The African BioGenome Project (AfricaBP) is a Pan-African initiative aimed at improving food systems and biodiversity conservation through genomics while ensuring equitable data sharing and benefits. The Open Institute is the knowledge exchange platform of the AfricaBP which aims to bridge local knowledge gaps in biodiversity genomics and bioinformatics and enable infrastructural developments. In 2024, the AfricaBP Open Institute advanced this mission by organising 31 workshops that attracted more than 3500 registered attendees and trained 380 African researchers in genomics, bioinformatics, molecular biology, sample collections and biobanking, and ethics, across all five African geographical regions involving 40 African and non-African organizations. These workshops provide current understanding on the applications of biodiversity genomics and bioinformatics to the African bioeconomy as well as providing practical and hands-on training in genomics, bioinformatics, molecular biology, gene editing, and sample collection and processing. Here, we provide the current understanding of the applications of biodiversity genomics and bioinformatics to the African bioeconomy through synthetic reviews and presentations, including descriptions of 31 workshops organised as well as three fellowship programs delivered or launched by the AfricaBP Open Institute in collaboration with African and international institutions and industry partners. We review the current national bioeconomy strategies across Africa and the economic impact of sequencing African genomes locally, illustrated by a case study on the proposed 1000 Moroccan Genome Project. Finally, we provide recommendations on how African countries could integrate biodiversity genomics and bioinformatics into national economic plans and bioeconomy strategies.

The African BioGenome Project (AfricaBP) is a Pan-African effort aimed at sequencing the genomes of 105,000 African endemic and indigenous species to support food systems, conservation, and ensure data-sharing and equitable benefits. This effort aligns with the Kunming-Montreal Global Biodiversity Framework (KMGBF), which aims to prevent or mitigate biodiversity loss while facilitating equitable access and benefit-sharing from genetic resources and Digital Sequence Information (DSI) and securing adequate technical and scientific cooperations. The AfricaBP Open Institute for Genomics and Bioinformatics (AfricaBP Open Institute) is the knowledge exchange programme of the AfricaBP which aims to overcome infrastructural barriers through the development of technology and infrastructure. A key component of AfricaBP Open Institute's vision is the establishment of the African Digital Sequence Information Data Bank for Biodiversity and Agriculture (African DSI Data Bank), a federated platform for storing, analyzing, visualizing and sharing genetic data across the African continent. The African DSI Data Bank will address the current fragmentation of DSI across African institutions by linking existing databases and resources while ensuring compliance with regional and global standards. It will use a federated model, leveraging existing (and new) infrastructures across Africa, that allow institutions and countries to retain data sovereignty while adhering to national, regional, and international access and benefit-sharing regulations. Through a proposed Global Access Point (GAP), researchers will be able to gain equitable access to sequence data and genomic metadata via a decentralized network. Furthermore, to understand the current landscape of biodiversity and agricultural DSI databases, analyses, visualization, and data sharing platforms, AfricaBP Open Institute conducted a survey across Africa, and recorded 161 responses. Although the majority of these participants shared common challenges such as limited infrastructure, funding, and capacity building, the overwhelming indication was that they support an African-based DSI platform through an inclusive governance model. Consequently, we describe the proposed roadmap for the creation of an African DSI Data Bank that includes African DSI federated database, visualization, analysis, and sharing platforms, as well as the ethical, legal, social, KMGBF, and sustainability considerations associated with such an infrastructure.

The African BioGenome Project (AfricaBP) Open Institute for Genomics and Bioinformatics aims to overcome barriers to capacity building through its distributed African regional workshops and prioritizes the exchange of grassroots knowledge and innovation in biodiversity genomics and bioinformatics. In 2023, we implemented 28 workshops on biodiversity genomics and bioinformatics, covering 11 African countries across the 5 African geographical regions. These regional workshops trained 408 African scientists in hands-on molecular biology, genomics and bioinformatics techniques as well as the ethical, legal and social issues associated with acquiring genetic resources. Here, we discuss the implementation of transformative strategies, such as expanding the regional workshop model of AfricaBP to involve multiple countries, institutions and partners, including the proposed creation of an African digital database with sequence information relating to both biodiversity and agriculture. This will ultimately help create a critical mass of skilled genomics and bioinformatics scientists across Africa. The African BioGenome Project (AfricaBP) Open Institute for Genomics and Bioinformatics established a series of regional workshops in 2023 to exchange knowledge and overcome barriers, which could serve as a model for other scientific communities.

