RareInsight simplifies the communication of genetic results for rare disease patients

Abstract

Following a confirmed genetic diagnosis, rare disease patients and their families encounter significant challenges in accessing diagnostic information and support. Patients and non-specialists are increasingly expected to interpret and share test results; however, existing standards are primarily designed for specialists. These standards fail to address the needs of resource-limited populations where low genomic literacy hampers accurate dissemination of genetic results. This research introduces RareInsight, an open-source, interactive dashboard designed to enhance the accessibility, comprehension, and collaboration of genetic data among patients, caregivers, clinicians, and researchers. Developed using shinydashboard, RareInsight was evaluated using whole exome sequencing data from skeletal dysplasia patients. It allows users to input and view Variant Call Format files and includes a searchable ClinVar variant table with filtering options, providing access to multiple resources based on search terms. RareInsight aims to simplify the dissemination of complex genetic information beyond the clinical setting. This dashboard serves as a pilot study demonstrating the potential of patient-centered interactive dashboards for the rare disease community.

Introduction

Individuals with rare diseases (RDs) in Sub-Saharan Africa encounter significant obstacles in accessing relevant information due to the limited availability of specialized healthcare providers, low awareness and genomic literacy regarding their conditions, and delays in obtaining accurate diagnoses^1,2. These disparities can be attributed to differences in research priorities between high-income and low-to-middle-income countries^3,4. To overcome these challenges, it is essential to improve data documentation, foster collaborative research efforts, and ensure accurate information dissemination tailored to diverse audiences, particularly as patients and non-specialist healthcare providers are increasingly tasked with understanding and interpreting these results.

Following a successful diagnosis, patients may struggle to access resources and support tailored to their diagnosis due to limited distribution channels, a lack of patient education on effective information utilization, and inadequate awareness of available support services^5,6. Although genetic counselors and geneticists play a key role in disseminating information, patients often navigate this information independently, underscoring the need for supplementary support systems. According to a recent qualitative analysis⁷, parents consistently sought additional information and resources for their child’s diagnosis as existing reports did not meet their needs, leading them to conduct extensive online searches.

Tailored educational resources customized to patients’ specific needs can improve their health literacy, enabling them to make well-informed decisions about their care⁶. Providing detailed information about rare diseases, including symptoms, treatment options, potential complications, and support services, can empower patients in their care decisions^8,9. Improving health literacy is vital for individuals with rare diseases as it empowers them to comprehend medical information and take control of their health¹⁰.

Researchers and clinicians have access to a variety of dashboards such as DECIPHER (https://www.deciphergenomics.org/)¹¹, ClinGen (https://clinicalgenome.org/)¹², ClinVar Miner (https://clinvarminer.genetics.utah.edu/)¹³, Franklin (https://franklin.genoox.com/), and MyGene2 (https://mygene2.org/MyGene2/), which play a crucial role in facilitating the interpretation of genomic information for clinical and research purposes. These dashboards share a common function in facilitating the interpretation of genomic information for clinical and research purposes, thereby contributing to improved patient care and research outcomes. While accessible to patients, they are primarily curated for clinicians and researchers. Significantly, the absence of research evaluating the usability and impact of dashboards tailored for patients, caregivers, and non-specialist healthcare providers raises concerns about the effectiveness and accessibility of these tools in improving healthcare outcomes. Tailored dashboards for RD patients could offer numerous advantages, including enhanced accessibility to research findings and support services, potentially leading to improved patient care and outcomes¹⁴.

Drawing on existing research and future trends in healthcare technology, we introduce RareInsight (https://github.com/omicscodeathon/rareinsight), an open-source, interactive dashboard aimed at enhancing the interpretation of genetic variant data through a user-friendly interface. RareInsight offers personalized genetic insights, essential resources, and support to aid users in accessing relevant genetic information efficiently. RareInsight is primarily designed to create customized resources based on precise search terms associated with genetic variants, disorders, and genes, aiding users in efficiently accessing pertinent genetic information.

