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

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.

Next-Generation Sequencing Applications for Inherited Retinal Diseases

“…More research groups are now making the move to cloud-based storage for their NGS data and minimising the amount of data stored has a direct impact on cost [ 94 ]. It is important to note that sample processing and analyses are available via cloud-based solutions also, and may be an attractive option for research groups lacking the necessary in-house infrastructure to process NGS data [ 95 ].…”

Efficient ancestry and mutation simulation with msprime 1.0

“…The second challenge facing simulation methods is that sample sizes in genetic studies have grown very quickly in recent years, enabled by the precipitous fall in genome sequencing costs. Human datasets like the UK Biobank ( Bycroft et al 2018 ) and gnomAD ( Karczewski et al 2020 ) now consist of hundreds of thousands of genomes and many other datasets on a similar scale are becoming available ( Tanjo et al 2021 ). Classical simulators such as and even fast approximate methods such as ( Staab et al 2015 ) simply cannot cope with such a large number of samples.…”

Applications of single-cell and bulk RNA sequencing in onco-immunology

“…Therefore, standardised pipelines need to be developed to allow reproducibility between centers. Awareness is increasing regarding this aspect, and efforts are being made in this direction [106].…”