Biology subjects

Fortier, N.

Publications and source records attributed to Fortier, N..

3 recordsLinked to original sources

bioRxiv2025

VSPGx: A High-Accuracy Pharmacogenomics Interpretation Software Solution with Automated CPIC Guideline Integration

Accurate pharmacogenomic genotype determination and interpretation are essential for personalized medicine, yet existing bioinformatics tools face significant limitations in detecting named alleles, maintaining current allele definitions, and providing comprehensive clinical annotations. We present VSPGx, a pharmacogenomics interpretation software solution that identifies diplotypes from next-generation sequencing data and annotates them against Clinical Pharmacogenetics Implementation Consortium (CPIC) and FDA drug recommendations using automated curation of the latest allele definitions. We benchmarked VSPGx against established tools including Aldy, PharmCAT, and Stargazer using both synthetic datasets and real-world clinical samples. In a comprehensive synthetic benchmark spanning 3,655 CYP2C9 diplotype combinations, VSPGx achieved 99.97% concordance, matching PharmCATs performance and substantially outperforming Aldy (93.08%) and Stargazer (27.06%). Clinical validation using 11 TaqMan OpenArray samples demonstrated 88.2% allele concordance and 89.1% phenotype concordance across 110 gene-sample combinations, with all discrepancies attributed to the benchmark data utilizing outdated allele definitions rather than VSPGx errors. Our automated curation process ensures continuous alignment with current CPIC guidelines, addressing a critical gap in existing pharmacogenomic analysis tools. VSPGx provides a robust, clinically-validated solution for pharmacogenomic analysis that combines high-accuracy diplotype calling with up-to-date, evidence-based drug recommendations.

bioinformatics↗

bioRxiv2025

Analyzing the Performance of Deep Learning Splice Prediction Algorithms

SpliceAI has become the leading computational tool for predicting splice-altering variants, but restrictive licensing has limited its adoption by commercial clinical laboratories. While open-source reimplementations have emerged with author-reported comparisons, independent benchmarking across diverse datasets is needed to establish their practical equivalence. We compared the original SpliceAI algorithm against two open-source alternatives (OpenSpliceAI and CI-SpliceAI) across three independent benchmarks: a curated dataset of 1,316 functionally validated variants, 213 variants with experimental splice assay data, and 58,064 clinically classified variants from ClinVar. All deep-learning methods were also compared with a legacy ensemble of four traditional splice prediction algorithms (MaxEntScan, NNSplice, GeneSplicer, and PWM), enabling direct comparison between modern and conventional approaches. Across all benchmarks, the deep-learning models consistently outperformed the ensemble of traditional algorithms. We evaluated sensitivity, specificity, and balanced accuracy for each algorithm, and performed statistical testing to assess significance of performance differences. Additionally, we conducted a correlation analysis on 100,000 variants to quantify the concordance of splice-scores and the agreement on splice site positions between implementations. All three deep learning algorithms demonstrated comparable performance on the literature-curated benchmark (balanced accuracies: 89.5-90.7%) and the ClinVar dataset (88.9-89.5%). While both open-source solutions achieved a statistically significantly higher accuracy than SpliceAI on the ClinVar dataset, the magnitude of this improvement was small and unlikely to be of practical significance. On the functional splice assay dataset, the original SpliceAI achieved the highest accuracy (83.6%), while OpenSpliceAI showed significantly lower performance (74.6%, p = 0.019). Correlation analysis revealed that CI-SpliceAI maintained balanced concordance across splice event types ({rho} = 0.786-0.883), whereas OpenSpliceAI exhibited asymmetric performance with stronger correlation for loss events ({rho} = 0.924-0.940) than gain events ({rho} = 0.668-0.677). Both implementations demonstrated high spatial agreement with SpliceAI, with exact splice site position match rates exceeding 90% for all event types. Together, these results demonstrate that both open-source reimplementations of SpliceAI successfully reproduce the predictive behavior of the original algorithm across multiple evaluation contexts, while consistently outperforming traditional splice prediction methods.

bioinformatics↗

bioRxiv2024

Analyzing Performance of Twist Bioscience Exome Enrichment with Spike-in CNV Backbone Panels at Various Probe Densities Leveraging Golden Helix VS-CNV Analysis Software

Clinical Whole Exome Sequencing (WES) offers a high diagnostic yield test by detecting pathogenic variants in all coding genes of the human genome. WES is poised to consolidate multiple genetic tests by accurately identifying Copy Number Variation (CNV) events, typically necessitating microarray analysis. However, standard commercial exome kits are typically limited to targeting exon coding regions, leaving significant gaps in coverage between genes, which could hinder comprehensive CNV detection. To convert microarray CNV calling with NGS, advances in both assay design and computational methods are needed. Addressing the need for comprehensive coverage, Twist Bioscience has developed an enhanced Exome 2.0 Plus Comprehensive Exome Spike-in panel with added CNV "backbone" probes. These probes target common SNPs polymorphic in multiple populations and are evenly distributed in the intergenic and intronic regions, with three varying densities at 25 kb, 50 kb, and 100 kb intervals from highest to lowest resolution respectively. Concurrently, Golden Helix has developed a multi-modal CNV caller designed specifically for target-capture NGS data to detect single-exon to whole-chromosome aneuploidy CNV events. This study evaluates the combined efficacy of the backbone-probe enhanced exome capture kit and VS-CNV 2.6 in identifying known CNVs using the Coriell CNVPANEL01 reference set. The integration of the enhanced capture kit with VS-CNV 2.6 achieved a 100% sensitivity rate for the detection of known CNV events at all three probe densities. The application of best-practice quality metrics, annotations, and filters was shown to have a minimal impact on this high sensitivity. These findings underscore the potential of the augmented Twist Exome in tandem with the VS-CNV caller and VarSeqs annotation and filtering capabilities. This combination presents a promising alternative to conventional microarray assays, potentially consolidating WES and CNV into a single assay obviating the need for additional testing in clinical CNV detection. The studys results advocate for the implementation of this integrated approach as a more efficient and equally sensitive method for CNV analysis in a clinical setting.

bioinformatics↗