Methods

Data access and download

Whole exome sequencing (WES) data were obtained from the European Nucleotide Archive (ENA) browser with the accession PRJNA747169 (https://www.ebi.ac.uk/ena/browser/view/PRJNA747169). This data was originally used for the bioinformatics analysis of fetal skeletal dysplasia detected by ultrasonography of 38 cases. The original authors identified 22 pathogenic variants, and 7 likely pathogenic variants directly associated with structural disorders (SDs), achieving a diagnostic yield of 65.79% (25 out of 38 cases). Additionally, four variants of uncertain significance (VUS) were detected in 4 fetuses.

For the purpose of developing and testing RareInsight, only the 25 cases with pathogenic or likely pathogenic variants were included in this study (cases 1–25), as they represent instances with a confirmed molecular diagnosis (see Supplementary Table S1 online). This selective inclusion was intended to validate the dashboard’s ability to interpret well-characterized variants.

Variant calling and file preparation

Variant calling was performed using the nf-core/raredisease pipeline (https://github.com/nf-core/raredisease/2.0.0) (see Supplementary Method S1 online). This pipeline adheres to best-practice workflows for germline variant detection, annotation, and quality control and is designed for reproducible rare disease analysis. The output VCF files were used as proof-of-concept for the dashboard. It should be noted that R has a maximum file size limit of 5 MB per file, however, annotated VCF files tend to be large (> 50 MB). Therefore, we developed additional Python scripts to filter the VCF files using a virtual skeletal dysplasia gene panel and extract only clinically relevant information such as clinical significance and diagnosis. Users can upload VCF files up to 60 MB to the dashboard interface.

Dashboard development and deployment

The RareInsight dashboard was built using the R programming language (v4.3.2) with Shiny (v1.8.0) and shinydashboard (v0.7.2) packages. The source code is hosted on GitHub at https://github.com/omicscodeathon/rareinsight and can be cloned and launched via RStudio (v 2024.04.1 + 748). Once launched, the dashboard automatically retrieves the most recent ClinVar variant summary file from the NCBI FTP server (https://ftp.ncbi.nlm.nih.gov/pub/clinvar/tab_delimited/variant_summary.txt.gz). This file is unzipped and loaded into the R environment for custom querying. This initial download may take several minutes, and the dashboard features become fully functional only after completion of this step.

Dashboard features and functionalities

RareInsight was designed to serve both clinical and non-specialist users through a friendly user interface with the following sections:

1. Home Introduction to RareInsight and its purpose.

2. Tutorial

3. Glossary Definitions of common genetic terms.

4. Basic Genetics Brief overview of foundational genetics concepts.

5. Rare diseases Summary of important rare disease principles.

Services (see Fig. 1 for interface layout):

[See PDF for image]

Fig. 1

Layout of the Services panel. This panel is used to input user information, view and filter VCF files, search for information from ClinVar and access relevant links to various resources.

Input user information optional input of relevant patient metadata.

VCF panel Upload and view annotated VCF files along with quality metrics.

Search panel Search and filter ClinVar variants by gene, disorder, accession, or variant type.

Diagnostic report User-specific resources provided based on the Search Panel input

Acknowledgements Recognition of contributors, collaborators, and funding bodies involved.

Results and discussion

RareInsight is an interactive dashboard designed to support the dissemination and interpretation of genetic test results for clinicians, researchers, and patients, including their caregivers. The dashboard provides tailored educational content, variant visualization, and curated dynamic links to enhance patient understanding and clinical communication of genetic test results. By integrating clinical genetics with patient support pathways, RareInsight reduces the need for time-consuming independent searches and empowers users to better understand and disseminate genetic test results.

Dashboard features and functionality

The dashboard comprises four main pages: Home, Tutorial, Services, and Acknowledgements. The Home page (Fig. 2) outlines the aims and background of the RareInsight project. It features a video demonstration that guides new users through the dashboard’s functions. Upon launching, the dashboard automatically downloads and unzips the latest ClinVar variant summary file (~ 2 GB), a process that can take up to five minutes. If the file is already present locally, this step is skipped in subsequent launches. An alert message appears upon launching. Users must select between clinician/researcher and patient/caregiver. If the latter is selected, an additional alert will appear requiring the user to click on a checkbox stating the dashboard’s educational use only before the pop-up disappears.

