Fereydoun Hormozdiari

VESPA-SS detects somatic structural variants from long-read sequencing data without requiring read mapping or a high-quality germline reference, achieving high F1 scores on cancer cell lines.

This study identifies oncRNAs as drivers of HPV-negative head and neck cancer and shows their potential for pre-diagnostic detection.

Exai-1 is a transformer-based model that integrates RNA sequence and expression data to improve cfRNA analysis for disease detection.

This study evaluates structural variant callers using long-read sequencing data, showing performance variation based on ground truth, reference genome, and genomic regions.

This paper introduces an assembly-free method to augment pangenome graphs using long reads from new samples, without aligning to a reference or calling variants.

The paper introduces MoToCC, a method to identify critical cell-types associated with neurodevelopmental disorders using genetic modules and single-cell data.

The paper presents a high coverage whole genome sequencing resource of 3,202 samples, including 602 trios, generated from the expanded 1000 Genomes Project.

SVDSS improves structural variant discovery in hard-to-call regions using long-read sequencing by combining multiple methodologies, outperforming existing methods in repetitive genomic regions.

MAGI-MS identifies genetic modules enriched in disease-relevant genes by integrating multiple seed genes associated with neurodevelopmental disorders.

Nebula efficiently genotypes structural variants without requiring alignment, outperforming mapping-based methods in speed while maintaining accuracy.

The paper introduces a method to identify sample-specific strings in long-read sequencing data for comparative genome analysis without read mapping.

The paper analyzes 41 diseases and traits to show transposable elements contribute uniquely to disease heritability, with older elements and SINEs showing specific enrichment patterns.

Kevlar identifies de novo variants without mapping reads to a reference genome, achieving high sensitivity and specificity in simulated and real pedigrees.

This study identifies a large number of structural variants in human genomes using multiple sequencing technologies and presents them as a gold standard for future studies.

The paper develops TARDIS, a tool for accurately detecting complex structural variations, including tandem and interspersed segmental duplications, using short-read whole genome sequencing data.

The paper introduces a method to score and rank structural variants based on their impact on TAD fusion, showing it outperforms existing approaches and identifies deleterious deletions.

This paper introduces a combinatorial framework, Odin, for early prediction of neurodevelopmental disorders using rare coding variation and functional information, achieving 8% additional autism case detection with less than 1% false positive rate.

The paper identifies 301 neurodevelopmental disease genes using de novo mutations and CNV data, finding overlaps with genomic disorder regions and highlighting new functional networks.