Biology subjects

Awe, O. I.

Publications and source records attributed to Awe, O. I..

13 recordsLinked to original sources

bioRxiv2025

EMImR: a Shiny Application for Identifying Transcriptomic and Epigenomic Changes

Identifying differentially expressed genes associated with genetic pathologies is crucial to understanding the biological differences between healthy and diseased states and identifying potential biomarkers and therapeutic targets. However, gene expression profiles are controlled by various mechanisms including epigenomic changes, such as DNA methylation, histone modifications, and interfering microRNA silencing. We developed a novel Shiny application for transcriptomic and epigenomic change identification and correlation using a combination of Bioconductor and CRAN packages. The developed package, named EMImR, is a user-friendly tool with an easy-to-use graphical user interface to identify differentially expressed genes, differentially methylated genes, and differentially expressed interfering miRNA. In addition, it identifies the correlation between transcriptomic and epigenomic modifications and performs the ontology analysis of genes of interest. The developed tool could be used to study the regulatory effects of epigenetic factors. The application is publicly available in the GitHub repository (https://github.com/omicscodeathon/emimr).

bioinformatics↗

bioRxiv2025

rhinotypeR: An R package for Rhinovirus Genotyping

Rhinoviruses (RV) are common pathogens characterized by extremely high antigenic and genotypic diversity, yet the tools for their genotyping remain limited. We present rhinotypeR, a Bioconductor package designed to streamline the genotyping of RVs using the VP4/2 region by automating sequence comparison against prototype strains and applying predefined pairwise distance thresholds. RhinotypeR offers a comprehensive suite of functions, including sequence alignment, genetic distance calculation, and genotype assignment, which collectively simplify the often convoluted process of RV classification. Additionally, the package supports visualization tools for single-nucleotide polymorphisms (SNPs), amino acid substitutions, and phylogenetic relationships, providing an accessible platform for genetic analysis and evolutionary studies of RV. Comparative analyses demonstrate that genetic distance calculations by rhinotypeR align closely with established software such as MegaX and ape. RhinotypeR addresses the need for accessible and robust genotyping tools by offering a suite of functions that support the workflow of rhinovirus genetic analysis. It empowers researchers with a toolkit that enhances data processing efficiency and provides detailed, actionable insights into the genetic diversity and evolution of rhinoviruses.

bioinformatics↗

bioRxiv2024

NeuroVar: An Open-source Tool for Gene Expression and Variation Data Visualization for Biomarkers of Neurological Diseases

BackgroundThe expanding availability of large-scale genomic data and the growing interest in uncovering gene-disease associations call for efficient tools to visualize and evaluate gene expression and genetic variation data. MethodologyData collection involved filtering biomarkers related to multiple neurological diseases from the ClinGen database. We developed a comprehensive pipeline that was implemented as an interactive Shiny application and a standalone desktop application. ResultsNeuroVar is a tool for visualizing genetic variation (single nucleotide polymorphisms and insertions/deletions) and gene expression profiles of biomarkers of neurological diseases. ConclusionThe tool provides a user-friendly graphical user interface to visualize genomic data and is freely accessible on the projects GitHub repository (https://github.com/omicscodeathon/neurovar).

bioinformatics↗

bioRxiv2024

Multiple Sclerosis Stages and their Differentially Expressed Genes: A Bioinformatics Analysis

