Gad A. Getz

The paper identifies clustered structural variant hotspots in osteosarcoma, linking them to oncogenic addiction and plasticity through transcriptional changes and clinical detectability.

The paper develops ALTitude, a machine learning model to predict ALT status from whole genome sequencing data, identifies SMARCAL1 as a therapeutic target in ALT-driven cancers.

The paper analyzes over 800,000 human genomes to assess penetrance of clinically relevant variants, finding that 95% of pLoF variants in severe diseases have explanations for lack of disease manifestation.

This study uses single-cell RNA sequencing to identify a CD6/ALCAM-mediated immune-suppressive pathway in recurrent NMIBC, suggesting CD6 as a potential therapeutic target.

This paper introduces scSet, a diffusion-based autoencoder that learns patient-level representations from single-cell data, outperforming existing methods in clinical prediction with limited labeled data.

This study creates a biliary tract cancer cell line atlas using multi-omics data to identify molecular subtypes and therapeutic targets.

SVelfie identifies cancer driver genes by analyzing enrichment of functional structural variants in cancer genomes.

This study examines the evolutionary dynamics of peritoneal metastases in colorectal cancer, finding they are more heterogeneous than liver metastases but less diverse than locoregional metastases.

scDiffEq models single-cell dynamics using neural stochastic differential equations, improving trajectory reconstruction and fate prediction in hematopoiesis.

Rare-variant depletion in 1kb windows of 76,156 gnomAD genomes, versus feature-adjusted mutation expectations, marks regulatory elements and outperforms seven alternative scores at flagging functional non-coding variants.

The paper develops a method to infer compound heterozygosity from exome sequencing data, achieving high accuracy in phasing rare variants for recessive disease diagnosis.

An evaluation of scBERT and scGPT on cell type annotation against L1-regularized logistic regression, with ablations of pre-training and tests of fine-tuning stability.

This study identifies molecular and immune features associated with response to anti-PD-(L)1 therapy in NSCLC using a cohort of 393 patients.

The paper develops DNA- and RNA-based models combining immune and tumor features to predict outcomes of checkpoint inhibition in melanoma.

PANOPLY is a cloud-based platform that automates and reproducibly analyzes proteogenomic data by integrating genomic, transcriptomic, proteomic, and post-translational modification data.

The paper analyzes the mutational landscape of normal lymphocytes, revealing off-target effects of programmed genome engineering and the impact of differentiation and microenvironment exposure.

This study identifies genomic signatures of chromosomal instability in Barrett's esophagus progressing to esophageal adenocarcinoma, showing sloping copy-number variation as a precursor to complexity.

The paper introduces a framework for assessing the quality of whole genome cancer sequences using a star rating system based on five QC measures, providing a tool for researchers to evaluate and exclude low-quality data.

The paper presents a large ASE resource from GTEx v8 with 431 million SNP-level and 153 million haplotype-level measurements, and extends phASER to estimate cis regulatory variant effect sizes.

The paper uses bulk RNA-seq data to estimate cell type abundances and identifies cell type interaction QTLs that provide finer resolution of tissue specificity than bulk cis-eQTLs.

The paper examines telomere length variation across 6,391 tissue samples from 952 individuals, showing tissue-specific differences, positive correlations between tissues, and associations with age and ancestry.

Integrating expression, allelic expression and splicing across tissues identifies rare variants with relevance to traits.

The paper quantifies protein levels across 32 human tissues and compares them to transcriptome data, revealing insights into post-transcriptional regulation and disease mechanisms.

The paper evaluates potential drug targets using loss-of-function genetic variants from gnomAD, highlighting the importance of deep curation and targeted recall studies for drug discovery.

From 125,748 exomes and 15,708 genomes, 443,769 high-confidence predicted loss-of-function variants yield LOEUF, a continuous gene intolerance score tracking mouse lethality, cell essentiality, and disease associations.

The paper develops an expression-based annotation metric to improve rare variant interpretation by filtering false-positive loss-of-function variants and identifying pathogenic variants in highly expressed exons.

The paper analyzes loss-of-function variants in LRRK2 across large genomic datasets to assess the safety of partial LRRK2 inhibition as a Parkinson's disease therapy.

The paper systematically studies uORF variants in 15,708 genomes, showing they are under strong negative selection and contribute to disease.

The paper analyzes multi-nucleotide variants (MNVs) in 125,748 exomes and 15,708 genomes, identifying their distribution, mutational origins, and functional implications.

The paper introduces the Cancer LncRNA Census (CLC), a curated resource of lncRNAs with causative roles in cancer, and shows their genomic and evolutionary features.