[See PDF for image]

Fig. 2

The layout and homepage of RareInsight. This is the first page that users will see when the dashboard is launched. It contains information about the project and what it aims to achieve. There is also a video demonstration of the dashboard in use. A pop-up indicates if the ClinVar variant summary file is being downloaded and unzipped.

The Understanding Your Genetics panel (Fig. 3) addresses a critical need for educational resources tailored to non-specialists. It aims to help users with no clinical or genetic background understand the basics behind genetics and what it means to have a rare disease. It includes three tabs:

• Glossary Defines 17 commonly used genetics terms that patients and families may encounter on their diagnostic odyssey, with an external link to the full NHS England Genomics glossary (https://www.genomicseducation.hee.nhs.uk/glossary/).

• Basic genetics Introduces core genetic principles, adapted from the NHS England Genomics Education Programme (https://www.genomicseducation.hee.nhs.uk/education/core-concepts/what-is-genomics/).

• Rare diseases Explains how rare diseases are caused, types of genetic variants, and inheritance patterns.

[See PDF for image]

Fig. 3

Understanding your genetics. This tutorial is divided into three tabs to give non-specialist users a basic overview of genetics and rare diseases.

Users can download each tab’s information as an HTML file, facilitating seamless sharing of information and knowledge beyond RareInsight’s users.

The Services panel contains four main functional components. Input User Information (Fig. 4) allows users to input relevant phenotypic and diagnostic information. General information can also be added, such as name, surname, ethnicity, date of birth, and the type of genetic test that was performed. These inputs can be exported as a PDF.

[See PDF for image]

Fig. 4

Input user information. This tab allows users to input relevant information about the patient’s diagnosis and phenotype.

The VCF Panel (Fig. 5) enables users to upload, view and filter VCF files in a table format, with accompanying quality control plots. This function was validated using the WES results from 25 diagnosed cases of fetal skeletal dysplasia. The causative variants were identified by filtering a patient’s VCF file. Currently, this panel can take unannotated and annotated VCF files from Annovar, Variant Effect Predictor (VEP), and SnpEff. In theory, the dashboard would be able to use both whole exome sequencing (WES) and whole genome sequencing (WGS) data as its input; however, this was not assessed in the present study. It is important to note that since patients typically do not get access to their VCF file, the VCF panel will likely be used by a clinician or researcher to easily view and filter the file. Additional unannotated VCF files have been made available for testing and can be accessed from the GitHub repository (https://github.com/omicscodeathon/rareinsight).

[See PDF for image]

Fig. 5

VCF panel view. The Results tab shows a visual representation of the input VCF file in a tabular format. The Graphs tab displays quality control metric for the VCF file.

The Search Panel (Fig. 6) facilitates targeted searches of the ClinVar variant database using gene names, variants, disorders, dbSNP or ClinVar accessions. Users can filter further based on pathogenicity, variant type, and clinical significance. These searches create dynamic links which are made available in the Diagnostic Report panel.

[See PDF for image]

Fig. 6

Search panel. This is a view of the ClinVar variant summary file where users can search for variants, disorders, genes, or accessions relevant to a patient’s diagnosis.

The Diagnostic Report panel (Fig. 7) generates dynamic links to external resources (e.g., PubMed, GeneReviews, OMIM, LitVar etc.) based on the search term used in the Search Panel. Each resource is contextualized with descriptions and usage instructions to enhance user experience (see Supplementary Table S2 online). The panel was assessed and validated using the skeletal dysplasia case results by searching for the causative gene, variant, disorder, and ClinVar accession in the Search Panel. Each dynamic link was individually accessed, demonstrating full functionality for known pathogenic and likely pathogenic variants.

[See PDF for image]

Fig. 7

Diagnostic report. This panel allows users to search for links based on the Search Panel’s input. Links are divided by relevance to the user (Clinician and Researcher vs. Patient and caregiver).

