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Figure S1. Cross-validation error in ADMIXTURE analyses. The best fit to data occurs at K = 3.

Successfully called markers on the MADCaP Array. This tab-delimited file includes genomic positions, inclusion criteria, and overlap with other arrays.

Although prostate cancer is the leading cause of cancer mortality for African men, the vast majority of known disease associations have been detected in European study cohorts. Furthermore, most genome-wide association studies have used genotyping arrays that are hindered by SNP ascertainment bias. To overcome these disparities in genomic medicine, the Men of African Descent and Carcinoma of the Prostate (MADCaP) Network has developed a genotyping array that is optimized for African populations. The MADCaP Array contains more than 1.5 million markers and an imputation backbone that successfully tags over 94% of common genetic variants in African populations. This array also has a high density of markers in genomic regions associated with cancer susceptibility, including 8q24. We assessed the effectiveness of the MADCaP Array by genotyping 399 prostate cancer cases and 403 controls from seven urban study sites in sub-Saharan Africa. Samples from Ghana and Nigeria clustered together, whereas samples from Senegal and South Africa yielded distinct ancestry clusters. Using the MADCaP array, we identified cancer-associated loci that have large allele frequency differences across African populations. Polygenic risk scores for prostate cancer were higher in Nigeria than in Senegal. In summary, individual and population-level differences in prostate cancer risk were revealed using a novel genotyping array.Significance:This study presents an Africa-specific genotyping array, which enables investigators to identify novel disease associations and to fine-map genetic loci that are associated with prostate and other cancers.

Early detection of disease outbreaks is crucial and even small improvements in detection can significantly impact on a country’s public health. In this work, we investigate the use of a crowdsourcing application and a real-time disease outbreak surveillance system for five diseases; Influenza, Gastroenteritis, Upper Respiratory Tract Infection (URTI), Scabies and Conjunctivitis, that are closely monitored in Mauritius. We also analyze and correlate the collected data with past statistics. A crowdsourcing mobile application known as Disease Outbreak Tracker (DOT) was implemented and made public. A real-time disease surveillance system using the Early Aberration Reporting System algorithm (EARS) for analysis of the collected data was also implemented. The collected data were correlated to historical data for 2017. Data were successfully collected and plotted on a daily basis. The results show that a few cases of Flu and Scabies were reported in some districts. The EARS methods C1, C2 and C3 also depicted spikes above the set threshold on some days. The study covers data collected over a period of one month. Once symptoms data were collected using DOT, probabilistic methods were used to find the disease or diseases that the user was suffering from. The data were further processed to find the extent of the disease outbreak district-wise, per disease. These data were represented graphically for a rapid understanding of the situation in each district. Our findings concur with existing data for the same period for previous years showing that the crowdsourcing application can aid in the detection of disease outbreaks.

Carriers of germline BRCA2 pathogenic sequence variants have elevated aggressive prostate cancer risk and are candidates for precision oncology treatments. We examined whether BRCA2-deficient (BRCA2d) prostate tumors have distinct genomic alterations compared with BRCA2-intact (BRCA2i) tumors. Among 2536 primary and 899 metastatic prostate tumors from the ICGC, GENIE, and TCGA databases, we identified 138 primary and 85 metastatic BRCA2d tumors. Total tumor mutation burden (TMB) was higher among primary BRCA2d tumors, although pathogenic TMB did not differ by tumor BRCA2 status. Pathogenic and total single nucleotide variant (SNV) frequencies at KMT2D were higher in BRCA2d primary tumors, as was the total SNV frequency at KMT2D in BRCA2d metastatic tumors. Homozygous deletions at NEK3, RB1, and APC were enriched in BRCA2d primary tumors, and RB1 deletions in metastatic BRCA2d tumors as well. TMPRSS2-ETV1 fusions were more common in BRCA2d tumors. These results identify somatic alterations that hallmark etiological and prognostic differences between BRCA2d and BRCA2i prostate tumors.