Multiple Sclerosis (MS) is an inflammatory, chronic, autoimmune, and demyelinating disease of the central nervous system. MS is a heterogeneous disease with three main clinical forms, affecting the progression and therefore the treatment of the disease. Thus, finding key genes and microRNAs (miRNA) associated with MS stages and analyzing their interactions is important to better understand the molecular mechanism underlying the occurrence and the evolution of MS. Based on publicly available datasets of mRNA and miRNA expression profiles, differentially expressed genes (DEGs) and differentially expressed miRNAs (DEMs) between patients with different stages of MS and healthy controls and between relapsing and remitting phases of RRMS were determined using Deseq2 and GEO2R tools. We then analyzed miRNA-mRNA regulatory interactions and gene ontology for the DEGs. Based on miRNA-mRNA regulatory interactions, we identified potential biomarkers of RRMS, 13 upregulated miRNA regulators of 30 downregulated genes and 17 downregulated miRNA regulators of 32 upregulated genes. We also identified 9 downregulated miRNA regulators of 12 upregulated genes as potential biomarkers of SPMS. Our study findings highlight some key protein-coding genes and miRNAs that are involved in the occurrence and evolution of MS.

bioinformatics↗

bioRxiv2023

Computational Identification of Potential Inhibitors Targeting cdk1 in Colorectal Cancer

Despite improved treatment options, colorectal cancer (CRC) remains a huge public health concern with a significant impact on affected individuals. Cell cycle dysregulation and overexpression of certain regulators and checkpoint activators are important recurring events in the progression of cancer. Cyclin-dependent kinase 1 (CDK1), a key regulator of the cell cycle component central to the uncontrolled proliferation of malignant cells, has been reportedly implicated in CRC. This study aimed to identify CDK1 inhibitors with potential for clinical drug research in CRC. Ten thousand (10,000) naturally occurring compounds were evaluated for their inhibitory efficacies against CDK1 through molecular docking studies. The stability of the lead compounds in complex with CDK1 was evaluated using molecular dynamics simulation for one thousand (1,000) nanoseconds. The top-scoring candidates ADME characteristics and drug-likeness were profiled using SwissADME. Four hit compounds namely spiraeoside, robinetin, 6-hydroxyluteolin, and quercetagetin were identified from molecular docking analysis to possess the least binding scores. Molecular dynamics simulation revealed that robinetin and 6-hydroxyluteolin complexes were stable within the binding pocket of the CDK1 protein. The findings from this study provide insight into novel candidates with specific inhibitory CDK1 activities that can be further investigated through animal testing, clinical trials, and drug development research for CRC treatment.

bioinformatics↗

bioRxiv2023

Investigating Antimicrobial Resistance Genes in Kenya, Uganda and Tanzania Cattle Using Metagenomics

Antimicrobial resistance (AMR) is a growing problem in African cattle production systems, posing a threat to human and animal health and the associated economic value chain. However, there is a poor understanding of the resistomes in small-holder cattle breeds in East African countries. This study aims to examine the distribution of Antimicrobial Resistance Genes (ARGs) in Kenya, Tanzania, and Uganda cattle using a metagenomics approach. We used the SqueezeMeta-Abricate (assembly-based) pipeline to detect ARGs and benchmarked this approach using the Centifuge-AMRplusplus (read-based) pipeline to evaluate its efficiency. Our findings reveal a significant number of ARGs of critical medical and economic importance in all three countries, including resistance to drugs of last resort such as carbapenems, suggesting the presence of highly virulent and antibiotic-resistant bacterial pathogens (ESKAPE) circulating in East Africa. Shared ARGs such as aph(6)-id (aminoglycoside phosphotransferase), tet (tetracycline resistance gene), sul2 (sulfonamide resistance gene) and cfxA_gen (betalactamase gene) were detected. Assembly-based methods revealed fewer ARGs compared to read-based methods, indicating the sensitivity and specificity of read-based methods in resistome characterization. Our findings call for further surveillance to estimate the intensity of the antibiotic resistance problem and wider resistome classification. Effective management of livestock and antibiotic consumption is crucial in minimizing antimicrobial resistance and maximizing productivity, making these findings relevant to stakeholders, agriculturists, and veterinarians in East Africa and Africa at large.

bioinformatics↗

bioRxiv2023

Differential Expression Analysis of miRNAs and mRNAs in Epilepsy Uncovers Potential Biomarkers

