Pocket-sized genomics and transcriptomics analyses: a look at the newborn BioVRPi project

BioVRPi is a newborn project, started in January 2021, that focuses on Raspberry Pi (RPi) employment in bioinformatics, with particular regards on genomics. In the previous years, some research groups have already reported several examples of applications for RPi, including bioinformatic basic training and proteomics. Our project aims to develop and offer a low-cost, stable, and tested bioinformatic environment for students and researchers involved in genomics and transcriptomics fields. Raspberry Pi is a small single-board low-cost computer that was developed by the Raspberry Pi Foundation since 2012. Its original purpose aimed to facilitate computer science basic teaching in developing countries, but the growing worldwide interest has permitted its constant progress and development. Thanks to its features, RPi can suit several disciplines in need for computational supports and reach almost every, if not all, research group in the world. We tested RPi capabilities on real case studies, relatively to Genome-Wide Association Studies (GWAS) for complex traits in Homo sapiens data and in transcriptomic analyses (RNA-seq) on the Strongyloides stercoralis human parasite samples, using two RPi-4 devices equipped with different amount of RAM (8GB for genomics and 2 GB for transcriptome analyses, respectively), and running a 64-bit Operating System. The analyses leveraged on state-of-art bioinformatic toolset, such as Plink and Plink1.9, SAMtools, Bowtie 2, R, and different R packages, all compiled from source code. Moreover, the GWAS was run according to the golden standard protocols and results from the different platforms were compared. The results showed that RPi are effective devices that can efficiently handle whole GWAS and RNA-seq analyses. Benchmarking showed that the computational time taken by RPi was of the same order of magnitude when compared to the ones from a commonly used bioinformatic computer. At last, BioVRPi project shows how to implement new strategies for bioinformatic analyses, in order to provide a having-fun environment to learn and explore new alternatives in bioinformatic data analysis.