Rebecca A. Reiman

DeepSomatic is a deep learning method for detecting somatic variants from short-read and long-read sequencing data, outperforming existing callers, especially for indels.

This study provides a multiomics resource of cancer extracellular vesicles, showing correlations between LO proteins and prostate cancer progression, and demonstrating the feasibility of single-cell RNA-Seq on EVs.

The paper presents diploid genome assemblies for two human fibroblast cell lines, enabling clone-specific variant calling, improved read mapping, and accurate phasing.

The paper provides a public resource of single nucleus RNA sequencing data and multiscale networks from Alzheimer's disease and non-disease individuals, linking genetic variants to molecular features and revealing novel disease biology.

The paper presents a multi-layered molecular dataset from iPSC-derived dopaminergic neurons to model Parkinson's disease genetic risk and provides a data browser for accessing these resources.

The paper compares transcriptomic responses in skeletal muscle and extracellular vesicles after traditional and high-intensity exercise in young adults, finding more consistent gene responses to traditional exercise and unique microRNA responses to high-intensity training.

The paper identifies genomic subtypes of multiple myeloma using the MMRF CoMMpass study and finds that 25.5% of patients transition to a high-risk expression subtype at progression, which expresses immunotherapy targets.