Genomics and Rare Diseases

MVP uses deep learning to predict pathogenicity of missense variants, outperforming previous methods in prioritizing pathogenic variants.

The paper develops a machine learning model to predict genes causing rare neurological disorders and shows it accurately identifies disease-associated genes.

This study finds enrichment of pathogenic variants linked to treatable genetic disorders in psychiatric populations, particularly in schizophrenia.

The paper assesses benign genetic variability in elderly individuals without neurodegenerative disease to aid in distinguishing pathogenic mutations.

The paper evaluates long indel detection in exome sequencing data from intellectual disability patients, identifying three pathogenic indels and highlighting the importance of accurate variant calling.

The paper screens nsSNPs in the CDKN1A gene using in silico methods and identifies 12 variants predicted to be deleterious by multiple algorithms.

SeqsLab annotates and interprets genetic variants from large personal genome cohorts using Spark for scalability and integrates with public databases for variant analysis.

This study evaluates synthetic specimens with challenging variants across 10 NGS workflows, finding limited variant detection consistency and highlighting bioinformatic limitations.

The paper analyzes mutation phase in diploid human genomes and finds that protein-altering mutations are significantly more common in cis than trans configurations across diverse populations.

This paper presents a pipeline for identifying and prioritizing causal variants in genetic disorders using exome or whole genome sequencing data, achieving success in over 35% of neurodevelopmental disorder cases.

The paper identifies biallelic UNC45A mutations as the cause of O2HE syndrome, characterized by cholestasis, congenital diarrhea, hearing loss, and bone fragility.

The paper analyzes rare variants in dystonia genes in population controls and sporadic dystonia patients to assess their clinical relevance.

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

The paper develops a paralog conservation score to identify disease-associated genes and shows that disease variants are enriched at conserved sites across gene families.

The paper assesses de novo missense variants in autism spectrum disorder using Evolutionary Action to identify pathway biases and links variant impact to phenotypic severity.

The paper assesses exome sequencing in a healthy cohort to determine the frequency of medically actionable and non-actionable findings, finding 17% had actionable variants, including some outside standard guidelines.

Sub-genic missense depletion in ExAC anchors MPC, a deleteriousness score; de novo missense variants with MPC ≥ 2 occur in neurodevelopmental cases at 5.79 times the control rate.

The paper evaluates variant calling tools and introduces sORF-c, a machine learning model to prioritize variants in short open-reading frame regions of the human genome.

The paper identifies functionally important protein residues by analyzing missense variant distribution across human protein domains and finds these residues are enriched in disease-associated variants.

RETA is an R package that simplifies whole exome and targeted region sequencing data analysis by providing a comprehensive, interactive report with advanced visualization tools.

The paper uses somatic mutation patterns from cancer to predict genes involved in phenotypic abnormalities and genetic diseases, showing their predictive power is independent of germline-based methods.

The paper develops a gene-specific meta-predictor for variant pathogenicity using a Random Forest model trained on the CFTR gene and evaluates its performance against existing tools.

This work improves pathogenicity prediction of missense mutations by incorporating sequence context, achieving accuracy comparable to state-of-the-art methods.

The paper predicts that Mendelian disease missense mutations are enriched in conserved amino acids and cause greater protein instability than benign mutations.