Genomic Data Science: Career Paths and Essential Skills

Genomic data science sits at the intersection of molecular biology, statistics, and computer science. It involves the analysis, interpretation, and management of large-scale genomic data generated by technologies such as DNA sequencing. For students, researchers, and life-science professionals considering this field, the career paths are diverse and the required skills span multiple disciplines. This article provides an overview of the main career roles, the competencies each demands, and the practical steps you can take to enter and advance in this field.

The Scope of Genomic Data Science

Genomic data science is a specialized branch of data science focused on genomic information. The field has grown rapidly because the cost of sequencing has fallen while the volume of data produced has exploded. Over the past 20 years, the collection of genomic data has expanded dramatically, and cloud computing has made computational analysis accessible to anyone with a web browser and an internet connection. However, access to training and professional development opportunities remains uneven, particularly at institutions with limited resources. Community colleges, historically Black colleges and universities, Hispanic-serving institutions, and tribal colleges and universities often have students who are underrepresented in genomics research and who have had relatively little exposure to genomic data science curricula. Efforts such as the Genomic Data Science Community Network aim to broaden participation by expanding access to infrastructure and data, developing modular open-source resources, and supporting faculty development at these institutions.

The work itself involves handling data from sources such as whole-genome sequencing, RNA sequencing, epigenomics, and metagenomics. Professionals in this field develop and apply computational methods to answer biological and clinical questions. They might identify genetic variants associated with disease, characterize gene expression patterns, or build predictive models for patient outcomes. The field also intersects with emerging technologies such as artificial intelligence, which is being applied to biomarker discovery in oncology and other areas.

Core Career Roles in Genomic Data Science

The genomic data science workforce includes several distinct roles, each with different responsibilities, required skills, and typical career trajectories. Understanding these roles helps you identify where your interests and abilities fit best.

Bioinformatics Analyst

The bioinformatics analyst is often the entry-level role in genomic data science. Analysts process and analyze genomic data using established pipelines and tools. They perform quality control on sequencing data, align reads to reference genomes, call variants, and generate reports for researchers or clinicians. The role requires proficiency in command-line tools, scripting languages such as Python or R, and familiarity with public databases.

Analysts typically work under the supervision of a bioinformatics scientist or principal investigator. They need strong attention to detail because errors in data processing can propagate through downstream analyses. Communication skills matter because analysts must explain their methods and results to colleagues who may not have computational backgrounds.

Genomic Data Scientist

The genomic data scientist role involves more advanced statistical modeling and machine learning. These professionals develop new analytical methods, build predictive models, and integrate multiple data types such as genomics, transcriptomics, and clinical data. They often work on research questions that require custom analysis instead of applying existing pipelines.

A genomic data scientist needs a deeper foundation in statistics and computer science than an analyst. They should understand experimental design, hypothesis testing, and the assumptions underlying different statistical models. Machine learning skills are increasingly important, particularly for analyzing high-dimensional data and integrating multimodal datasets.

Computational Biologist

The computational biologist role sits closer to the biology side of the field. These professionals use computational methods to answer specific biological questions. They might study gene regulation, evolutionary relationships, or the molecular basis of disease. The role requires a strong understanding of biology alongside computational skills.

Computational biologists often collaborate closely with experimental scientists. They help design experiments, analyze results, and interpret findings in a biological context. This role suits people who want to stay connected to biological questions while working primarily with data.

Variant Scientist

Variant scientists focus specifically on the interpretation of genetic variants identified through sequencing. This role has become increasingly important in clinical genomics, where variants must be classified according to their pathogenicity. The work involves reviewing variant evidence, consulting databases, and preparing reports for clinicians.

A 2025 survey of variant scientists examined their demographics, training, duties, and professional development needs. The role requires knowledge of human genetics, familiarity with variant classification guidelines, and the ability to synthesize evidence from multiple sources. Variant scientists often work in clinical laboratories or diagnostic companies.

Genomics Data Engineer

Data engineers in genomics focus on the infrastructure that stores, processes, and serves genomic data. They build and maintain data pipelines, optimize database performance, and ensure that data is accessible to analysts and scientists. This role requires strong software engineering skills and experience with cloud computing platforms.

Genomics data engineers must understand the unique characteristics of genomic data, including its size, format, and the computational demands of processing it. They work with file formats such as FASTQ, BAM, and VCF, and they must ensure that pipelines are reproducible and scalable.

Bioinformatics Software Developer

Software developers in bioinformatics create the tools and applications that other researchers use. They might build web-based platforms for data visualization, develop command-line tools for specific analyses, or create APIs that allow different software systems to communicate. This role requires strong programming skills and an understanding of user needs.

