大阪大学 大学院医学系研究科

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DEEP*HLA (multi-task convolutional DEEP learning for HLA imputation)

Naito T et al. A multi-task convolutional deep learning method for HLA allelic imputation and its application to trans-ethnic MHC fine-mapping of type 1 diabetes. medRxiv doi:https://doi.org/10.1101/2020.08.10.20170522. [medRxiv]
github page [Link]

GREP (Genome for REPositioning drugs)

Sakaue S and Okada Y. GREP: Genome for REPositioning drugs. Bioinformatics 35:3821-3823. [PubMed]
github page [Link]

MIGWAS (miRNA-target gene networks enrichment on GWAS)

Sakaue S et al. (2018) Integration of genetics and miRNA-target gene network identified disease biology implicated in tissue specificity. Nucleic Acids Res 46:11898-11909. [PubMed]
github page [Link]

eLD (entropy-based Linkage Disequilibrium index between multiallelic sites)

Okada Y. (2018) eLD: entropy-based Linkage Disequilibrium index between multi-allelic sites. Hum Genome Var 5:29. [PubMed]
R script [Link]

Grimon (Graphical interface to visualize multi-layer omics networks)

Kanai M, Maeda Y, Okada Y. (2018) Grimon: Graphical interface to visualize multi-omics networks. Bioinformatics 34:3934-3936. [PubMed]
github page [Link]

Softwares and data source used in Okada et al. Nature (2014)

Okada Y, Wu D, Trynka G et al. (2014) Genetics of rheumatoid arthritis contributes to biology and drug discovery. Nature 506:376-381. [PubMed]
Summary statistics of RA GWAS meta-analysis
  • Trans-ethnic RA GWAS meta-analysis (19,234 RA cases and 61,565 controls) [Link]
  • Eurpean RA GWAS meta-analysis (14,361 RA cases and 43,923 controls) [Link]
  • Eurpean RA GWAS meta-analysis (8,875 RA cases and 29,367 controls, non-immunochip) [Link]
  • Asian RA GWAS meta-analysis (4,873 RA cases and 17,642 controls) [Link]
  • Summary results of RA risk SNPs in 101 risk loci [Link]
Softwares for 1KG imputation and GWAS meta-analysis
  • Perl source codes for 1KG imputation reference panel preparation [Link]
  • Perl source codes for splitting GWAS data / 1KG reference panel into chuncks [Link]
  • Perl source codes for handling association analysis results [Link]
  • Java package for genomic control (GC) correction [Link]
  • Java package for GWAS meta-analysis [Link]
  • R source codes for plotting Manhattan / QQ plots of GWAS P-values [Link]
Pleiotropy analysis using GWAS catalogue database
  • Curated phenotype/SNP data from the GWAS catalogue (for data downloaded on January 31, 2013) [Link]
H3K4me3 histone mark enrichment analysis for GWAS signals
  • Software for enrichment analysis and histone mark data (from Raychaudhuri lab and the Broad Instiute) [Link]
Softwares and data source for in-silico pipeline to prioritize biological genes from GWAS risk loci
  • Java package for calculating LD between SNPs and 1KG reference data [Link]
  • R source codes for assigning LD regions to SNPs [Link]
  • Perl source codes for assiging UCSC genes into LD regions [Link]

  • Functional annotation of SNPs (Annovar software) [Link]
  • eQTL data for PBMC (from http://genenetwork.nl) [Link]
  • Cell-specific eQTL data (from ImmVar project) [Link]
  • PubMed text-mining (GRAIL software) [Link]
  • Protein-protein interaction analysis (DAPPLE software) [Link]
  • Primary immunodificiency (PID) gene list (from Journal web site) [Link]
  • Cancer somatic mutation gene list (from Journal web site) [Link]
  • Knockout mouse phenotype and gene list (from Journal web site) [Link]
  • Molecular pathway analysis (MAGENTA software) [Link]
Softwares and data source for GWAS-drug target overlap analysis
  • Drug target gene list (from Journal web site) [Link]
  • Perl source codes for overlap enrichment analysis [Link]