Kazuki Nakamae

tWhEEL quantifies CRISPR knockout feasibility by integrating transcriptome data with guide RNA design and functional annotations to prioritize tractable biological functions and organisms.

PtWAVE improves detection of large indels in genome editing by extending deconvolution range and enhancing accuracy and sensitivity.

The paper develops PiCTURE and machine learning models to predict RNA off-target effects of CRISPR base editors in tissue-specific transcriptomes, finding higher risks in colon and lungs compared to brain and ovaries.

DANGER analysis assesses on/off-target effects of CRISPR editing using RNA-seq data without a reference genome, identifying deleterious off-target sites and quantifying their phenotypic impact.

MaChIAto profiles genomic and epigenomic features affecting CRISPR edit efficiency and accuracy, revealing context-dependent factors influencing knock-out, knock-in, and Prime Editing outcomes.