Marion O. Adebiyi

Ten Simple Rules for Organizing a Virtual Conference—Anywhere

1 International Institute of Tropical Agriculture, Nairobi, Kenya, 2 Faculty of Life Sciences, The University of Manchester, Manchester, United Kingdom, 3 Department of Computer and Information Sciences, Covenant University, Ota, Nigeria, 4 Institute of Bioinformatics, Johannes Kepler University, Linz, Austria, 5 Moroccan Society for Bioinformatics Institute, Morocco, 6 South African National Bioinformatics Institute, University of the Western Cape, Bellville, South Africa, 7 University of Cape Town, Cape Town, South Africa, 8 University of Notre Dame, South Bend, Indiana, United States of America, 9 Biotechnology Unit, University of Buea, Buea, South West Region, Cameroon, 10 International Livestock Research Institute, Nairobi, Kenya, 11 Biosciences Eastern and Central Africa, Nairobi, Kenya, 12 International Center of Insect Physiology and Ecology, Nairobi, Kenya, 13 Bioinformatics Organization, Hudson, Massachusetts, United States of America, 14 Bioinformatics Team, Center for Development of Advanced Computing, Pune University Campus, Pune, India, 15 Harvard School of Public Health, Boston, Massachusetts, United States of America

Computational biology and bioinformatics in Nigeria.

Over the past few decades, major advances in the field of molecular biology, coupled with advances in genomic technologies, have led to an explosive growth in the biological data generated by the scientific community. The critical need to process and analyze such a deluge of data and turn it into useful knowledge has caused bioinformatics to gain prominence and importance. Bioinformatics is an interdisciplinary research area that applies techniques, methodologies, and tools in computer and information science to solve biological problems. In Nigeria, bioinformatics has recently played a vital role in the advancement of biological sciences. As a developing country, the importance of bioinformatics is rapidly gaining acceptance, and bioinformatics groups comprised of biologists, computer scientists, and computer engineers are being constituted at Nigerian universities and research institutes. In this article, we present an overview of bioinformatics education and research in Nigeria. We also discuss professional societies and academic and research institutions that play central roles in advancing the discipline in Nigeria. Finally, we propose strategies that can bolster bioinformatics education and support from policy makers in Nigeria, with potential positive implications for other developing countries.

Development of Bioinformatics Infrastructure for Genomics Research in H3Africa

BACKGROUND Although pockets of bioinformatics excellence have developed in Africa, generally, large-scale genomic data analysis has been limited by the availability of expertise and infrastructure. H3ABioNet, a pan-African bioinformatics network, was established to build capacity specifically to enable H3Africa (Human Heredity and Health in Africa) researchers to analyze their data in Africa. Since the inception of the H3Africa initiative, H3ABioNets role has evolved in response to changing needs from the consortium and the African bioinformatics community. OBJECTIVES H3ABioNet set out to develop core bioinformatics infrastructure and capacity for genomics research in various aspects of data collection, transfer, storage, and analysis. METHODS AND RESULTS Various resources have been developed to address genomic data management and analysis needs of H3Africa researchers and other scientific communities on the continent. NetMap was developed and used to build an accurate picture of network performance within Africa and between Africa and the rest of the world, and Globus Online has been rolled out to facilitate data transfer. A participant recruitment database was developed to monitor participant enrollment, and data is being harmonized through the use of ontologies and controlled vocabularies. The standardized metadata will be integrated to provide a search facility for H3Africa data and biospecimens. Because H3Africa projects are generating large-scale genomic data, facilities for analysis and interpretation are critical. H3ABioNet is implementing several data analysis platforms that provide a large range of bioinformatics tools or workflows, such as Galaxy, the Job Management System, and eBiokits. A set of reproducible, portable, and cloud-scalable pipelines to support the multiple H3Africa data types are also being developed and dockerized to enable execution on multiple computing infrastructures. In addition, new tools have been developed for analysis of the uniquely divergent African data and for downstream interpretation of prioritized variants. To provide support for these and other bioinformatics queries, an online bioinformatics helpdesk backed by broad consortium expertise has been established. Further support is provided by means of various modes of bioinformatics training. CONCLUSIONS For the past 4 years, the development of infrastructure support and human capacity through H3ABioNet, have significantly contributed to the establishment of African scientific networks, data analysis facilities, and training programs. Here, we describe the infrastructure and how it has affected genomics and bioinformatics research in Africa.

