Peter S. Choi

This paper introduces TrASPr+BOS, a generative AI model for predicting and designing RNA to achieve tissue-specific splicing outcomes, showing improved performance over existing methods.

The paper identifies sequence features associated with recursive splicing and develops a classifier to detect these features.

The paper improves sQTL modeling by incorporating alternative splicing representation and introduces MAJIQTL, finding new sQTLs linked to Alzheimer's disease.

This study investigates sources of non-uniform coverage in RNA-Seq data and finds that no single factor explains the issue, contradicting prior claims.

This paper introduces LSV-seq, a targeted RNA-seq method optimized with machine learning to detect alternative splicing with high sensitivity and low sequencing depth.

BEERS2 simulates RNA-seq reads with high fidelity, incorporating detailed library preparation and sequencing steps, and evaluates its impact on the Salmon pseudoalignment algorithm.