whole-exome sequencing

The paper identifies non-coding variants in CDKN2B that disrupt CTCF binding, reduce CDKN2A expression, and increase melanoma risk in families.

This study identifies rare variants and digenic interactions associated with congenital heart disease using a large-scale exome case-control analysis.

The paper develops a standardized case-level categorization metric for genomic results across the CSER2 consortium to assess diagnostic yield and identify resolvable factors in inconclusive cases.

LCR-modules provides a flexible, automated suite of workflows for cancer genome analysis, supporting diverse sequencing types and enabling reproducible, scalable analysis of over 10,800 samples.

The paper introduces the single mutation frequency spectrum (SMFS) to account for recurrent mutations in the site frequency spectrum of rare alleles, showing its application to human exome data.

The paper identifies ultrarare genetic variants in intestinal homeostasis genes in youth with neuropsychiatric deteriorations and gastrointestinal symptoms, suggesting a potential link to disease pathogenesis.

This study evaluates RNA-seq's diagnostic utility in three patient groups with non-diagnostic exome sequencing, finding higher yields in groups with candidate variants.

The paper identifies a common BRCA1 founder variant in Rwandan breast cancer patients and estimates its origin to be around 4,000-10,000 years ago.

The paper identifies monogenic variants in the Russian population associated with adverse drug reactions, highlighting their potential to improve personalized pharmacotherapy.

BINSEQ introduces binary formats for sequencing data to enable faster parallel processing, reducing analysis time for genome and transcriptome studies.

The paper analyzes somatic mutations and transcriptional profiles in breast tumors from 748 Hispanic/Latina women, finding differences in tumor immune microenvironment ecotypes linked to ancestry and APOBEC3A/B copy-number deletions.

This study identifies population-specific variants in Jordanian individuals, revealing admixture patterns and reclassifying variants to improve clinical interpretation.

This study detects low-level mosaic variants in blood DNA from individuals with congenital hyperinsulinism using tNGS and confirms them with ddPCR.

The paper identifies 800 exome-wide significant rare coding variants associated with blood lipids in over a million diverse individuals, including 130 driven by non-European-like populations.

DAVP uses an agentic-LLM pipeline to prioritize genetic variants for diagnosis, achieving high recall in identifying causal variants across multiple datasets.

The paper investigates the differential contributions of MUTYH and PALB2 germline variants to colorectal polyposis and breast cancer using mutational signatures in a single patient.

The paper identifies BRCA1/2 reversion mutations associated with drug resistance in cancer patients and highlights key domains critical for resistance.

This study identifies novel genetic drivers of orofacial clefts by analyzing de novo variants in 2,497 trios and integrating findings across subtypes and syndromic status.

The paper establishes a global consortium to discover rare genetic associations by integrating data from multiple biobanks, identifying 514 gene-trait associations, many detectable only through cross-ancestry meta-analysis.

AFQuery computes capture-aware allele frequencies for clinical genomics cohorts, reducing errors and bias in variant classification.

This paper evaluates generalist large language models (LLMs) in tumor genomic interpretation tasks, finding they can complement or outperform task-specific models in some cases.

ParaDISM improves read placement and variant calling accuracy in highly homologous genomic regions by reducing misalignment artifacts.

This study benchmarks local ancestry inference across different genotyping assays and parameters, showing that ultra low-pass WGS with GLIMPSE2 achieves high accuracy, while SNP microarrays perform poorly.

CharacTERT classifies hTERT missense variants using machine learning models that integrate sequence and structure-based features, achieving high accuracy and sensitivity in predicting pathogenicity.

This study uses exome and genome sequencing to identify 68 genes directly associated with inflammatory bowel disease, providing clearer genetic insights and therapeutic targets.

VX provides a genome and transcriptome viewer with an embedded API and analysis framework enabling script and AI agent interaction.

This study predicts neoantigen repertoires in MPNSTs and identifies immune-related differences between PRC2-Loss and PRC2-WT subtypes, highlighting potential immunotherapy targets.

This paper introduces ClinVar-BERT, a model that prioritizes variants for clinical review by analyzing variant summaries and distinguishing functional evidence, achieving high accuracy in predicting loss of function.

The paper compares automated and manual reanalysis of rare disease genome sequencing data, finding automated methods achieve similar results with less effort.

The paper introduces CANCAN, a computational framework for analyzing copy number and mutation heterogeneity in DNA sequencing data, showing high concordance with existing methods and improved performance on targeted sequencing data.