Stephen B. Montgomery

The paper reports on the All of Us multi-omics cohort, identifying 182,246 QTLs and demonstrating rare variant effects across proteomic and transcriptomic data.

The paper introduces Watershed-DL, a framework integrating sequence-to-omics predictions and multi-omics data to prioritize rare variants, finding it increases prioritized variants by 61.4% and identifies candidate disease alleles.

MetaDome now supports GRCh38 alongside GRCh37, adds coordinate lookup, and identifies 52,463 GRCh38 ClinVar missense variants of uncertain significance with uncontradicted pathogenic evidence at homologous Pfam-domain positions.

The paper develops a model to predict NMD outcomes for protein-truncating variants using RNA-seq data, achieving ~78% ROC-AUC and identifying non-canonical determinants.

The paper analyzes multi-omic, multi-tissue molecular responses to acute exercise in sedentary adults, identifying key pathways and exerkines involved in exercise effects.

This paper benchmarks 7 TR genotyping tools using Nanopore long-read data, finding that no single tool is best across all metrics, and that sequence-level accuracy is crucial for clinical and population studies.

This paper develops a pangenome-based method to improve trait association and genomic prediction by capturing complex genetic variation missed by traditional approaches.

METAFORA calls depth-aware, covariate-adjusted methylation outlier regions from PacBio and Nanopore long reads; in 551 rare-disease genomes, outliers tracked structural variants, repeat expansions, and imprinting defects.

The paper introduces a data model and dataset for rare disease research, enabling interoperable multi-omic data integration across clinical sites.

This study identifies tissue-specific lncRNA responses to endurance exercise in rats, revealing sex differences and potential therapeutic implications.

The paper introduces a multi-gene eQTL mapping framework to detect variants influencing multiple neighboring genes, finding novel loci and improving colocalization with GWAS variants.

The paper identifies tissue-specific transcriptional regulatory programs in rats undergoing endurance exercise training by integrating chromatin accessibility, DNA methylation, and gene expression data.

The paper uses a single-cell multi-omic atlas and deep learning to identify cis-regulatory elements and genetic drivers of heart development and disease.

The paper uses long-read genome sequencing and multi-omics data to identify structural variants associated with aging and neurodegenerative diseases, finding that SVs are more likely to be causal than SNVs.

The paper identifies individuals with minor spliceopathies by analyzing transcriptome-wide splicing outliers in RNA-seq data from rare disease cohorts.

The paper studies how dietary metabolites interact with genetic variants to influence colon cancer risk through transcriptional regulation.

The paper uses single-nucleus multiome sequencing to analyze gene regulatory changes in human skeletal muscle after endurance exercise, identifying fiber-type specific regulatory programs and transcription factor networks.

This study identifies a conserved PRP4K-CHMP4B/vps32 splicing circuit that regulates autophagy in both human cells and Dictyostelium discoideum.

The paper integrates long-read sequencing and gene expression to prioritize functional structural variants and tandem repeats in rare disease patients, identifying potential causal variants in a neurodevelopmental disorder.

The paper predicts the regulatory effects of non-coding variants across brain and heart cellular contexts using deep learning, identifying rare variants with significant developmental impacts.

The paper investigates functional rare variants on the X-chromosome and autosomes, showing sex-biased effects and pharmacogenomic implications.

The paper evaluates how base editor design features affect editing efficiency and mutation patterns in mammalian cells, identifying DivA-BE as the most effective variant.

This paper introduces a consensus variant-to-function score to prioritize disease-causing variants, showing improved performance in heritability enrichment and fine-mapping.

The paper uses long-read sequencing on 100 samples from the 1000 Genomes Project to identify structural variants and improve understanding of human genetic variation.

The paper expands the lncRNA catalog using capture long-read RNA sequencing, significantly increasing the number of annotated transcripts in human and mouse genomes.

Mostly de novo variants in an 18 bp constrained region of U4 snRNA gene RNU4-2 cause a syndromic neurodevelopmental disorder, estimated to explain 0.41% of cases.

This study examines how different genome builds affect RNA-seq results in rare disease samples and recommends cross-referencing with a database of build-dependent genes for more accurate transcriptomics analysis.

This study examines how endurance exercise training affects rat subcutaneous white adipose tissue, revealing sex-specific molecular responses and differences in adiposity and metabolism.

The paper introduces regionalpcs, a method for summarizing DNA methylation patterns, showing improved sensitivity in detecting associations with Alzheimer's disease traits.

The paper identifies novel transcripts, eQTLs, and caQTLs in African populations and fine-maps GWAS signals for complex diseases.