Background Genome-wide association studies do not always replicate well across populations, limiting the generalizability of polygenic risk scores (PRS). Despite higher incidence and mortality rates of prostate cancer in men of African descent, much of what is known about cancer genetics comes from populations of European descent. To understand how well genetic predictions perform in different populations, we evaluated test characteristics of PRS from three previous studies using data from the UK Biobank and a novel dataset of 1298 prostate cancer cases and 1333 controls from Ghana, Nigeria, Senegal, and South Africa. Results Allele frequency differences cause predicted risks of prostate cancer to vary across populations. However, natural selection is not the primary driver of these differences. Comparing continental datasets, we find that polygenic predictions of case vs. control status are more effective for European individuals (AUC 0.608–0.707, OR 2.37–5.71) than for African individuals (AUC 0.502–0.585, OR 0.95–2.01). Furthermore, PRS that leverage information from African Americans yield modest AUC and odds ratio improvements for sub-Saharan African individuals. These improvements were larger for West Africans than for South Africans. Finally, we find that existing PRS are largely unable to predict whether African individuals develop aggressive forms of prostate cancer, as specified by higher tumor stages or Gleason scores. Conclusions Genetic predictions of prostate cancer perform poorly if the study sample does not match the ancestry of the original GWAS. PRS built from European GWAS may be inadequate for application in non-European populations and perpetuate existing health disparities.

The engineering of polymeric scaffolds for tissue regeneration has known a phenomenal growth during the past decades as materials scientists seek to understand cell biology and cell–material behaviour. Statistical methods are being applied to physico-chemical properties of polymeric scaffolds for tissue engineering (TE) to guide through the complexity of experimental conditions. We have attempted using experimental in vitro data and physico-chemical data of electrospun polymeric scaffolds, tested for skin TE, to model scaffold performance using machine learning (ML) approach. Fibre diameter, pore diameter, water contact angle and Young's modulus were used to find a correlation with 3-(4,5-dimethylthiazol-2-yl)-2,5-diphenyltetrazolium bromide (MTT) assay of L929 fibroblasts cells on the scaffolds after 7 days. Six supervised learning algorithms were trained on the data using Seaborn/Scikit-learn Python libraries. After hyperparameter tuning, random forest regression yielded the highest accuracy of 62.74%. The predictive model was also correlated with in vivo data. This is a first preliminary study on ML methods for the prediction of cell–material interactions on nanofibrous scaffolds.

A number of examples of putative eukaryote-to-prokaryote horizontal gene transfer (HGT) have been proposed in the past using phylogenetic analysis in support of these claims but none have attempted to map these gene transfers to the presence of genomic islands (GIs) in the host. Two of these cases have been examined in detail, including an ATP sulfurylase (ATPS) gene and a class I fructose bisphosphate aldolase (FBA I) gene that were putatively transferred to cyanobacteria of the genus Prochlorococcus from either green or red algae, respectively. Unlike previous investigations of HGT, parametric methods were initially used to detect genomic islands, then more traditional phylogenomic and phylogenetic methods were used to confirm or deny the HGT status of these genes. The combination of these three methods of analysis- detection of GIs, the determination of genomic neighborhoods, as well as traditional phylogeny, lends strong support to the claim that trans-domain HGT has occurred in only one of these cases and further suggests a new insight into the method of transmission of FBA I, namely that cyanophage-mediated transfer may have been responsible for the HGT event in question. The described methods were then applied to a range of prochlorococcal genomes in order to characterize a candidate for eukaryote-to-prokaryote HGT that had not been previously studied by others. Application of the same methodology used to confirm or deny HGT for ATPS and FBA I identified a ⊗12 fatty acid desaturase (FAD) gene that was likely transferred to Prochlorococcus from either green or red algae.

