Endometriosis is a gynecological disorder prevalent in women of reproductive age. The primary symptoms include dysmenorrhea, irregular menstruation, and infertility. However, the pathogenesis of endometriosis remains unclear. With the advent of high-throughput technologies, various omics experiments have been conducted to identify genes related to the pathophysiology of endometriosis. This review highlights the molecular mechanisms underlying endometriosis using omics. When genes identified in omics experiments were compared with endometriosis disease genes identified in independent studies, the number of overlapping genes was moderate. However, the characteristics of these genes were found to be equivalent when functional gene set enrichment analysis was performed using gene ontology and biological pathway information. These findings indicate that omics technology provides invaluable information regarding the pathophysiology of endometriosis. Moreover, the functional characteristics revealed using enrichment analysis provide important clues for discovering endometriosis disease genes in future research.

Permutation testing is a robust and popular approach for significance testing in genomic research that has the advantage of reducing inflated type 1 error rates; however, its compu-tational cost is notorious in genome-wide association studies (GWAS). Here, we developed a supercomputing-aided approach to accelerate the permutation testing for GWAS, based on the message-passing interface (MPI) on parallel computing architecture. Our application, called MPI-GWAS, conducts MPI-based permutation testing using a parallel computing approach with our supercomputing system, Nurion (8,305 compute nodes, and 563,740 central processing units [CPUs]). For 107 permutations of one locus in MPI-GWAS, it was calculated in 600 s using 2,720 CPU cores. For 107 permutations of ~30,000–50,000 loci in over 7,000 subjects, the total elapsed time was ~4 days in the Nurion supercomputer. Thus, MPI-GWAS enables us to feasibly compute the permutation-based GWAS within a reason-able time by harnessing the power of parallel computing resources.

Tumor suppressor genes (TSGs) are essential genes in the development of cancer. While they have many roles in normal cells, mutation and dysregulation of the TSGs result in aberrant molecular processes in cancer cells. Therefore, understanding TSGs and their roles in the oncogenic process is crucial for prevention and treatment of cancer. In this research, multi-omics breast cancer data were used to identify molecular mechanisms of TSGs in breast cancer. Differentially expressed genes and differentially coexpressed genes were identified in four large-scale transcriptomics data from public repositories and multi-omics data analyses of copy number, methylation and gene expression were performed. The results of the analyses were integrated using enrichment analysis and meta-analysis of a p-value summation method. The integrative analysis revealed that TSGs have a significant relationship with genes of gene ontology terms that are related to cell cycle, genome stability, RNA processing and metastasis, indicating the regulatory mechanisms of TSGs on cancer cells. The analysis frame and research results will provide valuable information for the further identification of TSGs in different types of cancers.

Biological phenomena comprise various interactions between genes and molecules [...]