Christopher J. F. Cameron

This paper develops models linking histological images to genotype, transcriptome, and age, identifying 906 imageQTLs and predicting gene expression and chronological age from images.

REPIC uses ensemble learning to identify high-quality cryo-EM particles by consensus, reducing manual effort and improving consistency across different pickers.

The paper introduces the EN-TEx resource, which provides personal epigenomes and variant-impact models across multiple tissues, revealing allele-specific loci and their associations with GWAS loci.

The paper identifies widespread transcription initiation at microsatellites across species and develops deep learning models to predict STR-associated transcription and its impact on disease variants.

SACSANN predicts chromosomal compartments from genomic sequence features, showing transferability across human and mouse cell types.

The paper functionally annotates human long non-coding RNAs by systematically knocking down 285 lncRNAs and analyzing molecular phenotypes using CAGE sequencing.

RADICL-seq maps genome-wide RNA-chromatin interactions in intact nuclei, revealing general and cell type-specific patterns of transcript occupancy and the role of transcription in chromatin structure.

This paper introduces HIFI, a method to estimate DNA-DNA interaction frequencies at restriction-fragment resolution from Hi-C data, improving resolution and biological insights.

LESSeq identifies differential alternative splicing using RNA-Seq data by analyzing local splicing events and quantifying their significance across cellular conditions.