigning a default value of 0 to missing values (clinvar: 19888 tp and 16694 tn; hgmd/gnomad: 96569 tp and 100000 tn; hgmd: 96569 tp and 7376 tn); and (c) using only rare variants (af <=0 01) from the previous pool of variants (clinvar: 16531 tp and 15531; hgmd/gnomad: 90004 tp and 97828 tn; hgmd: 96004 tp and 2817 tn).; figure 2 benchmarking of the pathogenicity classifiers ediva-score cadd eigen revel and m-cap using roc for (a) set of 10494 clinvar pathogenic variants (tp) and 3887 clinvar "benign" variants (tn); (b) set of 16694 clinvar pathogenic variants (tp) and 19888 clinvar "benign" variants (tn) setting missing values to benign (c) subset of rare variants (af <1% from set c); (d) set of 63712 variants from hgmd (tp) and 100000 from gnomad (tn) for which values from all tools are available; (e) set of 96569 variants from hgmd (tp) and 100000 from gnomad (tn) setting missing values to benign; (f) subset of rare variants (af <1% from set e); (g) set of 63712 hgmd variants ("dm" and "dm?"); we first compared the performance on classifying pathogenic and benign variants from clinvar (figure 2 a) on distinguishing disease variants from hgmd (stenson et al 2017 ) from 100000 random variants from gnomad (figure 2 d) for which scores are available for all methods. the main functionality of ediva is to process next-generation sequencing (ngs) data for small sets of samples (e g families or parent-child trios) and to output a shortlist of potentially causal variants for the diagnosed disease ediva is available as an open-source repository https://github com/mbosio85/ediva with a docker container composition wrapped within a nextflow (di tommaso et al 2017 ) interface to guarantee exact reproducibility on the most common computing platforms (including several cloud platforms) and as a freely accessible web server: http://www ediva crg eu ."

"the main functionality of ediva is to process next-generation sequencing (ngs) data for small sets of samples (e g families or parent-child trios) and to output a shortlist of potentially causal variants for the diagnosed disease ediva is available as an open-source repository https://github com/mbosio85/ediva with a docker container composition wrapped within a nextflow (di tommaso et al 2017 ) interface to guarantee exact reproducibility on the most common computing platforms (including several cloud platforms) and as a freely accessible web server: http://www ediva crg eu .; it is composed of four components: ediva-predict handles read alignment and variant prediction ediva-annotate performs functional annotation of variants ediva-score estimates the probability of variants to be pathogenic and ediva-prioritize filters and ranks variants according to various quality criteria proper segregation and likelihood to cause phenotypic changes ediva is available as standalone software at https://github com/mbosio85/ediva and as a web service providing access to functional annotation pathogenicity classification and causal variant prioritization modules ( www ediva crg eu )."

"We acknowledge support of the Spanish Ministry of Economy and Competitiveness, Centro de Excelencia Severo Ochoa 2013–2017, and the CERCA Programme/Generalitat de Catalunya. This project has received funding from the “la Caixa” Foundation, the CRG emergent translational research award and the European Union's H2020 Research and Innovation Programme under the grant agreement No 635290 (PanCanRisk)."