Studies in human genetics deal with a plethora of human genome sequencing data that are generated from specimens as well as available on public domains. With the development of various bioinformatics applications, maintaining the productivity of research, managing human genome data, and analyzing downstream data is essential. This review aims to guide struggling researchers to process and analyze these large-scale genomic data to extract relevant information for improved downstream analyses. Here, we discuss worldwide human genome projects that could be integrated into any data for improved analysis. Obtaining human whole-genome sequencing data from both data stores and processes is costly; therefore, we focus on the development of data format and software that manipulate whole-genome sequencing. Once the sequencing is complete and its format and data processing tools are selected, a computational platform is required. For the platform, we describe a multi-cloud strategy that balances between cost, performance, and customizability. A good quality published research relies on data reproducibility to ensure quality results, reusability for applications to other datasets, as well as scalability for the future increase of datasets. To solve these, we describe several key technologies developed in computer science, including workflow engine. We also discuss the ethical guidelines inevitable for human genomic data analysis that differ from model organisms. Finally, the future ideal perspective of data processing and analysis is summarized.

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After the 2011 Great East Japan Earthquake in Japan, which subsequently shut down the nuclear power plant in Fukushima, many researchers who foresee such future threats have been proposing decentralized electricity management systems based on renewable green energy. In this paper, we study a graph partitioning problem of power grids assuming that each microgrid can transfer its electricity surplus to other clusters in a peer-to-peer way. We extend an existing graph partitioning algorithm such that it can check whether a set of clusters are self-sufficient after electricity sharing. Our experiments show that our method can reduce the cost of constructing a microgrid system and that it can produce a resilient graph partition that accommodates time-varying electricity surplus over the year.

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