To enhance accessibility, an additional tab was included to describe each available resource, with complex concepts simplified for non-specialist users such as patients and caregivers. Similar to the understanding your genetics panel, this tab’s information can be exported as an HTML file.

A tab named, Genetic Resources Guide (Fig. 8) was added. This simplifies the access to resources made available in the Diagnostic Report panel.

[See PDF for image]

Fig. 8

Genetic resources guide.

Unique features and impact

While several variant interpretation dashboards exist, RareInsight distinguishes itself by emphasizing patient and caregiver accessibility¹⁵ as well as emphasizing genetic variation as opposed to differential gene expression^{16, 17, 18, 19–20}. Most available tools cater exclusively to clinical or research professionals, often overlooking the needs of non-specialists²¹. RareInsight addresses this by integrating clinical data with patient advocacy, offering dynamic links to disease-specific resources and support organizations (see Supplementary Table S3 online). This places it in a unique position to empower patients and their support networks to interpret genetic findings more confidently and make informed decisions. While tools like Franklin and ClinGen are designed with clinicians in mind, RareInsight fills a critical gap by providing personalized support for those affected by rare diseases, particularly in under-resourced regions^{22, 23, 24–25}.

This research highlighted a distinct lack of informational resources and support for patients residing in Sub-Saharan Africa where drug development for complex diseases is generally challenging^{26, 27, 28–29}. An interesting initiative that has recently been developed to alleviate this issue is the South African Rare Diseases Access Initiative (RDAI)³⁰. Their work aims to prioritize and improve diagnosis, access to treatments and services, data collection and management, coordinated care, and collaborative research. This initiative is important since the lack of data and resources on rare disease research has a direct negative impact on diagnosis, patient care, support and improving outcomes to name a few. A goal for RDAI therefore calls for a national framework that connects patients with relevant patient support organizations. This is of great interest to us as RareInsight aligns closely with the goals of RDAI by offering an open-access tool that can facilitate patient empowerment and information access.

Limitations and future directions

While the current version of RareInsight provides a meaningful step toward patient-focused variant interpretation, several limitations remain. Currently, it only focuses on known variants annotated in ClinVar and related databases. Consequently, novel or poorly annotated variants may yield limited results, particularly in under-represented populations. The current implementation of RareInsight requires users to run the dashboard locally in RStudio, which may present barriers for non-technical users. Funding would be required to deploy the dashboard on a server such as Posit Connect or shinyapps.io as these options are not freely available for long-term use. RareInsight acknowledges the risk of misinterpretation when non-experts engage with complex genomic data. To address this, the dashboard features a basic tutorial to explain key concepts in rare diseases along with a link to more in-depth information. Additionally, a disclaimer appears when launching the dashboard to remind users that it is for educational purposes only.

The present study focused only on the development of the dashboard; however, we recommend that future iterations conduct a Knowledge-Attitude-Practices (KAP) survey with potential users (clinicians, researchers, patients and caregivers) before and after using RareInsight (see Supplementary Table S4 online). This will allow for a structured assessment of how the dashboard influences the users’ understanding of rare diseases and variant interpretation, their confidence in engaging with genetic information, and their behaviour in seeking further information or support. Incorporating a KAP framework will provide quantitative and qualitative insights into the platform’s real-world impact and guide further user-centered improvements³¹. Further implementations could also focus on incorporating multilingual support and enabling fully customizable and exportable reports.

Conclusion

In conclusion, this research serves as a pilot study for the development of rare disease dashboards for experts (clinicians and researchers) and non-experts (patients and caregivers). RareInsight demonstrates the potential for integrating clinical variant data with accessible, patient-friendly resources in a single interactive platform. By addressing both technical and educational needs, it supports more informed decision-making and enhances the overall diagnostic journey for patients, caregivers, researchers, and clinicians alike. The development of region-specific content, especially for African populations, remains a high priority for future releases. While the full functionality of RareInsight has not yet been achieved, it is a continuous endeavour and will continue to be improved and updated. Furthermore, we aim to initiate collaborations with relevant organizations to further enhance this dashboard’s functions to enable its implementation in a clinical setting.