A multitude of algorithms for sequence comparison, short-read assembly and whole-genome alignment have been developed in the general context of molecular biology, to support technology development for high-throughput sequencing, numerous applications in genome biology and fundamental research on comparative genomics. The computational complexity of these algorithms has been previously reported in original research papers, yet this often neglected property has not been reviewed previously in a systematic manner and for a wider audience. We provide a review of space and time complexity of key sequence analysis algorithms and highlight their properties in a comprehensive manner, in order to identify potential opportunities for further research in algorithm or data structure optimization. The complexity aspect is poised to become pivotal as we will be facing challenges related to the continuous increase of genomic data on unprecedented scales and complexity in the foreseeable future, when robust biological simulation at the cell level and above becomes a reality.

Over the last decade, there has been a tremendous growth in the amount and diversity of electronic health-related data, such as patient records, drug information, drug–disease associations, medical resource allocations, and clinical experiments’ results, altogether referred to as medical big data. Health data analytics refers to the proper exploitation of medical big data in view of getting better understandings that can drive health research, which may ultimately accelerate advancements in biomedicine, enhance patient outcomes, and reduce overall healthcare costs. This chapter provides an extensive review of the application areas that can benefit from health data analytics, namely drug–disease association, disease outbreak detection and surveillance, pharmacovigilance, healthcare management, clinical research, and clinical practice. A variety of tools and platforms have been developed to support health data analytics, each dealing with different application areas and diverse data types. These tools are analyzed. The challenges and future directions of health data analytics are also discussed.

Microbial genome-wide association studies (mGWAS) are a new and exciting research field that is adapting human GWAS methods to understand how variations in microbial genomes affect host or pathogen phenotypes, such as drug resistance, virulence, host specificity and prognosis. Several computational tools and methods have been developed or adapted from human GWAS to facilitate the discovery of novel mutations and structural variations that are associated with the phenotypes of interest. However, no comprehensive, end-to-end, user-friendly tool is currently available. The development of a broadly applicable pipeline presents a real opportunity among computational biologists. Here, (i) we review the prominent and promising tools, (ii) discuss analytical pitfalls and bottlenecks in mGWAS, (iii) provide insights into the selection of appropriate tools, (iv) highlight the gaps that still need to be filled and how users and developers can work together to overcome these bottlenecks. Use of mGWAS research can inform drug repositioning decisions as well as accelerate the discovery and development of more effective vaccines and antimicrobials for pressing infectious diseases of global health significance, such as HIV, TB, influenza, and malaria.

Although prostate cancer is the leading cause of cancer mortality for African men, the vast majority of known disease associations have been detected in European study cohorts. Furthermore, most genome-wide association studies have used genotyping arrays that are hindered by SNP ascertainment bias. To overcome these disparities in genomic medicine, the Men of African Descent and Carcinoma of the Prostate (MADCaP) Network has developed a genotyping array that is optimized for African populations. The MADCaP Array contains more than 1.5 million markers and an imputation backbone that successfully tags over 94% of common genetic variants in African populations. This array also has a high density of markers in genomic regions associated with cancer susceptibility, including 8q24. We assessed the effectiveness of the MADCaP Array by genotyping 399 prostate cancer cases and 403 controls from seven urban study sites in sub-Saharan Africa. Samples from Ghana and Nigeria clustered together, whereas samples from Senegal and South Africa yielded distinct ancestry clusters. Using the MADCaP array, we identified cancer-associated loci that have large allele frequency differences across African populations. Polygenic risk scores for prostate cancer were higher in Nigeria than in Senegal. In summary, individual and population-level differences in prostate cancer risk were revealed using a novel genotyping array. Significance: This study presents an Africa-specific genotyping array, which enables investigators to identify novel disease associations and to fine-map genetic loci that are associated with prostate and other cancers.

Despite some notable progress in data sharing policies and practices, restrictions are still often placed on the open and unconditional use of various genomic data after they have received official approval for release to the public domain or to public databases. These restrictions, which often conflict with the terms and conditions of the funding bodies who supported the release of those data for the benefit of the scientific community and society, are perpetuated by the lack of clear guiding rules for data usage. Existing guidelines for data released to the public domain recognize but fail to resolve tensions between the importance of free and unconditional use of these data and the “right” of the data producers to the first publication. This self-contradiction has resulted in a loophole that allows different interpretations and a continuous debate between data producers and data users on the use of public data. We argue that the publicly available data should be treated as open data, a shared resource with unrestricted use for analysis, interpretation, and publication.

