Yuichi Shiraishi

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 study identifies 16 AML subgroups based on chromatin accessibility profiles using ATAC-seq in 1,563 cases, revealing epigenetic heterogeneity and its clinical and therapeutic implications.

This paper presents near-complete, haplotype-resolved assemblies of a pancreatic cancer genome and normal tissue, identifying extensive somatic variants and epigenetic changes in previously excluded regions.

CrosSplice identifies rare splice-site creating variants using cross-tissue transcriptome data, discovering clinically relevant variants missed by conventional methods.

The paper introduces mm2-ivh, an algorithm that improves overlap detection in alpha satellite HORs by distinguishing identical k-mers from different repeats, leading to more accurate haplotype reconstruction.

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 a pipeline for multi-sample long-read transcriptome assembly and applies it to breast cancer specimens to identify subtype-specific isoforms and fusion transcripts.

The paper identifies a novel class of structural aberrations in lung cancers using long-read sequencing and shows their occurrence in vivo and in cancer-related genes.

The paper identifies hundreds of genes deregulated by SVs in cancer, showing increased expression linked to SV breakpoints and enhancer elements.

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 analyzes RNA alterations across 27 cancer types using whole-genome sequencing data, identifying gene expression associations with DNA variants and structural fusions.

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

This paper introduces a simplified model-based approach for inferring and visualizing cancer mutation signatures, showing improved robustness and interpretability.