Acknowledgements

The authors thank the National Institutes of Health (NIH) Office of Data Science Strategy (ODSS) for their immense support before and during the April 2024 Omics codeathon organized in collaboration with the African Society for Bioinformatics and Computational Biology (ASBCB).

Author contributions

KCC conceived the original idea. KCC and FZ developed the dashboard for the project and performed the bioinformatic analysis of the case study data. KCC contributed to writing, reviewing, and editing the manuscript. EA and GAW contributed to reviewing and editing the manuscript. OIA’s role was in the administration and supervision of the bioinformatics analysis in the project. OIA also provided the resources to facilitate and complete the analysis and provided guidance. OIA did the final review of the manuscript and provided critical feedback that helped shape the final version of the manuscript. All authors read and approved the final manuscript.

Funding

The authors declare that no financial support was received for the research, authorship, and/or publication of this article.

Data availability

The data supporting the results reported in this manuscript is included within the article and its additional files. The project repository, which also includes the source code and requirements for deployment are available in the GitHub repository, https://github.com/omicscodeathon/rareinsight.

Declarations

Competing interests

The authors declare that they have no competing interests.

Consent for publication

Not applicable.

Consent to participate

Not applicable.

Ethical approval

Not applicable.

Publisher’s note

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

References

1. Danese, E; Lippi, G. Rare diseases: The paradox of an emerging challenge. Ann. Transl. Med.; 2018; 6, 329. [DOI: https://dx.doi.org/10.21037/atm.2018.09.04] [PubMed: https://www.ncbi.nlm.nih.gov/pubmed/30306068][PubMedCentral: https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6174191]

2. Zanello, G; Chan, CH; Pearce, DA. Recommendations from the IRDiRC Working Group on methodologies to assess the impact of diagnoses and therapies on rare disease patients. Orphanet J. Rare Dis.; 2022; 17, 181. [DOI: https://dx.doi.org/10.1186/s13023-022-02337-2] [PubMed: https://www.ncbi.nlm.nih.gov/pubmed/35526001][PubMedCentral: https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9078009]

3. Peprah, E; Wonkam, A. Biomedical research is a tool to address the health issues that affect African populations. Glob. Health; 2013; 9, 50. [DOI: https://dx.doi.org/10.1186/1744-8603-9-50]

4. Wesonga, RM; Awe, OI. An assessment of traditional and genomic screening in newborns and their applicability for Africa. Inform. Med. Unlocked; 2022; 32, [DOI: https://dx.doi.org/10.1016/j.imu.2022.101050] 101050.

5. Litzkendorf, S et al. Use and importance of different information sources among patients with rare diseases and their relatives over time: A qualitative study. BMC Public Health; 2020; 20, 860. [DOI: https://dx.doi.org/10.1186/s12889-020-08926-9] [PubMed: https://www.ncbi.nlm.nih.gov/pubmed/32503483][PubMedCentral: https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7275578]

6. Quinn, L; Davis, K; Yee, A; Snyder, H. Understanding genetic learning needs of people affected by rare disease. J. Genet. Couns.; 2020; 29, pp. 1050-1058. [DOI: https://dx.doi.org/10.1002/jgc4.1233] [PubMed: https://www.ncbi.nlm.nih.gov/pubmed/32128950]

7. Stuckey, H et al. Enhancing genomic laboratory reports from the patients’ view: A qualitative analysis. Am. J. Med. Genet. A; 2015; 167, pp. 2238-2243. [DOI: https://dx.doi.org/10.1002/ajmg.a.37174] [PubMedCentral: https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4744953]

8. Lopez Gousset, V. & Bolz-Johnson, M. Common needs of the rare disease population identified globally. Rare Dis. Int. (2021).

9. Shukla, DP; Cutshall, JO; van der Heijden, L; Schindewolf, E; Sheppard, SE. The importance of patient-specific resources for families dealing with prenatal rare diseases. Am. J. Med. Genet. A; 2024; 194, [DOI: https://dx.doi.org/10.1002/ajmg.a.63450] [PubMed: https://www.ncbi.nlm.nih.gov/pubmed/37861066]e63450.

