MOTIVATION: Post-genome-wide association studies (pGWAS) analysis is designed to decipher the functional consequences of significant single-nucleotide polymorphisms (SNPs) in the era of GWAS. This can be translated into research insights and clinical benefits such as the effectiveness of strategies for disease screening, treatment and prevention. However, the setup of pGWAS (pGWAS) tools can be quite complicated, and it mostly requires big data. The challenge however is, scientists are required to have sufficient experience with several of these technically complex and complicated tools in order to complete the pGWAS analysis. RESULTS: We present SysBiolPGWAS, a pGWAS web application that provides a comprehensive functionality for biologists and non-bioinformaticians to conduct several pGWAS analyses to overcome the above challenges. It provides unique functionalities for analysis involving multi-omics datasets and visualization using various bioinformatics tools. SysBiolPGWAS provides access to individual pGWAS tools and a novel custom pGWAS pipeline that integrates several individual pGWAS tools and data. The SysBiolPGWAS app was developed to be a one-stop shop for pGWAS analysis. It targets researchers in the area of the human genome and performs its analysis mainly in the autosomal chromosomes. AVAILABILITY AND IMPLEMENTATION: SysBiolPGWAS web app was developed using JavaScript/TypeScript web frameworks and is available at: https://spgwas.waslitbre.org/. All codes are available in this GitHub repository https://github.com/covenant-university-bioinformatics.

Despite the advancement in the management of prostate cancer in recent years, treatment strategies are only efficient against localized disease while managing metastatic cancer remains a challenge. As a result, the global burden of the disease has remained significant. Efficient and personalized management of the disease before metastasis is therefore of prime importance. In this study, we developed a classifier to predict the response of prostate cancer patients to treatment leveraging on multi-omics datasets provided by The Cancer Genome Atlas (TCGA). Our investigation using ten machine learning algorithms reveals that tree-based algorithms had better predictive performance than probabilistic models such as Naive Bayes and kernel-based methods such as Support Vector Machines. We also investigated the performance of all possible omics combinations. Our results show that there is an overall increase in performance when multiple omics datasets are used in contrast to single omics strategies. We have predicted for the first time, possible androgen deprivation treatment response outcomes for 68 prostate cancer patients with missing phenotype values in the TCGA dataset.

In the originally published version of this manuscript, the statement in the Acknowledgements section was incomplete and read as follows: This work is fully supported by the Covenant University and Covenant University Center for Research, Innovation and Discovery. This section has now been updated.

Essential genes are critical for the growth and survival of any organism. The machine learning approach complements the experimental methods to minimize the resources required for essentiality assays. Previous studies revealed the need to discover relevant features that significantly classify essential genes, improve on the generalizability of prediction models across organisms, and construct a robust gold standard as the class label for the train data to enhance prediction. Findings also show that a significant limitation of the machine learning approach is predicting conditionally essential genes. The essentiality status of a gene can change due to a specific condition of the organism. This review examines various methods applied to essential gene prediction task, their strengths, limitations and the factors responsible for effective computational prediction of essential genes. We discussed categories of features and how they contribute to the classification performance of essentiality prediction models. Five categories of features, namely, gene sequence, protein sequence, network topology, homology and gene ontology-based features, were generated for Caenorhabditis elegans to perform a comparative analysis of their essentiality prediction capacity. Gene ontology-based feature category outperformed other categories of features majorly due to its high correlation with the genes' biological functions. However, the topology feature category provided the highest discriminatory power making it more suitable for essentiality prediction. The major limiting factor of machine learning to predict essential genes conditionality is the unavailability of labeled data for interest conditions that can train a classifier. Therefore, cooperative machine learning could further exploit models that can perform well in conditional essentiality predictions. SHORT ABSTRACT: Identification of essential genes is imperative because it provides an understanding of the core structure and function, accelerating drug targets' discovery, among other functions. Recent studies have applied machine learning to complement the experimental identification of essential genes. However, several factors are limiting the performance of machine learning approaches. This review aims to present the standard procedure and resources available for predicting essential genes in organisms, and also highlight the factors responsible for the current limitation in using machine learning for conditional gene essentiality prediction. The choice of features and ML technique was identified as an important factor to predict essential genes effectively.

Data is useless if information or knowledge that can be used for further reasoning cannot be inferred from it. Cluster analysis, based on some criteria, shares data into important, practical or both categories (clusters) based on shared common characteristics. In research, clustering and classification have been used to analyze data, in the field of machine learning, bioinformatics, statistics, pattern recognition to mention a few. Different methods of clustering include Partitioning (K-means), Hierarchical (AGNES), Density-based (DBSCAN), Grid-based (STING), Soft clustering (FANNY), Model-based (SOM) and Ensemble clustering. Challenges and problems in clustering arise from large datasets, misinterpretation of results and efficiency/performance of clustering algorithms, which is necessary for choosing clustering algorithms. In this paper, application of data clustering was systematically discussed in view of the characteristics of the different clustering techniques that make them better suited or biased when applied to several types of data, such as uncertain data, multimedia data, graph data, biological data, stream data, text data, time series data, categorical data and big data. The suitability of the available clustering algorithms to different application areas was presented. Also investigated were some existing cluster validity methods used to evaluate the goodness of the clusters produced by the clustering algorithms.

In this study, differentially expressed genes for the trophozoite and schizont stages of Plasmodium falciparum's life cycle were extracted from a time series RNA-Seq gene expression experiment. About 28% of the 5,270 genes used in the experiment were found to show significant expression at these stages. Enrichment analysis using Gene Ontology implicated a total of 62 functions as highly enriched from the list of differentially expressed genes (DEGs). Some include; protein targeting to membrane, protein import, establishment of proteins localization to organelle, ribonucleic protein complex, nucleotide-excision repair and processes related to the mitochondria. A protein interaction network (PIN) for the DEGs at the schizont stage was extracted from experimental data of protein-protein interactions and supplemented with data from a protein interaction database. We predicted a number of protein-protein interactions in Plasmodium falciparum that may be implicated in invasion of the human red blood cells (RBCs). Some of these predictions are consistent with those from previous studies while quite a number of them are novel. We also identified 16 protein complexes from the PIN using the Molecular Complex Detection (MCODE) algorithm. The functional enrichment of the identified protein complexes showed functions related to gene expression, translation, RNA transport and metabolic/biological processes which have been identified to be important in the invasion process. The result from this study is meant to provide better insight into disease at hand.