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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., (© 2024. Springer Nature America, Inc.)

Introduction: Increasing 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., Methods: Although 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., Results: The 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., Discussion: Through 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., Competing Interests: The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest., (Copyright © 2023 Chaddad, Kaddouri, Smouni, Missbah El Idrissi, Taha, Hayah and Badaoui.)

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 (F IS ) and deviations from the Hardy-Weinberg equilibrium. The Euclidean distance, the pairwise F ST , and the pairwise Nei's G ST 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., Competing Interests: The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest., (Copyright © 2023 Hayah, Talbi, Chafai, Houaga, Botti and Badaoui.)

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., Competing Interests: The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest., (Copyright © 2023 Chafai, Hayah, Houaga and Badaoui.)

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., Competing Interests: There are no conflicts of interest., (Copyright © 2021 Sanaa Ait Hamou et al.)

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., (© 2021. The Author(s), under exclusive licence to Springer Nature B.V.)