In 2022, around 54 % of African students were denied student visas to study in the United States (US), compared to 36 % of Asian students and 9 % of European students, despite African immigrants in the US often being more highly educated than the US native-born population. This issue cannot be attributed solely to the dichotomy between the Global North and South in visa regimes, but it is also evident among African nations across regional economic blocs. The African BioGenome Project (AfricaBP) Open Institute for Genomics and Bioinformatics, which aims to overcome barriers to capacity building through its distributed African regional workshops, prioritizes grassroots knowledge exchange and innovation in biodiversity genomics and bioinformatics. In 2023, we orchestrated the implementation of 27 capacity building workshops on biodiversity genomics and bioinformatics, covering 10 African countries across 5 African geographical regions. The AfricaBP Open Institute regional workshops raised awareness of biodiversity genomics and bioinformatics among 3788 registered participants, and trained 408 African scientists in hands-on molecular biology, genomics, and bioinformatics techniques. Here, we discuss the implementation of transformative strategies by deploying the AfricaBP Open Institute multi-country, multi-institution, and multi-partner hybrid regional workshop model, including the proposed creation of an African digital database containing sequence information relating to biodiversity and agriculture.

IntroductionIncreasing global warming has made heat stress a serious threat to crop productivity and global food security in recent years. One of the most promising solutions to address this issue is developing heat-stress-tolerant plants. Hence, a thorough understanding of heat stress response mechanisms, particularly molecular ones, is crucial.MethodsAlthough numerous studies have used microarray expression profiling technology to explore this area, these experiments often face limitations, leading to inconsistent results. To overcome these limitations, a random effects meta-analysis was employed using advanced statistical methods. A meta-analysis of 16 microarray datasets related to heat stress response in Arabidopsis thaliana was conducted.ResultsThe analysis revealed 1,972 significant differentially expressed genes between control and heat-stressed plants (826 over-expressed and 1,146 down-expressed), including 128 differentially expressed transcription factors from different families. The most significantly enriched biological processes, molecular functions, and KEGG pathways for over-expressed genes included heat response, mRNA splicing via spliceosome pathways, unfolded protein binding, and heat shock protein binding. Conversely, for down-expressed genes, the most significantly enriched categories included cell wall organization or biogenesis, protein phosphorylation, transmembrane transporter activity, ion transmembrane transporter, biosynthesis of secondary metabolites, and metabolic pathways.DiscussionThrough our comprehensive meta-analysis of heat stress transcriptomics, we have identified pivotal genes integral to the heat stress response, offering profound insights into the molecular mechanisms by which plants counteract such stressors. Our findings elucidate that heat stress influences gene expression both at the transcriptional phase and post-transcriptionally, thereby substantially augmenting our comprehension of plant adaptive strategies to heat stress.

The advent of modern genotyping technologies has revolutionized genomic selection in animal breeding. Large marker datasets have shown several drawbacks for traditional genomic prediction methods in terms of flexibility, accuracy, and computational power. Recently, the application of machine learning models in animal breeding has gained a lot of interest due to their tremendous flexibility and their ability to capture patterns in large noisy datasets. Here, we present a general overview of a handful of machine learning algorithms and their application in genomic prediction to provide a meta-picture of their performance in genomic estimated breeding values estimation, genotype imputation, and feature selection. Finally, we discuss a potential adoption of machine learning models in genomic prediction in developing countries. The results of the reviewed studies showed that machine learning models have indeed performed well in fitting large noisy data sets and modeling minor nonadditive effects in some of the studies. However, sometimes conventional methods outperformed machine learning models, which confirms that there's no universal method for genomic prediction. In summary, machine learning models have great potential for extracting patterns from single nucleotide polymorphism datasets. Nonetheless, the level of their adoption in animal breeding is still low due to data limitations, complex genetic interactions, a lack of standardization and reproducibility, and the lack of interpretability of machine learning models when trained with biological data. Consequently, there is no remarkable outperformance of machine learning methods compared to traditional methods in genomic prediction. Therefore, more research should be conducted to discover new insights that could enhance livestock breeding programs.

