James T. Webber

The paper introduces LRAA, a framework for accurate isoform discovery and quantification in long-read RNA-seq data, outperforming existing methods across multiple benchmarks.

The paper evaluates and optimizes single-cell RNA isoform sequencing by analyzing artifacts from three platforms and introducing methods to reduce their impact.

Whole-brain snRNA-seq paired with Slide-seq assigns cell types to each mouse neuroanatomical structure; most clusters need at least three markers to be defined uniquely.

This study uses single-cell RNA-sequencing to analyze cell type-specific responses to young or aged blood in heterochronic parabiosis, identifying mitochondrial function as a key factor in rejuvenation.

The paper identifies a SARS-CoV-2 N gene polymorphism that reduces RT-PCR assay sensitivity and emphasizes the need for multi-target testing.

The paper introduces molecular cross-validation to select and calibrate denoising methods for single-cell RNA-seq data without ground-truth, showing it correctly identifies optimal parameters and best methods.

The paper analyzes aging dynamics across 17 organs and plasma proteomics in mice, revealing consistent gene expression trajectories with organ-specific timing and amplitude.

This paper introduces MAGNETIC, a method to integrate molecular data using functional networks, identifying gene modules in breast cancers and therapeutic biomarkers robust to differences between tumors and cell lines.