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

Testing the performance of the imputation of MHC region in large datasets when using different reference panels

The major histocompatibility complex (MHC) contains a group of genes (~260 genes in ~4Mb) involved in several inflammatory disorders and immune response including the HLA-C gene. So far, the IPD-IMGT/HLA database reports more than 4000 different HLA-C alleles. Given the highly polymorphic nature of the gene, GWAS generally don’t study or study only a small subset of polymorphic sites of the region. Imputation procedures may help in gaining additional information on this region. However, the successful imputation of the MHC region would require a reference panel with detailed information. The main goal of this study is to investigate whether imputation procedures using appropriate reference panels may effectively increase the number of polymorphic sites of the MHC region for association with complex traits. We studied the MHC region imputation performances using 3 different reference panels (Michigan and TOPMed imputation servers): TOPMed-r2, 1000 Genomes (Phase3, v5), and the novel four-digit multi-ethnic HLA panel (v1, 2021). Here, 5 datasets with more than 1000 individuals each underwent imputation. We then focused on the imputation results of the MHC region that surround the HLA-C gene (hg19: 31234948-31241032). Imputation reported a different number of markers for the different reference panels: 482 in 1000G, 365 in TOPMed, and 1272 in HLA-panel. Of note, the HLA panels gave a higher number of imputed markers than the others. We then selected the 104 common markers imputed by all the 3 reference panels. Moreover, 162 markers were found only by 1000G panel, 194 by TOPMed, and 998 by the HLA-panel. The first preliminary comparisons showed a high concordance value for the genotype calling by the 3 different reference sets. The efficiency of the imputation was measured by the R-squared (R2) values stratifying the markers into 3 groups according to the minor allele frequency (MAF). The 104 common markers showed high R2 values (>0.96). As expected, in the other marker groups, the R2 mean values were lower for markers with MAF<0.1 (>0.65 in 1000G, 0.15-0.20 in TOPMed, >0.40 in HLA panel). In conclusion, imputation-based procedures with dedicated HLA panels can produce much more high-quality information than other general purpose reference panels for the MHC region

Towards an optimal design of target for tsetse control: comparisons of novel targets for the control of palpalis group tsetse in West Africa

Background: Tsetse flies of the Palpalis group are the main vectors of sleeping sickness in Africa. Insecticide impregnated targets are one of the most effective tools for control. However, the cost of these devices still represents a constraint to their wider use. The objective was therefore to improve the cost effectiveness of currently used devices. Methodology/Principal Findings: Experiments were performed on three tsetse species, namely Glossina palpalis gambiensis and G. tachinoides in Burkina Faso and G. p. palpalis in Côte d'Ivoire. The 1×1 m2 black blue black target commonly used in W. Africa was used as the standard, and effects of changes in target size, shape, and the use of netting instead of black cloth were measured. Regarding overall target shape, we observed that horizontal targets (i.e. wider than they were high) killed 1.6-5x more G. p. gambiensis and G. tachinoides than vertical ones (i.e. higher than they were wide) (P<0.001). For the three tsetse species including G. p. palpalis, catches were highly correlated with the size of the target. However, beyond the size of 0.75 m, there was no increase in catches. Replacing the black cloth of the target by netting was the most cost efficient for all three species. Conclusion/Significance: Reducing the size of the current 1*1 m black-blue-black target to horizontal designs of around 50 cm and replacing black cloth by netting will improve cost effectiveness six-fold for both G. p. gambiensis and G. tachinoides. Studying the visual responses of tsetse to different designs of target has allowed us to design more cost-effective devices for the effective control of sleeping sickness and animal trypanosomiasis in Africa