Background: The use of breed-informative genetic markers, specifically coding Single Nucleotide Polymorphisms (SNPs), is crucial for breed traceability, authentication of meat and dairy products, and the preservation and improvement of pig breeds. By identifying breed informative markers, we aimed to gain insights into the genetic mechanisms that influence production traits, enabling informed decisions in animal management and promoting sustainable pig production to meet the growing demand for animal products.Methods: Our dataset consists of 300 coding SNPs genotyped from three Italian commercial pig populations: Landrace, Yorkshire, and Duroc. Firstly, we analyzed the genetic diversity among the populations. Then, we applied a discriminant analysis of principal components to identify the most informative SNPs for discriminating between these populations. Lastly, we conducted a functional enrichment analysis to identify the most enriched pathways related to the genetic variation observed in the pig populations.Results: The alpha diversity indexes revealed a high genetic diversity within the three breeds. The higher proportion of observed heterozygosity than expected revealed an excess of heterozygotes in the populations that was supported by negative values of the fixation index (FIS) and deviations from the Hardy-Weinberg equilibrium. The Euclidean distance, the pairwise FST, and the pairwise Nei’s GST genetic distances revealed that Yorkshire and Landrace breeds are genetically the closest, with distance values of 2.242, 0.029, and 0.033, respectively. Conversely, Landrace and Duroc breeds showed the highest genetic divergence, with distance values of 2.815, 0.048, and 0.052, respectively. We identified 28 significant SNPs that are related to phenotypic traits and these SNPs were able to differentiate between the pig breeds with high accuracy. The Functional Enrichment Analysis of the informative SNPs highlighted biological functions related to DNA packaging, chromatin integrity, and the preparation of DNA into higher-order structures.Conclusion: Our study sheds light on the genetic underpinnings of phenotypic variation among three Italian pig breeds, offering potential insights into the mechanisms driving breed differentiation. By prioritizing breed-specific coding SNPs, our approach enables a more focused analysis of specific genomic regions relevant to the research question compared to analyzing the entire genome.

BACKGROUND:Assigning animals to their corresponding breeds through breed informative single-nucleotide polymorphisms (SNPs) is required in many fields. For instance, it is used in the traceability and the authentication of meat and other livestock products. SNPs' information for several pork breeds are now accessible thanks to the availability of dense SNP chips. These SNP chips cover a large number of molecular markers distributed across the entire genome. To identify the pork breed from a sample of industrial meat, one must analyze a large panel of genetic markers depending on the SNP chip used. The analysis of such large datasets requires intensive work. This leads to the idea of creating less dense chips of breed informative markers based on a reduced number of SNPs. Therefore, the analysis of the data emanating from the genotyping of these reduced chips will require less time and effort.AIM:The objective of this study is to find the most informative SNPs for the discrimination between four pig breeds, namely Duroc, Landrace, Large White, and Pietrain.METHOD:The Illumina Porcine 60 k SNP chip was used to genotype SNPs distributed all over the individuals' genomes. Firstly, we used three different statistical approaches for feature selection: (i) principal component analysis (PCA), (ii) least absolute shrinkage and selection operator (LASSO), and (iii) random forest (RF). These three approaches identified three sets of SNPs; each set corresponds to one approach. Then, we combined the results of the three methods by setting up a final panel containing the SNPs which appear on the three sets altogether.RESULTS:Separately, each method resulted in a panel with the corresponding most discriminating SNPs. The PCA, the LASSO, and the random forest with Boruta algorithm highlighted 28,816, 50, and 286 SNPs, respectively. The number of SNPs selected by PCA is high compared to Boruta and LASSO because PCA chooses the variables while preserving as much information about the data as possible. The only downside of LASSO regression is that among a group of correlated variables, LASSO tends to select only one variable and ignore the others regardless of their importance. Contrarily to LASSO, the Boruta algorithm considers the interdependence between SNPs and selects informative variables even if they are correlated and have the same effect. The three panels shared 23 SNPs; the distribution of the individuals according to these SNPs showed a grouping of individuals of each breed in well-defined clusters without any overlapping.CONCLUSIONS:The biological pathways represented by 23 breed informative SNPs resulted by the combination of PCA, LASSO, and Boruta should be explored in further analysis. The results provided by our study are promising for further applications of this method in other livestock animals.

The aim of this descriptive cross-sectional study is to evaluate the knowledge of toxoplasmosis among medical, biology, and veterinary students in Rabat in Morocco. The data was collected by using a questionnaire which includes demographic characteristics, epidemiology, diagnosis, and clinical issues related to knowledge of toxoplasmosis. During analysis, the study groups were divided based upon their specialty of students who were medical, biology, and veterinary students. Out of 230 students, 55.2% were female and 44.8% were male. The average age of the study population is 21.7 ± 02 years. Less than half (42.6%) have heard of the disease; most of them have heard from faculty during studies in classrooms with 75.8%, and 3.2% were from the internet. Only 36.5% knew the correct causative agent of toxoplasmosis, and 32.1% were aware of the definitive host. The current study documented that there are gaps in the knowledge of the students regarding toxoplasmosis. Therefore, the present study puts the basis for future studies highlighting the importance of educating students to improve knowledge and attitudes towards toxoplasmosis.