Abstract
OBJECTIVES: To evaluate seven bioinformatics platforms for automated AI-based genomic variant prioritization and classification. METHODS: An evaluation was performed of 24 genetic variants that explained the phenotype of 20 patients. FASTQ files were simultaneously uploaded on the following bioinformatics platforms: Emedgene, eVai, Varsome Clinical, CentoCloud, QIAGEN Clinical Insight (QCI) Interpret, SeqOne and Franklin. Automated variant prioritization and classification was performed using patient phenotypes. Phenotypes were entered onto the different platforms using HPO terms. The classification of reference was established based on the criteria of the American College of Medical Genetics and Genomics (ACMG) and the Association of Molecular Pathology and ACMG/ClinGen guidelines. RESULTS: SeqOne demonstrated the highest performance in variant prioritization and ranked 19 of 24 variants in the Top 1; four in the Top 5, and one in the Top 15, followed by CentoCloud and Franklin. QCI Interpret did not prioritize six variants and failed to detect one. Emedgene did not prioritize one and failed to detect one. Finally, Varsome Clinical did not prioritize four variants. Franklin classified correctly 75 % of variants, followed by Varsome Clinical (67 %) and QCI Interpret (63 %). CONCLUSIONS: SeqOne, CentoCloud, and Franklin had the highest performance in automated variant prioritization, as they prioritized all variants. In relation to automated classification, Franklin showed a higher concordance with the reference and a lower number of discordances with clinical implications. In conclusion, Franklin emerges as the platform with the best overall performance. Anyway, further studies are needed to confirm these results.