Gill Bejerano

The paper develops criteria for identifying, sharing, and reporting novel candidate genes in clinical genomic testing to improve diagnostic yield for Mendelian conditions.

MonoMiner identifies monogenic disease patients with confirmed molecular diagnoses from clinical notes, achieving high precision and recall in a large hospital system.

AIMHIGH identifies novel disease-causing genes by analyzing inbred mouse strain data and machine learning-driven literature-based discovery.

This study evaluates the impact of population structure on murine GWAS and finds minimal effects in most datasets, with some cases where PS correction may reject true signals.

InpherNet improves disease gene hypothesis generation by leveraging gene network neighbors, achieving higher accuracy than existing methods in prioritizing causative genes.

The paper introduces a cryptographic algorithm that allows private DNA profile matching against a CODIS database without revealing the profile to the database provider.

The paper identifies de novo PRKAR1B variants in six individuals with neurodevelopmental disorder, autism, apraxia, and reduced pain sensitivity.

The paper analyzes structural variation in inbred mouse strains to identify genetic factors associated with autism-related traits.

AMELIE 3 automatically reanalyzes undiagnosed Mendelian disease patients using new literature, achieving 80% diagnostic yield with 0.5-1 alerts per patient per year.

The paper shows Neurog1 controls olfactory organ morphogenesis through cxcr4b in zebrafish.

The paper identifies transcriptional networks in developing mouse projection neurons by correlating chromatin accessibility with gene expression to understand cell fate diversity.

The paper identifies 500+ high-confidence gene loss events in 58 mammals using automated methods, revealing some linked to human disease and early lethality.

AMELIE 2 improves Mendelian disorder diagnosis by matching patient phenotypes and genotypes to primary literature, achieving high accuracy and efficiency.

The paper identifies de novo variants in EIF2AK1 and EIF2AK2 associated with neurodevelopmental disorders and leukoencephalopathy, showing similar pathogenic mechanisms to CACH/VWM.

The paper uses DeGAs to identify genetic components across 2,138 phenotypes in the UK Biobank, revealing novel adipocyte biology through variant and gene contributions to obesity-related traits.

AVADA automatically curates a database of pathogenic variants from full-text literature, outperforming existing methods and providing more variants than ClinVar.

This paper develops secure protocols to protect patient privacy during disease diagnosis by enabling secure computations on both genotypic and phenotypic data in two scenarios: cohort discovery and gene prioritization.

WhichTF identifies dominant transcription factors from open chromatin data and finds they are functionally important in various cell types and contexts.

Darwin accelerates genomic sequence alignment with hardware acceleration, achieving significant speed and energy efficiency improvements over existing methods.

S-CAP improves pathogenicity prediction for splicing-relevant variants by reducing uncertain variants by 41% with 95% sensitivity.

ClinPhen automatically extracts and prioritizes patient phenotypes from medical records to improve genetic disease diagnosis efficiency.

This paper introduces a deep learning model, LaBranchoR, that predicts RNA splicing branchpoints with high accuracy, showing better reliability than existing annotations.

This study evaluates the use of whole blood transcriptome sequencing for diagnosing rare diseases, finding an 8.5% diagnostic rate across 56 undiagnosed cases.

Phrank improves Mendelian disease prioritization by ranking candidate genes or diseases based on phenotype similarity, outperforming existing methods in real patient exome data.

The paper identifies 48 limb enhancers with accelerated evolution in Galapagos cormorants, suggesting non-coding regions contribute to flightlessness.

This paper develops secure multiparty computation methods to identify causal variants in Mendelian diseases without exposing patient genomes.

AMELIE automates Mendelian disorder diagnosis by analyzing literature and prioritizing candidate genes, achieving high diagnostic accuracy for singleton patients.