10. Pauer, F et al. Rare diseases on the internet: An assessment of the quality of online information. J. Med. Internet Res.; 2017; 19, 2017pfgd.book...P [DOI: https://dx.doi.org/10.2196/jmir.7056] [PubMed: https://www.ncbi.nlm.nih.gov/pubmed/28100442][PubMedCentral: https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5288561]e23.

11. Firth, HV et al. DECIPHER: Database of chromosomal imbalance and phenotype in humans using Ensembl resources. Am. J. Hum. Genet.; 2009; 84, pp. 524-533. [DOI: https://dx.doi.org/10.1016/j.ajhg.2009.03.010] [PubMed: https://www.ncbi.nlm.nih.gov/pubmed/19344873][PubMedCentral: https://www.ncbi.nlm.nih.gov/pmc/articles/PMC2667985]1:CAS:528:DC%2BD1MXmvVahsLk%3D

12. Rehm, HL et al. ClinGen: The clinical genome resource. N. Engl. J. Med.; 2015; 372, pp. 2235-2242. [DOI: https://dx.doi.org/10.1056/NEJMsr1406261] [PubMed: https://www.ncbi.nlm.nih.gov/pubmed/26014595][PubMedCentral: https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4474187]1:CAS:528:DC%2BC2MXhtFyrsbfI

13. Henrie, A et al. ClinVar Miner: Demonstrating utility of a web-based tool for viewing and filtering ClinVar data. Hum. Mutat.; 2018; 39, pp. 1051-1060. [DOI: https://dx.doi.org/10.1002/humu.23555] [PubMed: https://www.ncbi.nlm.nih.gov/pubmed/29790234]

14. Leutner, LA; Bathelt, F; Sedlmayr, B; Sedlmayr, M; Zoch, M. Development of a dashboard for rare diseases—A technical case report. Stud. Health Technol. Inform.; 2021; 283, pp. 78-85. [PubMed: https://www.ncbi.nlm.nih.gov/pubmed/34545822]

15. Ben Aribi, H; Dixon, I; Abassi, N; Awe, OI. Efficient and easy gene expression and genetic variation data analysis and visualization using exvar. Sci. Rep.; 2025; 15, 93067. [DOI: https://dx.doi.org/10.1038/s41598-025-93067-5] 1:CAS:528:DC%2BB2MXpt1Cgu7w%3D

16. Chikwambi, Z., Hidjo, M., Chikondowa, P., Afolabi, L., Aketch, V., Jayeoba, G., Enoma, D.O. and Awe, O.I. Multi-omics data integration approach identifies potential biomarkers for prostate cancer. bioRxiv. https://doi.org/10.1101/2023.01.26.522643 (2023).

17. Nzungize, L., Kengne-Ouafo, J.A., Wesonga, M.R., Umuhoza, D., Murithi, K., Kimani, P., Awe, O.I. and Dillman, A. Transcriptional profiles analysis of COVID-19 and malaria patients reveals potential biomarkers in children. bioRxiv. https://doi.org/10.1101/2022.06.30.498338 (2022).

18. El Abed, F., Baraket, G., Nyamari, M.N., Naitore, C., and Awe, O.I. Differential expression analysis of miRNAs and mRNAs in epilepsy uncovers potential biomarkers. bioRxiv. https://doi.org/10.1101/2023.09.11.557132 (2023).

19. Nyamari, M.N., Omar, K.M., Fayehun A.F., Dachi, O., Bwana, B.K. and Awe, O.I. Expression level analysis of ACE2 receptor gene in African-American and Non-African-American COVID-19 Patients. bioRxiv. https://doi.org/10.1101/2023.09.11.557129 (2023).

20. Alaya, F., Baraket, G., Adediran, D.A., Cuttler, K., Ajiboye, I., Kivumbi, M.T., Sitharam, N. and Awe, O.I. Multiple sclerosis stages and their differentially expressed genes: A bioinformatics analysis. bioRxiv. https://doi.org/10.1101/2024.01.20.576448 (2024).

