Iain Beehuat Tan

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 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 paper identifies a SERPINE1-based gene signature (RESIST-M) that predicts oxaliplatin resistance and metastasis in colorectal cancer and proposes therapeutic strategies targeting this pathway.

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

This paper evaluates differences in a breast cancer polygenic risk score across genomic platforms and finds that calibration improves consistency in high-risk classification.

ctQC improves biological inferences by applying cell-type-specific quality control to single cell and spatial transcriptomics data, enhancing clustering and reducing artifacts.

This paper identifies FAXDC2 as a cholesterol biosynthesis enzyme that links Wnt/beta-catenin to RTK/MAPK signaling, showing its role in cancer progression and cellular differentiation.

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 evaluates polygenic risk scores for predicting common cancers in East Asians using a large cohort, finding significant risk stratification for breast, prostate, and colorectal cancers.

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.

ConsensusDriver improves driver alteration prediction by combining multiple methods, increasing sensitivity by 20% across cancer types and individual patients.