Diana Baralle

The paper investigates missense spliceogenic variants (MSVs) using genomic and proteomic data, finding they are common and can cause disease through splicing disruption, often missed by protein-centric tools.

This study identifies immune-related gene expression differences in paediatric patients with SARS-CoV-2 compared to other lower respiratory tract infections.

The paper uses blood-based RNA-Seq on 5,412 individuals with rare diseases to identify new candidate diagnoses and shows its utility in prioritizing genetic variants.

The paper identifies non-canonical FBN1 splice variants in undiagnosed Marfan syndrome families using genome sequencing and RNA testing, finding 20 candidate variants with significant enrichment in FTAAD cases.

MAJIQ-CLIN improves the detection of Mendelian disease-causing variants from RNA-Seq by addressing splicing aberrations, showing better accuracy and efficiency than existing tools.

The paper develops a framework for classifying variants of reduced penetrance in cancer susceptibility genes to improve variant interpretation consistency.

GenePy identifies potential missed biallelic diagnoses in the 100KGP by scoring variants and analyzing phenotypic overlaps, finding 14% of undiagnosed participants may have a missed diagnosis.

The paper evaluates splicing prediction methods on 56 clinically validated variants, finding some methods match or exceed SpliceAI's performance but highlighting ongoing challenges in accuracy.

The paper identifies de novo monoallelic DDX17 variants in 11 patients with neurodevelopmental disorders and shows DDX17 dysfunction causes neuronal migration and axon outgrowth defects in multiple models.

This study identifies disease-causing promoter and untranslated region variants in 8,040 undiagnosed rare disease patients using a new framework and finds 12 variants with potential pathogenicity.

This study uses RNA-sequencing to identify novel diagnoses in Mendelian disorders by detecting aberrant splicing and expression abnormalities, particularly in patients without candidate sequence variants.

The paper identifies non-coding variants in trans with coding variants in patients with developmental disorders, finding them to be a rare cause of recessive disorders.

The paper compares a panel-agnostic variant filtering approach (HiPPo) with a gene panel-based strategy in exome and genome data from NHS patients, finding HiPPo identifies more diagnostic variants with fewer assessments.

The paper introduces DeNovoLOEUF, a method to identify diagnostic variants in genome sequencing data, achieving 96% positive predictive value and uncovering 39 missed diagnoses in the 100,000 Genomes Project.

PhenoScore quantifies phenotypic similarity using AI and HPO data to improve genetic variant interpretation and genotype-phenotype studies.

This study analyzes splicing variants in whole-genome sequencing data to identify new diagnoses in rare diseases.

This paper provides recommendations for interpreting non-coding variants in clinical settings, adapting existing guidelines to account for their role in disease.

The paper introduces MRSD, a metric to determine optimal RNA-seq depth and tissue for assessing splicing variants in Mendelian diseases, showing LCLs are best for most gene panels.

The paper compares immune responses in influenza and SARS-CoV-2 infected patients and COVID-19 survivors using transcriptomic data, identifying distinct immune signatures and prognostic indicators.

The paper identifies a novel short ACE2 isoform expressed in respiratory epithelia that is upregulated by interferon and RNA virus infection but not SARS-CoV-2, and shows reduced SARS-CoV-2 binding affinity.

The paper identifies novel truncating mutations in CTNND1 associated with craniofacial and cardiac anomalies, expanding the known phenotypic spectrum of the gene.

The paper identifies epigenetic aging abnormalities in growth disorders caused by mutations in epigenetic regulatory molecules.

The paper analyzes de novo mutations in developmental disorders using exome sequencing of 4,293 families, identifying 94 genes and estimating a birth prevalence of 0.22-0.47%.