Li Tai Fang

This study benchmarks five scCNV inference methods across multiple scRNA-seq platforms and mixed samples, finding variations in performance based on data characteristics and batch effects.

The paper evaluates variant calling in difficult genomic regions using short- and long-read sequencing, showing improved performance with new methods and multi-technology approaches.

The paper presents whole-genome and whole-exome sequencing datasets from a multi-center, cross-platform study to benchmark NGS technologies and bioinformatics pipelines in cancer genomics.

The paper establishes reference samples with high-coverage whole-genome sequencing to create truth sets for somatic mutation and germline variant detection using NGS technologies.

This study evaluates reproducibility and accuracy of cancer mutation detection using whole-genome and whole-exome sequencing across multiple sequencing centers and bioinformatics pipelines.

This study develops and validates deep learning strategies for robust cancer mutation detection using tumor-normal sequencing data across multiple technologies and scenarios.