Preliminary Study on a System for Visualization of Big Data in SMEs

The 2012 White Paper on Information and Communications in Japan issued by the Ministry of Internal Affairs and Communications of Japan advocates use of big data under its “Special Theme: ICT-induced and accelerated Disaster Recovery and Japan’s Re-birth.” However, the analysis in the Japan Users Association of Information Systems’ white paper on its 2014 IT trend survey for companies reports that less than 10% of companies utilize big data, and it would appear that progress in its use is centered on large firms. Under such conditions, use of big data is becoming a challenge for the purpose of ensuring the survival and success of SMEs as well. As a result, R&D and technological support for SMEs are becoming pressing issues. However, at present there has been almost no academic research concerning policies and future directions for use of big data at SMEs. Accordingly, this study conducted the modelization of the procedure for visualization of big data for SMEs. Specifically, we organized the procedure as a tutorial, from obtaining data of Japanese hot-spring areas using web scraping, to visualizing them using the visualization software Cytoscape

RAISING is a high-performance method for identifying random transgene integration sites

Both natural viral infections and therapeutic interventions using viral vectors pose significant risks of malignant transformation. Monitoring for clonal expansion of infected cells is important for detecting cancer. Here we developed a novel method of tracking clonality via the detection of transgene integration sites. RAISING (Rapid Amplification of Integration Sites without Interference by Genomic DNA contamination) is a sensitive, inexpensive alternative to established methods. Its compatibility with Sanger sequencing combined with our CLOVA (Clonality Value) software is critical for those without access to expensive high throughput sequencing. We analyzed samples from 688 individuals infected with the retrovirus HTLV-1, which causes adult T-cell leukemia/lymphoma (ATL) to model our method. We defined a clonality value identifying ATL patients with 100% sensitivity and 94.8% specificity, and our longitudinal analysis also demonstrates the usefulness of ATL risk assessment. Future studies will confirm the broad applicability of our technology, especially in the emerging gene therapy sector.Communications Biology, 5, art. no. 535; 202