Yuk Kei Wan

This paper introduces HOMES-seq, a spatial transcriptomics workflow for detecting microbes and host transcripts in colorectal tumours, improving sensitivity and reducing costs.

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

This paper develops a lightweight k-mer model for RNA004 chemistry using basecaller move tables, achieving high alignment rates and comparable m6A detection performance to default models.

The paper benchmarks eight tools for identifying and quantifying alternative polyadenylation sites from RNA-seq data using real, synthetic, and 3'-end sequencing data.

Bambu improves transcript quantification from long-read RNA-Seq by using context-aware annotations, enhancing precision without sacrificing sensitivity.

This paper develops MTtrans, a multi-task learning model that identifies robust, generalizable cis-regulatory motifs in 5' UTRs by integrating diverse datasets, showing improved accuracy and cross-species transferability.

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.