Epilepsy is a neurological disease defined by episodes of synchronous convulsions. Recently, miRNAs have been proven as promising biomarkers for multiple ailments like tumors and neurodegenerative disorders; their role in epilepsy is still unclear. This study aimed to understand the involvement of miRNAs in the disease and to detect the potential biomarkers for the treatment of epilepsy. RNA transcripts, and miRNA from brain tissue and plasma small extracellular vesicle samples of epileptogenic patients from 6 different studies downloaded from the NCBI sequence read archive (SRA) were analyzed with particular interest in genes that might be involved in epilepsy. Alignment of transcripts to hg38 was done using HISAT2 and the raw counts were generated using HTseq-count. miRNA genes were identified using miRDeep2. EdgeR and GEO2 were used to identify DEGs for both mRNA and miRNA datasets. Finally, TargetScan web tool was used to predict potentially significantly expressed mRNA target genes using the identified miRNA genes. Analysis of these datasets revealed target genes in epilepsy and their associated miRNAs. SIX4 and KCTD7 were under-expressed in epileptogenic zones of the brain compared to the irritative zone. CABP1, SLC20A1 and SLC35G1 were under-expressed in brain tissues. Hsa-miR-27a-3p was identified as a regulator of CABP1 expression, hsa-let-7b-5p regulates SLC20A1 while hsa-miR-15a-5p and hsa-miR-195-5p are regulators for SLC35G1. These observations highlight the importance of miRNAs as novel biomarkers of epilepsy. Understanding and controlling these regulatory interactions may help to define potential therapies for epilepsy. This would also help to better understand miRNA-mediated gene regulation in epilepsy.

bioinformatics↗

bioRxiv2023

Expression Level Analysis of ACE2 Receptor Gene in African-American and Non-African-American COVID-19 Patients

BackgroundThe COVID-19 pandemic caused by SARS-CoV-2 has spread rapidly across the continents. While the incidence of COVID-19 has been reported to be higher among African-American individuals, the rate of mortality has been lower compared to that of non-African-Americans. ACE2 is involved in COVID-19 as SARS-CoV-2 uses the ACE2 enzyme to enter host cells. Although the difference in COVID-19 incidence can be explained by many factors such as low accessibility of health insurance among the African-American community, little is known about ACE2 expression in African-American COVID-19 patients compared to non-African-American COVID-19 patients. The variable expression of genes can contribute to this observed phenomenon. MethodologyIn this study, transcriptomes from African-American and non-African-American COVID-19 patients were retrieved from the sequence read archive and analyzed for ACE2 gene expression. HISAT2 was used to align the reads to the human reference genome, and HTseq-count was used to get raw gene counts. EdgeR was utilized for differential gene expression analysis, and enrichR was employed for gene enrichment analysis. ResultsThe datasets included 14 and 33 transcriptome sequences from COVID-19 patients of African-American and non-African-American descent, respectively. There were 24,092 differentially expressed genes, with 7,718 upregulated (log fold change > 1 and FDR 0.05) and 16,374 downregulated (log fold change -1 and FDR 0.05). The ACE2 mRNA level was found to be considerably downregulated in the African-American cohort (p-value = 0.0242, p-adjusted value = 0.038). ConclusionThe downregulation of ACE2 in the African-American cohort could indicate a correlation to the low COVID-19 severity observed among the African-American community.

genetics↗

bioRxiv2023

Enhanced Deep Convolutional Neural Network for SARS-CoV-2 Variants Classification

