whole-exome sequencing

The paper identifies genomic features associated with response or resistance to Gem/Doce in BCG-unresponsive NMIBC using whole-exome sequencing and phylogenetic analysis.

This paper shows cosine similarity in RAG systems conflates clinically distinct cancer variants, proposing typed-graph retrieval as a more accurate alternative for precision oncology.

This paper evaluates Evo 2's ability to predict pathogenicity and mechanisms of cardiomyopathy variants, showing high accuracy and structural feature identification.

CN-RNN is a deep learning framework for detecting copy number variations from exome sequencing data, showing improved performance over existing methods.

SarcDBase is a tool that extracts and interprets genetic alterations from tumor sequencing data, showing high agreement with curated diagnoses in most cases.

mafsmith reimplements vcf2maf in Rust, achieving faster and more efficient conversion of VCF files to MAF with identical output across multiple datasets.

mehari predicts variant effects with a strict HGVS-first approach, achieving high speed and accuracy compared to existing tools.

tugMedi simulates cancer-cell evolution using patient genomic data to predict drug responses and tumor dynamics.

This paper develops a germline polygenic score (PRSagg) to predict prostate cancer aggressiveness and validates it across multiple datasets.

The paper analyzes genome sequencing data from UK Biobank to assess the penetrance of COL1A1/2 pathogenic variants in OI, finding lower penetrance than expected.

This paper introduces scClone2DR, a framework predicting tumour drug response at the clone level using single-cell DNA/RNA sequencing and drug-screening data, showing improved accuracy in identifying clonal sensitivities and resistance.

The paper develops a model to analyze X-linked rare variation in ASD, identifying 9 genes associated with ASD and explaining how X-linked variation could contribute to sex-differential prevalence.

The paper presents genetic and phenotypic data from 521 children with neurodevelopmental disorders in Kenya and South Africa, highlighting high genetic diagnosis rates linked to environmental risk factors.

This study identifies four molecular archetypes of lung adenocarcinoma premalignant lesions using bulk RNA and exome sequencing, linking proliferation-associated features to aggressive tumor outcomes.

This study characterizes the molecular differences in high-grade endometrial cancer across an ancestrally diverse cohort, identifying ancestry-associated mutations and immune microenvironment differences.

The paper introduces a framework for validating somatic mutation burden assays using relative validation and secondary metrics, implemented in SomaticCODEC with strong linearity and precision in human samples.

MOSAIC identifies driver mutations in cancer by accounting for patient subgroup heterogeneity in mutual exclusivity analysis, improving detection of subgroup-specific patterns.

MCNV2 assesses CNV quality using Mendelian Precision, offering tools to compute, explore, and optimize CNV call accuracy in parent-offspring trios.

This study uses multi-omics factor analysis to identify molecular profiles and biomarkers in gliomas, revealing relationships between methylation, genetics, and transcriptional patterns.

The paper describes the development of variant classification specifications for the VHL gene using ACMG/AMP guidelines, resulting in updated evidence codes and improved classification consistency.

The paper develops a method called Newmanization to reduce tissue signals in transcriptomic data and applies it to digestive tract cancer samples, showing improved clustering and separation compared to mutation data.

This paper reviews parental and caregiver needs during pediatric genome-wide sequencing, identifying informational and emotional support requirements across different stages of the process.

CMS improves sequencing uniformity and accuracy in non-canonical genomic regions by optimizing chemistry and enzymatic systems, reducing low-coverage bins and false negatives.

The paper introduces a framework to infer de novo exonic variants without parental genotypes, enhancing autism association studies by improving gene-based tests and controlling false discovery rates.

vcfsim simulates all-sites VCFs with missing data, enabling accurate population genetic studies and benchmarking.

This study identifies shared genetic factors across eight cardiovascular diseases using large-scale genetic data, revealing clusters and pathways related to vascular biology and systemic risk factors.

This study identifies rare genetic variants in Swedish SCAD patients, showing 4% with known SCAD-associated variants and 60% with potentially relevant variants in vascular-related genes.

This study identifies four immune subtype clusters in HNSCC using multi-omics data and shows their association with treatment response and survival.

BioAutoML-FAST provides an automated ML platform for biological sequence analysis, offering reusable models and benchmarking across genomic, transcriptomic, and proteomic tasks.

This study quantifies how patient-level factors and site-level practices affect diagnostic yield in exome and genome sequencing across five clinical sites.