Ploy N. Pratanwanich

This study benchmarks Nanopore long read RNA sequencing in human cell lines, showing improved isoform resolution compared to short reads.

This paper introduces m6Anet, a Multiple Instance Learning framework for detecting m6A methylation from direct RNA sequencing data, achieving performance comparable to experimental methods without retraining.

This paper introduces xPore, a method to detect differential RNA modifications from direct RNA sequencing, showing it identifies m6A sites with single base resolution and quantifies modification rate differences across cell lines.

This paper introduces a scalable latent-factor model for scRNA-seq data that separates biological drivers from confounding effects using pathway annotations and improves gene set annotations.