Kubal, M. | bioRxiv Science

bioRxiv2025

Evaluating a Standard Benchmark for Gene Prioritization: The InheriNext(R) Algorithm's Integration of Genomic and Phenotypic Information

This study presents a comprehensive benchmark analysis of InheriNext(R), a domain-specific, AI-powered tool designed for phenotype-driven pathogenic variant prioritization. For this study, 7,244 whole exome test cases were generated using phenotype and genotype data from Phenopackets, along with pools of variants from healthy individuals to serve as genomic backgrounds. Performance was evaluated across diverse testing scenarios and compared against four established tools. The results show InheriNext(R) achieving a 98.6% sensitivity in identifying pathogenic variants and consistent performance across diverse tests for variant types, phenotype counts, and disease groups--supporting the robustness and adaptability of its methodology. Sharing these benchmarking results and samples is intended to drive progress by assisting clinicians and researchers in evaluating interpretation tools and identifying areas for improvement.

genomics↗

bioRxiv2022

Crowdsourcing Temporal Transcriptomic Coronavirus Host Infection Data: resources, guide, and novel insights.

The emergence of SARS-CoV-2 reawakened the need to rapidly understand the molecular etiologies, pandemic potential, and prospective treatments of infectious agents. The lack of existing data on SARS-CoV-2 hampered early attempts to treat severe forms of COVID-19 during the pandemic. This study coupled existing transcriptomic data from SARS-CoV-1 lung infection animal studies with crowdsourcing statistical approaches to derive temporal meta-signatures of host responses during early viral accumulation and subsequent clearance stages. Unsupervised and supervised machine learning approaches identified top dysregulated genes and potential biomarkers (e.g., CXCL10, BEX2, and ADM). Temporal meta-signatures revealed distinct gene expression programs with biological implications to a series of host responses underlying sustained Cxcl10 expression and Stat signaling. Cell cycle switched from G1/G0 phase genes, early in infection, to a G2/M gene signature during late infection that correlated with the enrichment of DNA Damage Response and Repair genes. The SARS-CoV-1 meta-signatures were shown to closely emulate human SARS-CoV-2 host responses from emerging RNAseq, single cell and proteomics data with early monocyte-macrophage activation followed by lymphocyte proliferation. The circulatory hormone adrenomedullin was observed as maximally elevated in elderly patients that died from COVID-19. Stage-specific correlations to compounds with potential to treat COVID-19 and future coronavirus infections were in part validated by a subset of twenty-four that are in clinical trials to treat COVID-19. This study represents a roadmap to leverage existing data in the public domain to derive novel molecular and biological insights and potential treatments to emerging human pathogens. The data from this study is available in an interactive portal (http://18.222.95.219:8047).

bioinformatics↗