Chen Yang

Benchmarking six long-read RNA-seq protocol-platform combinations and 14 tools across human, mouse, and manatee shows read length and accuracy drive isoform detection, depth drives quantification.

RNA-Bloom2 is a reference-free transcriptome assembler for long-read data, showing competitive performance with reference-based methods and lower resource usage.

Meta-NanoSim simulates and characterizes nanopore metagenomic data to improve microbial abundance estimation and guide algorithm development.

AMPlify is a deep learning model that predicts antimicrobial peptides from bullfrog genomes, identifying novel peptides effective against WHO priority pathogens including drug-resistant bacteria.

This study uses Mendelian randomisation to show that higher serum CC-16 levels protect against COPD risk and progression, with genetic variants linked to CC-16 expression in lung tissue.

RNA-Bloom is a de novo RNA-seq assembler for scRNA-seq data that reconstructs cell-specific isoforms and detects gene fusions, outperforming bulk RNA-seq methods.

Trans-NanoSim simulates nanopore RNA-seq reads using features learned from real data, enabling cost-effective development of analytical tools.

Terminitor predicts polyadenylation cleavage sites from RNA-seq data, outperforming existing tools in sensitivity and precision on experimental data.

The paper analyzes APA patterns in 114 cancer-related genes across 33 cancer types to show complex 3 UTR cleavage changes in tumors.

The paper reviews and benchmarks overlap detection tools for error-prone long reads, evaluating their performance, efficiency, and scalability.

NanoSim simulates nanopore sequencing reads with ONT-specific characteristics, enabling testing and development as the technology evolves.