In silico models for drug resistance.

Resistance to drugs that treat infectious disease is a major problem worldwide. The rapid emergence of drug resistance is not well understood. We present two in silico models for the discovery of drug resistance mechanisms and for combating the evolution of resistance, respectively. In the first model, we computationally investigated subgraphs of a biological interaction network that show substantial adaptations when cells transcriptionally respond to a changing environment or treatment. As a case study, we investigated the response of the malaria parasite Plasmodium falciparum to chloroquine and tetracycline treatments. The second model involves a machine learning technique that combines clustering, common distance similarity measurements, and hierarchical clustering to propose new combinations of drug targets.

Experimental Investigation of Frequency Chaos Game Representation for in Silico and Accurate Classification of Viral Pathogens from Genomic Sequences

This paper presents an experimental investigation to determine the efficacy and the appropriate order of Frequency Chaos Game Representation (FCGR) for accurate and in silico classification of pathogenic viruses. For this study, we curated genomic sequences of selected viral pathogens from the virus pathogen database and analysis resource corpus. The viral genomes were encoded using the first to seventh order FCGRs so as to produce training and testing genomic data features. Thereafter, four different kernels of naive Bayes classifier were experimentally trained and tested with the generated FCGR genomic features. The performance result with the highest average classification accuracy of 98% was returned by the third and fourth order FCGRs. However, due to consideration for memory utilization, computational efficiency vis-a-vis classification accuracy, the third order FCGR is deemed suitable for accurate classification of viral pathogens from genome sequences. This provides a promising foundation for developing genomic based diagnostic toolkit that could be used to promptly address the global incidence of epidemics from pathogenic viruses.

Breathogenomics: A Computational Architecture for Screening, Early Diagnosis and Genotyping of Lung Cancer

The genome sequences of some genes have been implicated to carry various mutations that lead to the initiation and advancement of lung cancer. In addition, it has been scientifically established that anytime we breathe out, chemicals called Volatile Organic Compounds (VOCs) are released from the breath. Hundreds of such VOCs have been uniquely identified from samples of breathe collected from lung cancer patients, which make them viable as chemical biomarkers for lung cancer. Based on the foregoing scientific breakthroughs, we developed breathogenomics, a computational architecture for screening, early diagnosis and genotyping of lung cancer victims anchored on the analysis of exhaled breath and mutational profiles of genomic biomarkers. The architecture contains two important sub-modules. At the first sub-module, the exhaled breadths of smokers or persons that are at risk of lung cancer are collected and appropriate computational algorithms are employed to determine the presence of any of the VOC biomarkers. Next, a patient with any VOC biomarker in the exhaled breath proceeds to the second sub-module, which contains appropriate computational models for the detection of mutated genes. Once mutations are detected in any of the biomarker genes found in a given patient, such patient is recommended for targeted therapy to promptly curtail the progression of the mutations to advanced stages. The breathogenomics architecture serves as a generic template for the development of clinical equipment for breath and genomic based screening, early diagnosis and genotyping of lung cancer. In this paper, we report the preliminary result obtained from the prototype that we are currently developing based on the architecture. Constructing a lung cancer early diagnosis/screening system based on the prototype when fully developed will hopefully minimize the current spate of deaths as a result of late diagnosis of the disease.

Assessing computational genomics skills: Our experience in the H3ABioNet African bioinformatics network

The H3ABioNet pan-African bioinformatics network, which is funded to support the Human Heredity and Health in Africa (H3Africa) program, has developed node-assessment exercises to gauge the ability of its participating research and service groups to analyze typical genome-wide datasets being generated by H3Africa research groups. We describe a framework for the assessment of computational genomics analysis skills, which includes standard operating procedures, training and test datasets, and a process for administering the exercise. We present the experiences of 3 research groups that have taken the exercise and the impact on their ability to manage complex projects. Finally, we discuss the reasons why many H3ABioNet nodes have declined so far to participate and potential strategies to encourage them to do so.