Background: While healthcare systems are investing resources on type 2 diabetes patients, self-management is becoming the new trend for these patients. Due to the pervasiveness of computing devices, a number of computerized systems are emerging to support the self-management of patients.Objective: The primary objective of this review is to identify and categorize the computational tools that exist for the self-management of type 2 diabetes, and to identify challenges that need to be addressed.Results: The tools have been categorized into web applications, mobile applications, games and ubiquitous diabetes management systems. We provide a detailed description of the salient features of each category along with a comparison of the various tools, listing their challenges and practical implications. A list of platforms that can be used to develop new tools for the self-management of type 2 diabetes, namely mobile applications development, sensor development, cloud computing, social media, and machine learning and predictive analysis platforms, are also provided.Discussions: This paper identifies a number of challenges in the existing categories of computational tools and consequently presents possible avenues for future research. Failure to address these issues will negatively impact on the adoption rate of the self-management tools and applications.

With the expansion of biological data sources available online, integration is a major challenge facing researchers wishing to explore this information. Users often need to integrate data derived from multiple, diverse and heterogeneous sources for investigation. This paper presents the features of Infect-DB, a data warehouse that can localize and integrate genomes of pathogenic species, retrieved from NCBI, based on information from the American Biological Safety Association (ABSA). The list of bacteria and their corresponding host specificity were programmatically accessed from ABSA and integrated into Infect-DB. The list of organisms obtained from ABSA was used to target the automated download of corresponding genomes from the NCBI FTP site. Infect-DB provides a set of analysis tools, including a comparison of genomes using local-BLAST, dN/dS analysis, multiple sequence alignment, phylogenetic analysis and visualization tools. To date, Infect-DB has integrated 854 bacterial genomes from 207 genera considered as important pathogens causing infectious diseases.

International audience; Genomics is the study of the genetic material that constitutes the genomes of organisms. This genetic material can be sequenced and it provides a powerful tool for the study of human, plant and animal evolutionary history and diseases. Genomics research is becoming increasingly commonplace due to significant advances in and reducing costs of technologies such as sequencing. This has led to new challenges including increasing cost and complexity of data. There is, therefore, an increasing need for computing infrastructure and skills to manage, store, analyze and interpret the data. In addition, there is a significant cost associated with recruitment of participants and collection and processing of biological samples, particularly for large human genetics studies on specific diseases. As a result, researchers are often reluctant to share the data due to the effort and associated cost. In Africa, where researchers are most commonly at the study recruitment, determination of phenotypes and collection of biological samples end of the genomic research spectrum, rather than the generation of genomic data, data sharing without adequate safeguards for the interests of the primary data generators is a concern. There are substantial ethical considerations in the sharing of human genomics data. The broad consent for data sharing preferred by genomics researchers and funders does not necessarily align with the expectations of researchers, research participants, legal authorities and bioethicists. In Africa, this is complicated by concerns about comprehension of genomics research studies, quality of research ethics reviews and understanding of the implications of broad consent, secondary analyses of shared data, return of results and incidental findings. Additional challenges with genomics research in Africa include the inability to transfer, store, process and analyze large-scale genomics data on the continent, because this requires highly specialized skills and expensive computing infrastructure which are often unavailable. Recently initiatives such as H3Africa and H3ABioNet which aim to build capacity for large-scale genomics projects in Africa have emerged. Here we describe such initiatives, including the challenges faced in the generation, analysis and sharing of genomic data and how these challenges are being overcome.

