Polly S. Y. Poon

This study benchmarks tumor-naive ctDNA detection and quantification methods using WGS and methylation data from cancer patients, identifying performance trade-offs and optimal integration strategies.

The paper benchmarks 9 somatic variant calling algorithms on cfDNA samples from cancer patients, identifying optimal methods based on ctDNA levels and sequencing depth.

This study introduces NDRDiff, a computational framework for analyzing cfDNA fragmentation profiles to infer gene expression and identify differentially expressed genes in cancer.

Fragle quantifies ctDNA from fragment length profiles using deep learning, showing high accuracy and improved detection limits across multiple datasets and sequencing types.

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 develops a targeted cfDNA sequencing approach for detecting genomic alterations in breast cancer using UMIs and bioinformatics pipelines, showing high concordance with tumor sequencing.