Ru Feng

Torino, a reference-free framework for isoform discovery, identifies RNA processing patterns linked to disease risk by analyzing read coverage variation across species and disease states.

The paper presents an xQTL atlas of regulatory variation in the aging human brain, integrating multiple modalities and cell types across four studies, identifying more variant-molecular-trait associations by including TAD boundaries.

The paper identifies EGFR as a potential therapeutic target in Alzheimer's disease by linking a risk variant to increased EGFR expression in brain cell types through genomic analysis and CRISPRi validation.

The paper develops scEEMS models to predict cell-type-specific eQTLs, enhancing discovery of AD risk variants by linking them to cell-type-specific genes.

This paper links molecular mechanisms to Alzheimer's disease and aging by integrating neuroimaging endophenotypes with molecular quantitative trait loci.

LeafCutter2 identifies and quantifies unproductive splicing events from RNA-seq data, revealing their role in gene regulation and disease, including Alzheimer's.

The paper develops a pipeline to profile both host expression and microbial abundance from spatial transcriptomic data, demonstrating host-microbe interactions and method efficiency.

The paper develops a pipeline to analyze spatial transcriptomic data for microbial sequences, validates it on human and murine intestinal datasets, and presents initial biological insights.

The paper analyzes meta-transcriptomic data to identify gene expression and novel conserved sub-genomic RNAs in SARS-CoV-2 and MERS-CoV, revealing new peptides with potential functional roles in viral virulence.