Mahdi Sarmady

This study evaluates how in silico predictors influence clinical variant classification, finding that PP3 and BP4 criteria are frequently applied and often affect variant classifications.

The paper develops a Bayesian method to detect balancing selection across different sequencing technologies and identifies novel genes under selection in complex genomic regions.

AnthOligo automates the design of oligonucleotides for genomic capture technologies by optimizing parameters like Tm and ΔG while minimizing primer dimers.

Phen2Gene prioritizes genes based on HPO terms for rare diseases, outperforming existing tools in speed and accuracy for clinical diagnosis.

The paper demonstrates that RNA-seq of B lymphoblastoid cell lines can effectively diagnose neurodevelopmental disorders, including Cornelia de Lange syndrome, with high sensitivity.

The paper presents a machine learning classifier to identify true somatic variants from next-generation sequencing data, achieving high specificity and sensitivity with minimal uncertainty.

The paper introduces a disease-specific variant pathogenicity predictor trained on clinical sequencing data from cardiomyopathy, epilepsy, and RASopathies patients.

Phenoxome improves clinical exome variant prioritization by integrating phenotypic and genomic data, achieving high accuracy in identifying causative variants.

ExomeSlicer is a web-based resource that identifies technically challenging regions in exome data, aiding in the development and validation of exome-based clinical panels.