21. Ben Aribi, H; Abassi, N; Awe, OI. NeuroVar: An open-source tool for the visualization of gene expression and variation data for biomarkers of neurological diseases. Gigabyte; 2024; 2024, 143. [DOI: https://dx.doi.org/10.46471/gigabyte.143]

22. Awe, OI; En Najih, N; Nyamari, MN; Mukanga, LB. Comparative study between molecular and genetic evolutionary analysis tools using African SARS-CoV-2 variants. Inform. Med. Unlocked; 2023; 33, [DOI: https://dx.doi.org/10.1016/j.imu.2022.101143] 101143.

23. Obura, H.O. et al. Molecular phylogenetics of HIV-1 subtypes in African populations: A case study of Sub-Saharan African countries. bioRxiv. https://doi.org/10.1101/2022.05.18.492401 (2022).

24. Ather, S.H., Awe, O.I., Butler, T.J., Denka, T., Semick, S.A., Tang, W. Busby B. SeqAcademy: An educational pipeline for RNA-Seq and ChIP-Seq analysis. F1000Research. https://doi.org/10.12688/f1000research.14880.4 (2018).

25. Mwanga, M.J., Obura, H.O., Evans, M. and Awe, O.I. Enhanced deep convolutional neural network for SARS-CoV-2 variants classification. bioRxiv. https://doi.org/10.1101/2023.08.09.552643 (2023).

26. Gakpey, MEL; Aidoo, SA; Jumah, T; Msipa, S; Mbaoji, F; Omonijo, B; Tjale, P; Sangare, M; Tebourbi, H; Hanson, G; Awe, OI. Targeting aldose reductase using natural African compounds as promising agents for managing diabetic complications. Front. Bioinf.; 2025; [DOI: https://dx.doi.org/10.3389/fbinf.2025.1499255]

27. Ogbodo, UC; Enejoh, OA; Okonkwo, CH; Gnanasekar, P; Gachanja, PW; Osata, S; Atanda, HC; Iwuchukwu, EA; Achilonu, I; Awe, OI. Computational identification of potential inhibitors targeting cdk1 in colorectal cancer. Front. Chem.; 2023; [DOI: https://dx.doi.org/10.3389/fchem.2023.1264808] [PubMed: https://www.ncbi.nlm.nih.gov/pubmed/38099190][PubMedCentral: https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10720044]

28. Enejoh, OA; Okonkwo, CH; Nortey, H; Kemiki, OA; Ainembabazi, M; Mbaoji, FN; Yusuf, AS; Awe, OI. Machine learning and molecular dynamics simulations predict potential TGR5 agonists for type 2 diabetes treatment. Front. Chem.; 2025; [DOI: https://dx.doi.org/10.3389/fchem.2024.1503593] [PubMed: https://www.ncbi.nlm.nih.gov/pubmed/39850718][PubMedCentral: https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11754275]

29. Hanson, G; Adams, J; Kepgang, DIB; Zondagh, LS; Bueh, LT; Asante, A; Shirolkar, SA; Kisaakye, M; Bondarwad, H; Awe, OI. Machine learning and molecular docking prediction of potential inhibitors against dengue virus. Front. Chem.; 2024; [DOI: https://dx.doi.org/10.3389/fchem.2024.1510029] [PubMed: https://www.ncbi.nlm.nih.gov/pubmed/39776767][PubMedCentral: https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11703810]

30. Malherbe, HL. Introducing the South African rare diseases access initiative. S. Afr. Med. J.; 2023; 113, 8. [DOI: https://dx.doi.org/10.7196/SAMJ.2023.v113i8.1142] [PubMed: https://www.ncbi.nlm.nih.gov/pubmed/37882112]1:STN:280:DC%2BB2sbivVOisQ%3D%3D

31. Andrade, C; Menon, V; Ameen, S; Kumar Praharaj, S. Designing and conducting knowledge, attitude, and practice surveys in psychiatry: Practical guidance. Indian J. Psychol. Med.; 2020; 42, pp. 478-481. [DOI: https://dx.doi.org/10.1177/0253717620946111] [PubMed: https://www.ncbi.nlm.nih.gov/pubmed/33414597][PubMedCentral: https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7750837]