Wyeth W. Wasserman

This study compares functional, transcriptomic, and proteomic profiles of human primary and stem cell-derived beta cells in high and low insulin production states, revealing differences in resilience and protein machinery.

The paper identifies fibroblast growth factors (FGFs) as protective for human stem cell-derived insulin-secreting beta cells through a medium-throughput screen.

ExplaiNN provides interpretable deep learning models for genomics tasks, achieving comparable performance to state-of-the-art methods while offering transparent predictions.

The paper identifies small molecules that enhance stem cell differentiation into definitive endoderm, reducing costs by 90% through a drug repurposing approach.

This study identifies heterozygous gain-of-function STAT6 variants in two patients with severe allergic disease and shows JAK inhibitors reduce the enhanced STAT6 responses.

The paper introduces a variant catalogue pipeline to generate high-quality variant libraries from whole genome sequences, applied to 100 human genomes to improve variant cataloguing for diverse populations.

The paper identifies a human with complete NFAT1 deficiency, revealing its role in joint contractures, osteochondromas, and B cell malignancy through single-cell omic analyses.

The paper introduces the Allele Dispersion Score (ADS) to quantify allele frequency dispersion across populations using UMAP, avoiding population classification and privacy concerns.

This study shows that transfer learning improves transcription factor binding prediction by pre-training with biologically relevant TFs and fine-tuning single-task models.

FlexTyper enables reverse mapping of short reads to extract genomic insights without reference-guided alignment, demonstrating utility in genotyping, ancestry detection, and pathogen identification.

The paper examines X-chromosome inactivation across 12 species, finding mouse as an outlier with fewer escape genes and highlighting domain-level control and gene-based influences on inactivation.

GeneBreaker simulates rare genetic disease cases to benchmark variant calling and prioritization tools, aiding in the diagnosis of Mendelian disorders.

This study identifies a 4bp non-coding deletion in the FOXG1 locus at 14q12 as a candidate for isolated strabismus, showing its potential role in regulating FOXG1 expression.

metPropagate uses metabolomics data to prioritize candidate genes for neurometabolic diseases, achieving high accuracy in identifying causative genes for both known and novel IEMs.

This study uses multi-omic profiling of blood samples from ALD patients to identify molecular markers predictive of cerebral demyelination, but finds no statistically significant markers due to high inter-individual variation.

ExpansionHunter Denovo detects known and novel repeat expansions in short-read sequencing data without requiring predefined repeat catalogs.

The paper investigates transcription initiation at polyT short tandem repeats (STRs) using CAGE data and develops a deep learning model to predict STR-associated transcription with high accuracy.

TFEA.ChIP identifies transcription factor enrichment in gene lists using ChIP-Seq data, achieving high accuracy in identifying relevant TFs.

The paper introduces TF2Exp, a framework predicting gene expression impact from cis-regulatory variants by modeling altered transcription factor binding, showing comparable performance to SNP-based models.

The paper identifies two classes of human enhancers distinguished by sequence composition, activity, and genome organization, with one class specifically linked to immune response genes.

The paper identifies SIN3A's role in hypoxia-induced transcriptional changes, showing it represses 75% of hypoxia-repressed genes and inhibits 47% of hypoxia-induced genes.

CAGEd-oPOSSUM analyzes CAGE-derived TSSs to identify transcription factors regulating gene expression in specific cell types and conditions.

The paper identifies YY1's role in X-linked gene escape from inactivation by analyzing promoter activity and allelic binding in human datasets.