High-throughput sequencing techniques and sequence analysis have enabled the taxonomic classification of pathogens present in clinical samples. Sequencing provides an unbiased identification and systematic classification of pathogens and this is generally achieved by comparing novel sequences to pre-existing annotated reference databases. However, this approach is limited by large-scale reference databases which require considerable computational resources and skills to compare against. Alternative robust methods such as machine learning are currently employed in genome sequence analysis and classification, and it can be applied in classifying SARS-CoV-2 variants, whose continued evolution has resulted in the emergence of multiple variants. We developed a deep learning Convolutional Neural Networks-Long Short Term Memory (CNN-LSTM) model to classify dominant SARS-CoV-2 variants (omicron, delta, beta, gamma and alpha) based on gene sequences from the surface glycoprotein (spike gene). We trained and validated the model using > 26,000 SARS-CoV-2 sequences from the GISAID database. The model was evaluated using unseen 3,057 SARS-CoV-2 sequences. The model was compared to existing molecular epidemiology tool, nextclade. Our model achieved an accuracy of 98.55% on training, 99.19% on the validation and 98.41% on the test dataset. Comparing the proposed model to nextclade, the model achieved significant accuracy in classifying SARS-CoV-2 variants from unseen data. Nextclade identified the presence of recombinant strains in the evaluation data, a mechanism that the proposed model did not detect. This study provides an alternative approach to pre-existing methods employed in the classification of SARS-CoV-2 variants. Timely classification will enable effective monitoring and tracking of SARS-CoV-2 variants and inform public health policies in the control and management of the COVID-19 pandemic.

bioinformatics↗

bioRxiv2023

Multi-omics data integration approach identifies potential biomarkers for Prostate cancer

Prostate cancer (PCa) is one of the most common malignancies, and many studies have shown that PCa has a poor prognosis, which varies across different ethnicities. This variability is caused by genetic diversity. High-throughput omics technologies have identified and shed some light on the mechanisms of its progression and finding new biomarkers. Still, a systems biology approach is needed for a holistic molecular perspective. In this study, we applied a multi-omics approach to data analysis using different publicly available omics data sets from diverse populations to better understand the PCa disease etiology. Our study used multiple omic datasets, which included genomic, transcriptomic and metabolomic datasets, to identify drivers for PCa better. Individual omics datasets were analysed separately based on the standard pipeline for each dataset. Furthermore, we applied a novel multi-omics pathways algorithm to integrate all the individual omics datasets. This algorithm applies the p-values of enriched pathways from unique omics data types, which are then combined using the MiniMax statistic of the PathwayMultiomics tool to prioritise pathways dysregulated in the omics datasets. The single omics result indicated an association between up-regulated genes in RNA-Seq data and the metabolomics data. Glucose and pyruvate are the primary metabolites, and the associated pathways are glycolysis, gluconeogenesis, pyruvate kinase deficiency, and the Warburg effect pathway. From the interim result, the identified genes in RNA-Seq single omics analysis are linked with the significant pathways from the metabolomics analysis. The multi-omics pathway analysis will eventually enable the identification of biomarkers shared amongst these different omics datasets to ease prostate cancer prognosis.

bioinformatics↗

bioRxiv2022

Transcriptional Profiles Analysis of COVID-19 and Malaria Patients Reveals Potential Biomarkers in Children

The clinical presentation overlap between malaria and COVID-19 poses special challenges for rapid diagnosis in febrile children. In this study, we collected RNA-seq data of children with malaria and COVID-19 infection from the public databases as raw data in fastq format paired end files. A group of six, five and two biological replicates of malaria, COVID-19 and healthy donors respectively were used for the study. We conducted differential gene expression analysis to visualize differences in the expression profiles. Using edgeR, we explored particularly gene expression levels in different phenotype groups and found that 1084 genes and 2495 genes were differentially expressed in the malaria samples and COVID-19 samples respectively when compared to healthy controls. The highly expressed gene in the COVID-19 group we found CD151 gene which is facilitates in T cell proliferation, while in the malaria group, among the highly expressed gene we identified GBP5 gene which involved in inflammatory response and response to bacterium. By comparing both malaria and COVID-19 infections, the overlap of 62 differentially expressed genes patterns were identified. Among them, three genes (ENSG00000234998, H2AC19 and TXNDC5) were highly upregulated in both infections. Strikingly, we observed 13 genes such as HBQ1, HBM, SLC7A5, SERINC2, ATP6V0C, ST6GALNAC4, RAD23A, PNPLA2, GAS2L1, TMEM86B, SLC6A8, UBALD1, RNF187 were downregulated in children with malaria and uniquely upregulated in children with COVID-19, thus may be further validated as potential biomarkers to delineate COVID-19 from malaria-related febrile infection. The hemoglobin complexes and lipid metabolism biological pathways are highly expressed in both infections. Our study provided new insights for further investigation of the biological pattern in hosts with malaria and COVID-19 coinfection.

