Genomics and Rare Diseases

The paper builds a cohort-specific allelic frequency database to prioritize variants and genes in non-solved inherited retinal dystrophies, identifying 8 potential causative variants and 18 genes for further study.

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 updates ACMG/AMP criteria for RYR1-related MHS and classifies 251 variants, finding that many VUS classifications lack sufficient evidence and suggesting functional studies are needed.

This study identifies ANKRD11 missense variants as a cause of KBG syndrome by impairing protein stability or transcriptional activity, supporting haploinsufficiency as the underlying mechanism.

StabilitySort prioritizes pathogenic genetic variants by assessing their impact on protein stability using AlphaFold2 predictions.

This paper outlines a community-driven initiative to create a consolidated catalog of human Ribo-seq ORFs, addressing the lack of standardized annotation for these features.

This study identifies ultra-rare genetic variants in PANS patients through exome and whole genome sequencing, revealing candidate genes involved in both immune and neuronal pathways.

The paper identifies that WSS-causing variants in KMT2A cluster in the CXXC domain and demonstrates AlphaFold2's ability to accurately classify these variants.

DeNovoCNN is a deep learning tool for de novo variant calling in sequencing data, achieving high recall and precision compared to existing methods.

The paper identifies deleterious SNPs in the ACD11 gene of Arabidopsis using computational methods and molecular docking, showing their impact on protein structure and function.

eXNVerify evaluates genome coverage for clinical sequencing data, focusing on pathogenic variants in BRCA1, TP53, and CFTR, and is available on GitHub and DockerHub.

The paper investigates the phenotypic effects of rare variants in developmental disorder genes in the general population, finding milder related traits in carriers.

The paper uses a Markov Random Field model with protein-protein interaction data to identify 46 candidate genes for congenital heart disease, including known and highly expressed genes.

This paper introduces EncoreDNM, a statistical framework to quantify shared genetic effects of de novo mutations between disorders using exome-wide summary data, revealing abundant pairwise enrichment correlations.

The paper evaluates three DNA sample tracking tools in a whole exome sequencing workflow, finding the pxlence kit most reliable and cost-effective.

The paper presents simulated exome-sequencing data for 150 families with lymphoid cancer to study genomic patterns and disease inheritance.

The paper identifies genetic and chemotherapeutic causes of germline hypermutation by analyzing genome-wide sequences of families with rare genetic diseases.

The paper identifies functionally consequential noncoding variants in undiagnosed rare disease patients using a massively parallel reporter assay and integrative analysis.

The paper describes the formation of the Gene Curation Coalition to standardize gene-disease validity terminology and create a unified database for curated gene-disease assertions.

The paper validates a whole genome sequencing protocol for diagnosing rare diseases in Brazil, showing high accuracy for SNVs and indels but limitations in detecting certain structural variants.

This paper introduces FINSURF, a machine learning tool that improves the prediction of pathogenic non-coding variants, achieving high accuracy in identifying disease-causing variants in clinical settings.

The paper trains a machine learning model on human retinal epigenomic data to predict the functional impact of non-coding regulatory variants.

STRipy simplifies STR genotyping in HTS data by providing a user-friendly tool that targets all known disease-causing STRs and handles long alleles.

This study shows that adjusting for ancestry improves genome-wide estimates of regional intolerance by incorporating demographic history, leading to better variant classification.

The paper implements RNA-seq in diagnostics for Mendelian diseases, achieving a 16% diagnosis rate in WES-inconclusive cases by detecting aberrant expression, splicing, and mono-allelic expression.

The paper identifies a de novo missense variant in KCNC2 in two affected siblings with autism and epilepsy using long-read sequencing, showing its potential clinical relevance.

CADD-SV scores structural variants using annotations and random forest models, correlating with known pathogenic and non-coding variants affecting gene expression.

The paper describes the RYR1 variant landscape in a Singapore cohort using whole genome sequencing, identifying pathogenic variants and their allele frequencies across ethnic groups.

The paper describes the process of obtaining an FDA IDE for returning genomic results via WGS in the All of Us Research Program and presents validation results.