Kengo Kinoshita

ibSLS provides a platform for accessing multi-omics data and biospecimens from spaceflight research to support biomedical and disease studies.

The paper examines how monoaminergic genetic variation, cortical structure, and trauma interact to influence emotional vulnerability using a large cohort dataset.

This paper updates the Japanese population-specific reference genome JG1 to JG2 using Hi-C and meta-assembly methods, improving coverage and accuracy for genetic research.

The paper develops resources for NGS analysis using the Japanese reference genome JG, including variant calling, annotation, and coordinate conversion tools.

The paper presents cryo-EM structures of human ZnT7, revealing its mechanism for Zn2+ uptake into the Golgi apparatus through conformational changes and histidine-rich loop interactions.

This study predicts and characterizes disorder-order transition regions in proteins using deep learning methods, finding that most transitions prefer ordered states over disordered ones.

The paper introduces Japonica Array NEO, a SNP array for the Japanese population with enhanced coverage and disease risk SNPs, showing high imputation quality and utility in genotyping cohorts.

The paper identifies a mtDNA polymorphism in MOTS-c associated with increased T2D risk in males, interacting with physical activity.

The paper identifies seven new potentially pathogenic BRCA1/BRCA2 variants in a Japanese population using computational scores and cohort analysis.

This paper constructs and integrates three de novo Japanese human genome assemblies to create a population-specific reference, improving variant calling accuracy for Japanese individuals.

This paper introduces a recurrent neural network method for genotype imputation using phased genotype data, showing comparable accuracy to existing methods for common variants and better performance when haplotype data is limited.

The paper constructs a population-level allele frequency panel of 3,552 Japanese individuals using a standardized pipeline and makes it publicly available.

The paper introduces a de novo amino acid sequence profile generator using LSTM, showing improved performance over existing methods for predicting beta-strands.