ObjectiveThis project aimed to develop and propose a standardised reporting guideline for kidney disease research and clinical data reporting, in order to improve kidney disease data quality and integrity, and combat challenges associated with the management and challenges of ‘Big Data’.MethodsA list of recommendations was proposed for the reporting guideline based on the systematic review and consolidation of previously published data collection and reporting standards, including PhenX measures and Minimal Information about a Proteomics Experiment (MIAPE). Thereafter, these recommendations were reviewed by domain-specialists using an online survey, developed in Research Electronic Data Capture (REDCap). Following interpretation and consolidation of the survey results, the recommendations were mapped to existing ontologies using Zooma, Ontology Lookup Service and the Bioportal search engine. Additionally, an associated eXtensible Markup Language schema was created for the REDCap implementation to increase user friendliness and adoption.ResultsThe online survey was completed by 53 respondents; the majority of respondents were dual clinician-researchers (57%), based in Australia (35%), Africa (33%) and North America (22%). Data elements within the reporting standard were identified as participant-level, study-level and experiment-level information, further subdivided into essential or optional information.ConclusionThe reporting guideline is readily employable for kidney disease research projects, and also adaptable for clinical utility. The adoption of the reporting guideline in kidney disease research can increase data quality and the value for long-term preservation, ensuring researchers gain the maximum benefit from their collected and generated data.

The need for portable and reproducible genomics analysis pipelines is growing globally as well as in Africa, especially with the growth of collaborative projects like the Human Health and Heredity in Africa Consortium (H3Africa). The Pan-African H3Africa Bioinformatics Network (H3ABioNet) recognized the need for portable, reproducible pipelines adapted to heterogeneous computing environments, and for the nurturing of technical expertise in workflow languages and containerization technologies. Building on the network’s Standard Operating Procedures (SOPs) for common genomic analyses, H3ABioNet arranged its first Cloud Computing and Reproducible Workflows Hackathon in 2016, with the purpose of translating those SOPs into analysis pipelines able to run on heterogeneous computing environments and meeting the needs of H3Africa research projects. This paper describes the preparations for this hackathon and reflects upon the lessons learned about its impact on building the technical and scientific expertise of African researchers. The workflows developed were made publicly available in GitHub repositories and deposited as container images on Quay.io.

Nicol et al. make insightful comments on issues related to data sharing and ethics, regulations, and imbalance of power. We largely share their concerns, and we did indeed mention some of them briefly in our Policy Forum. Our discussion pertained to all types of data, many or most of which do not include any personal information or do not involve individuals or humans in any way. For example, environmental genomics data generated through public funding should become available shortly after generation and should enjoy unrestricted usage. As we acknowledged in the Policy Forum, when it comes to personal data, issues of privacy, confidentiality, and informed consent need to be considered carefully and the best arrangements may vary on a case-by-case basis. It makes sense to anticipate these issues before data collection begins and to aim for research designs and informed consent forms that allow maximal, prompt, open use of valuable data. We agree with Nicol et al. that public release of data may affect distribution of burdens and benefits across and within populations, and this is something that should be closely monitored. However, we think that usually more openness would diminish the inadvertent concentration of informational power in the hands of select for-profit enterprises that may wish to hoard data for their own advantage. Conversely, the public release of data may offer more value for the public and more widely distributed benefits of science. The list of author affiliations is available at [www.sciencemag.org/content/363/6425/350/suppl/DC1][1]. [1]: http://www.sciencemag.org/content/363/6425/350/suppl/DC1

Summary BioPAXViz is a Cytoscape (version 3) application, providing a comprehensive framework for metabolic pathway visualization. Beyond the basic parsing, viewing and browsing roles, the main novel function that BioPAXViz provides is a visual comparative analysis of metabolic pathway topologies across pre-computed pathway phylogenomic profiles given a species phylogeny. Furthermore, BioPAXViz supports the display of hierarchical trees that allow efficient navigation through sets of variants of a single reference pathway. Thus, BioPAXViz can significantly facilitate, and contribute to, the study of metabolic pathway evolution and engineering. Availability and Implementation BioPAXViz has been developed as a Cytoscape app and is available at: https://github.com/CGU-CERTH/BioPAX.Viz. The software is distributed under the MIT License and is accompanied by example files and data. Additional documentation is available at the aforementioned GitHub repository.