Developers often work closely with analysts and scientists to understand what tools are needed. They must write clean, documented, maintainable code and test their software thoroughly. Experience with version control, continuous integration, and software packaging is valuable.

At a Glance: Career Role Comparison

The following table summarizes the main career roles in genomic data science, their typical responsibilities, and the skills most important for each.

Essential Skills for Genomic Data Science

The skills required for genomic data science fall into several categories. No single role requires all of these skills, but most positions need a combination drawn from each area.

Programming and Command-Line Skills

Programming is fundamental to genomic data science. Python and R are the most commonly used languages. Python is widely used for data processing, pipeline development, and machine learning. R is popular for statistical analysis and visualization, particularly in the Bioconductor ecosystem.

Unix command-line skills are essential because most bioinformatics tools run on Linux systems. A curriculum developed for life scientists with little or no command-line experience emphasizes progressive skill levels, starting with basic file navigation and moving to more complex operations such as scripting and automation. Interactive online tutorials that embed a command line in the browser can remove barriers related to software installation and access, making it easier for beginners to practice.

Version control with Git is important for tracking changes to code and collaborating with others. Containerization tools such as Docker and Singularity help ensure that analyses are reproducible across different computing environments.

Statistics and Machine Learning

A solid foundation in statistics is necessary for interpreting genomic data. Concepts such as hypothesis testing, multiple testing correction, regression, and Bayesian inference appear throughout the field. Understanding the assumptions and limitations of statistical methods is as important as knowing how to apply them.

Machine learning has become increasingly relevant. A systematic review of artificial intelligence approaches for predictive biomarker discovery in immuno-oncology found that standard machine learning methods were used in 72% of the 90 studies reviewed, with deep learning methods used in 22% and both approaches in 6%. The review covered genomics, radiomics, digital pathology, real-world data, and multimodal data. This pattern reflects the broader trend in genomic data science toward more sophisticated analytical methods.

Biological and Genomic Knowledge

Understanding the biology underlying genomic data is critical for meaningful analysis. You need to know what a gene is, how variants can affect function, and what different sequencing technologies measure. Familiarity with the central dogma of molecular biology, genome structure, and basic genetics provides the context needed to interpret results.

Domain-specific knowledge matters for specialized roles. For example, a variant scientist needs deep knowledge of human genetics and clinical genomics. A computational biologist studying cancer needs to understand tumor biology and the genomic alterations that drive cancer.

Data Management and Reproducibility

Genomic data is large, complex, and sensitive. Managing it requires attention to data organization, metadata, and versioning. Reproducibility is a core principle, meaning that analyses should be documented well enough that others can repeat them and obtain the same results.

The FAIR Guiding Principles describe four foundational concepts for scientific data management: findability, accessibility, interoperability, and reusability. These principles, published in Scientific Data, provide a framework for making data useful to the broader community. Applying FAIR principles to your own work means using standard file formats, documenting your methods, and depositing data in public repositories where appropriate.

Communication and Collaboration

Genomic data scientists rarely work alone. They collaborate with experimental biologists, clinicians, and other data scientists. The ability to explain technical concepts to non-specialists is valuable. Writing clear documentation, presenting results effectively, and participating in team discussions are all part of the job.

A study of data scientist skills identified 44 distinct skills, with the top five being business, statistics, machine learning, communication, and analysis. Communication appears in the top five because data scientists must translate their findings into decisions and actions. In genomic data science, this might mean explaining to a clinician why a particular variant is or is not clinically significant.

Educational Pathways

There is no single educational path into genomic data science. People enter the field from biology, computer science, statistics, and other backgrounds. The most common pathways include formal degree programs, online courses, and hands-on training.

Degree Programs

Many genomic data scientists hold a master's degree or PhD in bioinformatics, computational biology, genomics, or a related field. Graduate programs provide structured training in both the biological and computational aspects of the field. They also offer opportunities for research experience, which is critical for building a portfolio of work.

Undergraduate degrees in bioinformatics are becoming more common, but many professionals enter the field with degrees in biology, computer science, or statistics and then gain genomic skills through graduate study or on-the-job training.

The International Society for Computational Biology has developed a competency framework for bioinformatics education and training. Version 3 of this framework, published in 2024, defines the knowledge, skills, and attitudes needed by individuals in bioinformatics roles. The framework is openly accessible and can help you identify what you need to learn and at what level.

Online Courses and Certificates

Online courses offer a flexible way to build skills. Platforms such as Coursera host genomic data science courses covering topics from introductory bioinformatics to specialized areas like machine learning for genomics. These courses can be useful for gaining specific skills or exploring whether the field is a good fit.

However, online courses alone are rarely sufficient for entering the field. Employers typically look for demonstrated experience applying skills to real problems. Online courses should be supplemented with hands-on projects, internships, or research experience.