bioinformatics↗

bioRxiv2022

Molecular Phylogenetics of HIV-1 Subtypes in African Populations: A Case Study of Sub-Saharan African Countries

Despite advances in antiretroviral therapy that have revolutionized HIV-1 disease management, effective control of the HIV-1 infection pandemic remains elusive. Increased HIV-1 infection rates and genetic diversity in Sub-Saharan African countries pose a challenge in HIV-1 clinical management. This study provides a picture of HIV-1 genetic diversity and its implications for HIV-1 disease spread and the effectiveness of therapies in Africa. Whole-genome sequences of HIV-1 were obtained from Genbank using the accession numbers from 10 African countries with high HIV-1 prevalence. Alignment composed of the query and reference sequences retrieved from the Los Alamos database. The alignment file was viewed and curated in Aliview. Phylogenetic analysis was done by constructing a phylogenetic tree using the maximum likelihood method implemented in IQ-TREE. The clustering pattern of the studied countries showed both homogeneous clustering and heterogeneous clustering with all Zambia sequences clustering with HIV-1 subtype-C indicating local distribution of only subtype-C. Sequences from nine countries showed heterogeneous clustering along with different subtypes as well as individual clustering of the sequences away from references suggesting cross border genetic exchange. Sequences from Kenya and Nigeria clustered with almost all the HIV-1 subtypes suggesting high HIV-1 genetic diversity in Kenya and Nigeria as compared to other African countries. Our results indicate that there is the presence of subtype-specific HIV-1 polymorphisms and interactions during border movements.

genomics↗

bioRxiv2018

GeneHummus: A pipeline to define gene families and their expression in legumes and beyond

During the last decade, plant biotechnological laboratories have sparked a monumental revolution with the rapid development of next sequencing technologies at affordable prices. Soon, these sequencing technologies and assembling of whole genomes will extend beyond the plant computational biologists and become commonplace within the plant biology disciplines. The current availability of large-scale genomic resources for non-traditional plant model systems (the so-called orphan crops) is enabling the construction of high-density integrated physical and genetic linkage maps with potential applications in plant breeding. The newly available fully sequenced plant genomes represent an incredible opportunity for comparative analyses that may reveal new aspects of genome biology and evolution. Analysis of the expansion and evolution of gene families across species is a common approach to infer biological functions. To date, the extent and role of gene families in plants has only been partially addressed and many gene families remain to be investigated. Manual identification of gene families is highly time-consuming and laborious, requiring an iterative process of manual and computational analysis to identify members of a given family, typically combining numerous BLAST searches and manually cleaning data. Due to the increasing abundance of genome sequences and the agronomical interest in plant gene families, the field needs a clear, automated annotation tool. Here, we present the GeneHummus pipeline, a step-by-step R-based pipeline for the identification, characterization and expression analysis of plant gene families. The impact of this pipeline comes from a reduction in hands-on annotation time combined with high specificity and sensitivity in extracting only proteins from the RefSeq database and providing the conserved domain architectures based on SPARCLE. As a case study we focused on the auxin receptor factors gene (ARF) family in Cicer arietinum (chickpea) and other legumes. We anticipate that our pipeline should be suitable for any plant gene family, and likely other gene families, vastly improving the speed and ease of genomic data processing.

bioinformatics↗