BACKGROUND: Complex biological systems are described as a multitude of cell-cell interactions (CCIs). Recent single-cell RNA-sequencing studies focus on CCIs based on ligand-receptor (L-R) gene co-expression but the analytical methods are not appropriate to detect many-to-many CCIs. RESULTS: In this work, we propose scTensor, a novel method for extracting representative triadic relationships (or hypergraphs), which include ligand-expression, receptor-expression, and related L-R pairs. CONCLUSIONS: Through extensive studies with simulated and empirical datasets, we have shown that scTensor can detect some hypergraphs that cannot be detected using conventional CCI detection methods, especially when they include many-to-many relationships. scTensor is implemented as a freely available R/Bioconductor package.

BACKGROUND: In genome-wide studies, over-representation analysis (ORA) against a set of genes is an essential step for biological interpretation. Many gene annotation resources and software platforms for ORA have been proposed. Recently, Medical Subject Headings (MeSH) terms, which are annotations of PubMed documents, have been used for ORA. MeSH enables the extraction of broader meaning from the gene lists and is expected to become an exhaustive annotation resource for ORA. However, the existing MeSH ORA software platforms are still not sufficient for several reasons. RESULTS: In this work, we developed an original MeSH ORA framework composed of six types of R packages, including MeSH.db, MeSH.AOR.db, MeSH.PCR.db, the org.MeSH.XXX.db-type packages, MeSHDbi, and meshr. CONCLUSIONS: Using our framework, users can easily conduct MeSH ORA. By utilizing the enriched MeSH terms, related PubMed documents can be retrieved and saved on local machines within this framework.

We have developed the RIKEN AJAX browser computing (RABC) system. This system enables the simple realization of large scale Global Computing systems, like the SETI@home project, that take advantage of unused computing resources. Its greatest advantage is that an agent program, which has been traditionally required, does not need to be installed. Volunteers can participate by simply browsing to a Web page provided by the RABC system. This system allows greater CPU cycle resources to be acquired than traditional methods. We have evaluated a performance difference between JavaScript and legacy computer languages.

We have developed an instant environment for protein sequence profile scanning application by re-mastering Knoppix distribution as high throughput computing edition. This is an integrated environment with bioinformatics applications, parallel file system and scheduler. The integrated environment allows biology researchers to perform scanning applications immediately on their computational pool without any system configuration changed.