Akanksha Khorgade

This paper introduces a high-throughput single-cell RNA isoform sequencing method using sequencing-by-expansion (SBX) and the CUDLL algorithm, achieving high accuracy and enabling isoform-level analysis across cell types.

This study evaluates the diagnostic yield of short-read and long-read sequencing in rare diseases, finding limited added value from long-read sequencing despite increased variant discovery.

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 constructs a single-cell splicing atlas of human CD8 T cells and identifies isoform-specific immune checkpoints that enhance effector function and tumor control.

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.