Kenichi Chiba

PRCGAP provides a haplotype-resolved analysis of cancer genomes, detecting variants missed by standard references and revealing new insights into structural variants and fusion events in leukemia.

This paper uses rare k-mers to identify centromere haplogroups in human populations and cancer translocations, revealing structural diversity and translocation mechanisms.

The paper develops a framework to identify splice-site creating variants (SSCVs) using transcriptome data, identifies 30,130 SSCVs, and demonstrates their therapeutic potential with ASOs.

nanomonsv detects somatic structural variations from long-read sequencing data with high precision and recall, revealing complex SV features in cancer.

This study develops a computational workflow for target adaptive sampling long-read sequencing and applies it to hereditary cancer genomes to identify genetic variants and epigenetic changes.

The paper identifies intron retention associated variants using transcriptome data alone, finding 27,937 variants and 3,077 putative disease-associated ones.

The paper develops SF3B1ness score to predict SF3B1 mutation status from transcriptomes using machine learning, achieving high precision and sensitivity in identifying functional mutations.

The paper identifies and characterizes splicing-associated variants in cancer using a statistical framework applied to 8,976 samples across 31 cancer types, revealing 14,438 SAVs and their association with smoking and tumor suppressors.

The paper introduces paplot, a software tool for generating interactive reports in cancer genome analysis, enabling richer data exploration than static visualizations.