Internships and Research Experience

Internships provide valuable experience and help you build a professional network. Genomic data science internships are available at academic institutions, government agencies, and companies. These positions allow you to work on real projects, learn from experienced professionals, and develop skills that are difficult to acquire in a classroom.

Research experience is particularly important for academic careers. Working in a research laboratory, even as an undergraduate, teaches you how to approach open-ended problems, manage data, and communicate results. Course-based undergraduate research experiences, or CUREs, are one way to gain research experience within a structured curriculum.

Training Programs for Faculty

For current life science faculty who want to incorporate bioinformatics into their teaching, specialized training programs exist. One example is a module-based, week-long short course designed to train professors from small colleges and regional universities in essential bioinformatics skills. The modules are built to be adapted by the professor-trainees for use in their own classes. This approach addresses the gap where many life science professors did not acquire bioinformatics analysis skills during their own training.

Practical Steps to Enter the Field

If you are considering a career in genomic data science, the following steps can help you build the skills and experience you need.

Assess Your Current Skills

Start by evaluating your current strengths and gaps. If you come from a biology background, you may need to focus on programming and statistics. If you come from a computer science background, you may need to learn molecular biology and genomics. The ISCB competency framework can help you identify specific competencies to develop.

Build Foundational Programming Skills

Learn Python or R first. Both are widely used, and many resources are available for beginners. Practice working with real data, even if it is small. The Unix command line is also essential, and interactive tutorials that run in the browser can help you practice without needing to install software.

Learn Genomic Data Formats and Tools

Familiarize yourself with common file formats such as FASTQ, BAM, and VCF. Learn how to use standard tools like BWA for read alignment, SAMtools for processing alignment files, and GATK for variant calling. Public databases such as the National Center for Biotechnology Information provide access to genomic data and tools that you can use for practice.

Work on Projects

Apply your skills to real problems. You might analyze a public dataset, reproduce a published analysis, or contribute to an open-source bioinformatics project. Document your work carefully and make it available, for example through GitHub. A portfolio of projects demonstrates your abilities to potential employers.

Seek Mentorship and Community

Connect with people working in the field. Attend conferences, join online communities, and reach out to researchers whose work interests you. Mentorship can provide guidance on career decisions and help you navigate the field.

Consider Formal Training

If you decide that genomic data science is the right career path, consider pursuing a graduate degree or a specialized certificate program. Formal training provides structure, access to experts, and opportunities for research that are difficult to replicate through self-study.

Records and Measurements in Genomic Data Science

Keeping good records is essential in genomic data science. The field depends on reproducibility, and your records should allow you or others to reconstruct exactly what you did.

Laboratory Notebooks and Electronic Records

Maintain a detailed record of your analyses. This includes the commands you ran, the parameters you used, the versions of software tools, and the data files you analyzed. Electronic notebooks such as Jupyter Notebooks are useful because they combine code, output, and narrative in a single document.

Version Control

Use version control for all code and configuration files. Git is the standard tool. Commit changes frequently with clear messages describing what you did and why. This practice allows you to track the evolution of your analysis and revert to earlier versions if needed.

Data Management Plans

For larger projects, develop a data management plan that describes how data will be stored, backed up, and shared. The plan should address data formats, metadata standards, and access controls. Following FAIR principles helps ensure that your data will be useful to others.

Quality Control Metrics

Record quality control metrics for every dataset you process. For sequencing data, this includes metrics such as read depth, mapping rate, and duplication rate. These metrics help you identify problems early and provide evidence that your data is suitable for downstream analysis.

Common Failure Patterns

Understanding common failure patterns can help you avoid mistakes and troubleshoot problems when they arise.

Insufficient Quality Control

Skipping or rushing quality control is a common error. Poor-quality sequencing data can produce misleading results. Always check basic metrics before proceeding with analysis. If metrics indicate problems, investigate the cause before continuing.

Inadequate Documentation

Failing to document your analysis makes it difficult to reproduce or troubleshoot. You may not remember what you did six months later, and collaborators will not be able to understand your work. Document as you go instead of trying to reconstruct your steps afterward.

Overlooking Data Versioning

Genomic data files are often updated as reference genomes improve or as new annotations become available. If you do not record which version of a reference genome or annotation you used, your results may not be comparable to others. Always record version information for reference data.

Misapplying Statistical Methods

Genomic data has unique statistical properties, including high dimensionality and complex correlation structures. Applying standard statistical methods without considering these properties can lead to false conclusions. Multiple testing correction is essential when testing many hypotheses simultaneously.

Ignoring Reproducibility

Analyses that cannot be reproduced by others have limited value. Ensure that your code, data, and documentation are organized so that someone else could repeat your analysis. Containerization tools can help by capturing the software environment used for an analysis.

