Takahiro Arakawa

The paper introduces UNIChro-seq, a method for quantifying chromatin accessibility at target loci, and demonstrates its application in analyzing disease risk alleles and genome-edited variants.

CATaN integrates TF-GRNs and transcriptomes to identify genetic risk components across complex diseases, finding 588 significant CC components enriched for SNP heritability.

The paper identifies novel cell states and molecular profiles in lupus patients using single-cell data, linking them to disease severity and treatment outcomes.

xcore is an R package that models transcription factor activity using ChIP-seq signatures and gene expression data, demonstrating improved biological relevance in case studies.

ReFeaFi predicts regulatory elements genome-wide using a two-model approach, achieving good performance across species and identifying disease-associated variants in human regulatory regions.

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.

C1 CAGE detects transcription start sites and enhancer activity in lung cancer cells, revealing subpopulations with distinct responses to TGF-β and enhancer RNA dynamics.