Asa Shin

This paper develops computational tools for allelic imbalance analysis using single-nucleus RNA-seq and demonstrates their application in a Parkinson's disease cohort.

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.

CTAT-LR-fusion detects fusion transcripts from long and short read RNA-seq data, improving accuracy in bulk and single cell transcriptomes.