International audience; The application of genomics technologies to medicine and biomedical research is increasing in popularity, made possible by new high-throughput genotyping and sequencing technologies and improved data analysis capabilities. Some of the greatest genetic diversity among humans, animals, plants, and microbiota occurs in Africa, yet genomic research outputs from the continent are limited. The Human Heredity and Health in Africa (H3Africa) initiative was established to drive the development of genomic research for human health in Africa, and through recognition of the critical role of bioinformatics in this process, spurred the establishment of H3ABioNet, a pan-African bioinformatics network for H3Africa. The limitations in bioinformatics capacity on the continent have been a major contributory factor to the lack of notable outputs in high-throughput biology research. Although pockets of high-quality bioinformatics teams have existed previously, the majority of research institutions lack experienced faculty who can train and supervise bioinformatics students. H3ABioNet aims to address this dire need, specifically in the area of human genetics and genomics, but knock-on effects are ensuring this extends to other areas of bioinformatics. Here, we describe the emergence of genomics research and the development of bioinformatics in Africa through H3ABioNet.

In the context of recent international initiatives to bolster genomics research for Africa, and more specifically to develop bioinformatics expertise and networks across the continent, a workshop on computational metagenomics was organized during the end of 2014 at the University of Mauritius. The workshop offered background on various aspects of computational biology, including databases and algorithms, sequence analysis fundamentals, metagenomics concepts and tools, practical exercises, journal club activities and research seminars. We have discovered a strong interest in metagenomics research across Africa, to advance practical applications both for human health and the environment. We have also realized the great potential to develop genomics and bioinformatics through collaborative efforts across the continent, and the need for further reinforcing the untapped human potential and exploring the natural resources for stronger engagement of local scientific communities, with a view to contributing towards the improvement of human health and well-being for the citizens of Africa.

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, H3ABioNet’s 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.HighlightsH3ABioNet is building capacity to enable analysis of genomic data in Africa.Infrastructure has been built for clinical and genomic data storage, management, and analysis.New algorithms and pipelines for African genomic data analysis have been developed.Data are being harmonized using ontologies to enable easy search and retrieval.Genomics training is implemented using various online and face-to-face approaches.

Over the past decade, researchers have discovered that apart from the essential genes, bacterial genomes also contain a variable amount of accessory genes acquired by horizontal gene transfer (HGT) that are categorized as genomic islands (GIs). GIs encode adaptive traits, which might be beneficial for the species under certain growth or environmental conditions. It has always been a challenge for biologists to identify GIs within a bacterial genome as they evolve very rapidly. This paper proposes a standalone software, IslanHunter, that has been developed using Java and BioJava and can extract GI regions using GC content, codon usage bias, dinucleotide frequency bias, tetranucleotide frequency bias, k-mer signature analysis (2-mer, 3-mer, 4-mer, 5-mer, and 6-mer) and presence of mobility genes. IslandHunter provides a simple graphical user interface where disclosed GIs are displayed in a tree-view and a circular graph. Users are presented with options to save the GI regions as blocks of DNA sequences in FASTA format. They can later use these predicted GI regions for further analysis. IslandHunter can take as input, files in GenBank, EMBL or FASTA formats. IslandHunter provides flexible display options and save options. The software has been evaluated against exiting tools with good performance. It is available for evaluation at https://github.com/ShakunBaichoo/IslandHunter .

The volume of the data involved in healthcare systems and the sensitivity of the data call for strict, non-obtrusive and efficient access control. This paper presents the design and implementation of a software prototype to demonstrate how Role-Based Access Control (RBAC), supported by context-awareness, can be applied in the Mauritian healthcare service for providing efficient and effective access control to patient's data. The work has consisted of studying different models of Role-Based and Context-Based access control used elsewhere and applying it to the Mauritian healthcare sector. The software prototype is based on information flow in a collaborator healthcare institution. The prototype has been implemented as a distributed system based on the client-server model, with the location of users and time of access being forms of context considered. The prototype has been successfully implemented and tested under different scenarios of data access.