Practical guide for managing large-scale human genome data in research

Tomoya Tanjo,Yosuke Kawai,Masao Nagasaki,Osamu Ogasawara,Katsushi Tokunaga

doi:10.1038/s10038-020-00862-1

Abstract

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.

Highlights

In human genetics, advancements in next-generation sequencing technology have enabled population-scale sequencing from just one sequencer and allowed sharing millions of human genome sequencing data from publicly archived data including privacy-protected ones
This review aims to guide researchers in human genetics to process and analyze these large-scale genomic data to extract relevant information for improved downstream analyses in their specific research domains
In “How to store and analyze human genome data efficiently?” section, we focus on the development of data format and software that manipulate whole-genome sequencing (WGS) including hardware-based acceleration

Summary

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Advancements in next-generation sequencing technology have enabled population-scale sequencing from just one sequencer and allowed sharing millions of human genome sequencing data from publicly archived data including privacy-protected ones. The genomic data are widely distributed under the open access policy though various computational platforms, e.g., high-performance computing (HPC) system of the National Institute of Genetics (NIG) in Japan and public cloud services. These efforts ease the reusability by researchers. The combination of a workflow description language and workflow engines allows the portability to different computational environments and the scalability of data analysis that adapts to the increase of the size of computational resources. On July 16, 2020, the Court of Justice of the European Union issued a judgment declaring as “invalid” on the adequacy of the protection provided by the EU-U.S Privacy Shield (https://www.privacyshield.gov/Program-Overview)

Conclusion and future direction

Compliance with ethical standards

50. Multicloud