Alignment-Free Z-Curve Genomic Cepstral Coefficients and Machine Learning for Classification of Viruses

Accurate detection of pathogenic viruses has become highly imperative. This is because viral diseases constitute a huge threat to human health and wellbeing on a global scale. However, both traditional and recent techniques for viral detection suffer from various setbacks. In codicil, some of the existing alignment-free methods are also limited with respect to viral detection accuracy. In this paper, we present the development of an alignment-free, digital signal processing based method for pathogenic viral detection named Z-Curve Genomic Cesptral Coefficients (ZCGCC). To evaluate the method, ZCGCC were computed from twenty six pathogenic viral strains extracted from the ViPR corpus. Naïve Bayesian classifier, which is a popular machine learning method was experimentally trained and validated using the extracted ZCGCC and other alignment-free methods in the literature. Comparative results show that the proposed ZCGCC gives good accuracy (93.0385%) and improved performance to existing alignment-free methods.

In Silico Prediction of 3D Structure of Anopheles Gambiae ABCC12 Protein

In this paper, the Anopheles gambiae ABCC12 MRP protein domain sequence which contained 216 residues was obtained from the NCBI database in its fasta format (NCBI entry EAA12438.4). This MRP protein sequence was Gapped Blast using BLOSUM 62 matrix with an E-value cut-off of 0.000001 to identify the closest homologous structure as at the date of the study (Nov 2016). Additionally, the sequence was aligned with three prediction modelers, which are the Modeller v9.15 alignment script, the Swiss Model Server and the Raptor-X server for modelling based on the server’s automated choice for a suitable template. The structure predicted by Raptor-X has a higher percentage (90.1%) of residues in the most favored regions as compared to Modeller and Swiss-Model (86.5%). This paper further unveils the quality of structure predicted during homology modeling and the diverse correlation as well as the significance of ABCC12 in drug design for malaria vector.

Analyzing a single nucleotide polymorphism in schizophrenia: a meta-analysis approach

Background Schizophrenia is a severe mental disorder affecting >21 million people worldwide. Some genetic studies reported that single nucleotide polymorphism (SNP) involving variant rs1344706 from the ZNF804A gene in human beings is associated with the risk of schizophrenia in several populations. Similar results tend to conflict with other reports in literature, indicating that no true significant association exists between rs1344706 and schizophrenia. We seek to determine the level of association of this SNP with schizophrenia in the Asian population using more recent genome-wide association study (GWAS) datasets. Methods Applying a computational approach with inclusion of more recent GWAS datasets, we conducted a meta-analysis to examine the level of association of SNP rs1344706 and the risk of schizophrenia disorder among the Asian population constituting Chinese, Indonesians, Japanese, Kazakhs and Singaporeans. For a total of 21 genetic studies, including a total of 28,842 cases and 35,630 controls, regression analysis, publication bias, Cochran’s Q and I2 tests were performed. The DerSimonian and Laird random-effects model was used to assess the association of the genetic variant to schizophrenia. Leave-one-out sensitivity analysis was also conducted to determine the influence of each study on the final outcome of the association study. Results Our summarized analysis for Asian population revealed a pooled odds ratio of 1.06, 95% confidence interval of 1.01–1.11 and two-tailed P-value of 0.0228. Our test for heterogeneity showed the presence of large heterogeneity (I2=53.44%, P =0.00207) and Egger’s regression test (P =0.8763) and Begg’s test (P =0.8347), indicating no presence of publication bias among our selected studies. In our sensitivity analysis, 10 different studies comprising of ~50% of the entire study had an impact on our final results as each leave-one-out test became insignificant. Our result suggests that genetic variant rs1344706 might be associated with the development of schizophrenia in Asians.