16The introduction of several differential gene expression analysis tools has made it difficult for 17 researchers to settle on a particular tool for RNA-seq analysis. This coupled with the appropriate 18 determination of biological replicates to give an optimum representation of the study population 19 and make biological sense. To address these challenges, we performed a survey of 8 tools used 20 for differential expression in RNA-seq analysis. We simulated 39 different datasets (from 10 to 21 200 replicates, at an interval of 5) using compcodeR with a maximum of 100 replicates. Our goal 22 was to determine the effect of varying the number of replicates on the performance (F1-score, 23 recall and precision) of the tools. EBSeq and edgeR-glmRT recorded the highest (0.9385) and 24 lowest (0.6505) average F1-score across all replicates, respectively. We also performed a 25 pairwise comparison of all the tools to determine their concordance with each other in 26 identifying differentially expressed genes. We found the greatest concordance to be between 27 limma voom treat and limma voom ebayes. Finally, we recommend employing edgeR-glmRT for 28 RNA-seq experiments involving 10-50 replicates and edgeR-glmQLF for studies with 55 to 200 29 replicates. 30Author summary 31 Downstream analysis of RNA-seq data in R often poses several challenges to researchers as it is 32 a daunting task to choose a specific differential expression analysis tool over another. 33Researchers also find it challenging to determine the number (replicates) of samples to use in 34 order to give comparable and accurate results. In this paper, we surveyed eight differential 35 expression analysis tools using different number of replicates of simulated RNA-seq count data. 36 We measured the performance of each tool and based on the recorded F1-scores, recall and 37 precision, we made the following recommendations; consider edgeR-glmRT and edgeR-glmQLF 38 for replicates of 10-50 and 55-200 respectively. 39 40 42 exponential increase in RNA-seq data generation with an equivalent rise in the development of 43 algorithms for differential gene expression (DGE) analyses with varying performances. These 44 methods seek to make data analyses relatively easier and address complex biological questions 45 with greater levels of statistical confidence. However, the challenge still remains the selection of 46 optimal DGE tools and sample size calculations for optimal accuracy. This makes the selection 47 of tools and sample sizes for optimum analyses a very crucial but daunting task. 48Over the years, several research articles have been published that address the lack of consensus 49 among DGE tools. Examples of these are the works of Seyednasrollah et al (1) who performed a 50 systematic comparison of some popular DGE tools and provided recommendations for choosing 51 the optimal tool. Rapaport et al (2) assessed a number of tools based on the performance of 52 normalization, false-positive rates and the effect of sequencing depth and sample ...

Meningitis is an inflammation of the meninges, which covers the brain and spinal cord. Every year, most individuals within sub-Saharan Africa suffer from meningococcal meningitis. Moreover, tens of thousands of these cases result in death, especially during major epidemics. The transmission dynamics of the disease keep changing, according to health practitioners. The goal of this study is to exploit robust mechanisms to manage and prevent the disease at a minimal cost due to its public health implications. A significant concern found to aid in the transmission of meningitis disease is the movement and interaction of individuals from low-risk to high-risk zones during the outbreak season. Thus, this article develops a mathematical model that ascertains the dynamics involved in meningitis transmissions by partitioning individuals into low- and high-risk susceptible groups. After computing the basic reproduction number, the model is shown to exhibit a unique local asymptotically stability at the meningitis-free equilibrium E † , when the effective reproduction number R 0 < 1 , and the existence of two endemic equilibria for which R 0 † < R 0 < 1 and exhibits the phenomenon of backward bifurcation, which shows the difficulty of relying only on the reproduction number to control the disease. The effective reproductive number estimated in real time using the exponential growth method affirmed that the number of secondary meningitis infections will continue to increase without any intervention or policies. To find the best strategy for minimizing the number of carriers and infected individuals, we reformulated the model into an optimal control model using Pontryagin’s maximum principles with intervention measures such as vaccination, treatment, and personal protection. Although Ghana’s most preferred meningitis intervention method is via treatment, the model’s simulations demonstrated that the best strategy to control meningitis is to combine vaccination with treatment. But the cost-effectiveness analysis results show that vaccination and treatment are among the most expensive measures to implement. For that reason, personal protection which is the most cost-effective measure needs to be encouraged, especially among individuals migrating from low- to high-risk meningitis belts.

The article elaborates on the program highlights of the 3rd African Student Council Symposium 2019. The one-day symposium was held in Kwame Nkrumah University of Science and Technology (KNUST), Ghana, on 11 November 2019 during the 6th joint international bioinformatics conference of the ISCB and ASBCB. It consisted of three sessions that included keynote talks by Prof Christine Orengo and Dr. Amel Ghouila, and seven selected student speaker talks from different areas of bioinformatics. The students benefited from networking and learning about ongoing research work by their peers hailing from different countries of the African region. The symposium proved to be pivotal to strengthen connections in the African bioinformatics student community.

Globally, one out of every two reported cases of hematologic malignancies (HMs) results in death. Each year approximately 1.24 million cases of HMs are recorded, of which 58% become fatal. Early detection remains critical in the management and treatment of HMs. However, this is thwarted by the inadequate number of reliable biomarkers. In this study, we mined public databases for RNA-seq data on four common HMs intending to identify novel biomarkers that could serve as HM management and treatment targets. A standard RNA-seq analysis pipeline was strictly adhered to in identifying differentially expressed genes (DEGs) with DESeq2, limma+voom and edgeR. We further performed gene enrichment analysis, protein-protein interaction (PPI) network analysis, survival analysis and tumor immune infiltration level detection on the genes using G:Profiler, Cytoscape and STRING, GEPIA tool and TIMER, respectively. A total of 2,136 highly-ranked DEGs were identified in HM vs. non-HM samples. Gene ontology and pathway enrichment analyses revealed the DEGs to be mainly enriched in steroid biosynthesis (5.075×10 -4 ), cholesterol biosynthesis (2.525×10 -8 ), protein binding (3.308×10 -18 ), catalytic activity (2.158×10 -10 ) and biogenesis (5.929×10 -8 ). The PPI network resulted in 60 hub genes which were verified with data from TCGA, MET500, CPTAC and GTEx projects. Survival analyses with clinical data from TCGA showed that high expression of SRSF1 , SRSF6 , UBE2Z and PCF11, and low expression of HECW2 were correlated with poor prognosis in HMs. In summary, our study unraveled essential genes that could serve as potential biomarkers for prognosis and may serve as drug targets for HM management.

Burkitt lymphoma (BL) is one of the most aggressive forms of non‐Hodgkin's lymphomas that affect children and young adults. The expression of genes and other molecular markers during carcinogenesis can be the basis for diagnosis, prognosis and the design of new and effective drugs for the management of cancers. The aim of this study was to identify genes that can serve as prognostic and therapeutic targets for BL. We analysed RNA‐seq data of BL transcriptome sequencing projects in Africa using standard RNA‐seq analyses pipeline. We performed pathway enrichment analyses, protein–protein interaction networks, gene co‐expression and survival analyses. Gene and pathway enrichment analyses showed that the differentially expressed genes are involved in tube development, signalling receptor binding, viral protein interaction, cell migration, external stimuli response, serine hydrolase activity and PI3K‐Akt signalling pathway. Protein–protein interaction network analyses revealed the genes to be highly interconnected, whereas module analyses revealed 25 genes to possess the highest interaction score. Overall survival analyses delineated six genes (ADAMTSL4, SEMA5B, ADAMTS15, THBS2, SPON1 and THBS1) that can serve as biomarkers for prognosis for BL management.