Limitations and Challenges in the Field

Genomic data science faces several limitations and challenges that professionals should understand.

Data Volume and Computational Demands

Genomic data is massive. A single human whole-genome sequencing run produces hundreds of gigabytes of raw data. Storing, processing, and analyzing this data requires substantial computational infrastructure. Cloud computing has made this more accessible, but costs can still be significant.

Data Heterogeneity

Genomic data comes in many forms, including DNA sequence, RNA expression, epigenetic modifications, and protein abundance. Integrating these different data types is challenging but increasingly important. The concept of medical digital twins, which involves creating a virtual copy of a patient that updates with new data, highlights the potential of integrating multimodal data for precision medicine.

Privacy and Data Sharing

Genomic data is sensitive personal information. Sharing data for research requires careful attention to privacy and consent. The National Institutes of Health Genomic Data Sharing Policy describes expectations for data sharing, including how data should be deposited and accessed. Professionals must understand and follow applicable policies and regulations.

Reproducibility Crisis

Reproducibility is a persistent challenge across the sciences. In genomic data science, the complexity of analyses and the many choices involved in data processing make it easy to produce results that are difficult to reproduce. Following best practices for documentation, version control, and containerization can help address this problem.

Workforce Diversity

The genomic data science workforce does not fully reflect the diversity of the population. Students at institutions with limited resources have had relatively little exposure to curricula and professional development opportunities that lead to careers in genomic data science. Broadening participation requires intentional efforts to support education and training at underserved institutions.

Professional Development and Career Advancement

Once you enter the field, ongoing professional development is important. Genomic technologies and analytical methods evolve rapidly, and staying current requires continuous learning.

Continuing Education

Attend workshops, conferences, and webinars to learn about new methods and tools. The European Bioinformatics Institute offers training courses covering various aspects of bioinformatics and genomics. These courses range from introductory to advanced and are taught by experts in the field.

Building a Professional Network

Networking helps you learn about job opportunities, collaborate on projects, and stay informed about developments in the field. Join professional organizations such as the International Society for Computational Biology. Participate in online communities and contribute to open-source projects.

Developing Leadership Skills

As you advance, you may take on leadership roles such as managing a team, leading a project, or directing a core facility. Leadership requires skills beyond technical expertise, including project management, budgeting, and people management. Seek opportunities to develop these skills through training or by taking on increasing responsibility.

Specializing

Many professionals eventually specialize in a particular area, such as cancer genomics, rare disease diagnosis, or microbial genomics. Specialization allows you to develop deep expertise and can lead to higher-level positions. Consider what areas interest you most and seek opportunities to work in those areas.

The Role of Artificial Intelligence in Genomic Data Science

Artificial intelligence is transforming genomic data science. Machine learning and deep learning methods are being applied to problems ranging from variant calling to drug response prediction.

AI for Biomarker Discovery

A systematic review of artificial intelligence approaches for predictive biomarker discovery in immuno-oncology examined 90 studies across five data modalities. The review found that AI methods have expanded the horizon for biomarker discovery, with novel biomarkers identified in radiomics and pathomics, and molecular biomarkers expanding beyond genomics into transcriptomics and epigenomics. Complex algorithms and new types of AI-based markers, such as meta-biomarkers, are emerging by integrating multimodal and multi-omics data.

Medical Digital Twins

The concept of medical digital twins is gaining attention. A digital twin is a constantly updating virtual copy of a real-world object or process. In medicine, a digital twin of a patient could enable analysis, simulation, and prediction of health outcomes. Five key components have been outlined: the patient, data connection, patient-in-silico, interface, and twin synchronization. Enabling technologies include multimodal data, artificial intelligence, and mechanistic modeling.

Limitations of AI Approaches

AI methods have limitations. The systematic review of AI in immuno-oncology found that no prospective study design incorporated AI-based methodologies from the outset, all implemented AI as a post hoc analysis. This suggests that AI is still being validated for clinical applications. Professionals should understand both the potential and the limitations of AI methods.

Genomic Data Science in Clinical Applications

Genomic data science has important clinical applications, particularly in the diagnosis and treatment of disease.

Cancer Genomics

Cancer genomics is a major application area. Genomic data from tumors can identify mutations that drive cancer, inform prognosis, and guide treatment selection. The use of immune checkpoint inhibitors has revolutionized treatment of multiple cancer types, but selecting patients who may benefit remains challenging. AI approaches that integrate genomic, radiomic, pathomic, and clinical data are being developed to improve patient selection.

Rare Disease Diagnosis

Whole-genome and whole-exome sequencing are used to diagnose rare genetic diseases. Variant scientists play a critical role in interpreting the clinical significance of identified variants. The process involves comparing patient variants to population databases, assessing the predicted impact on protein function, and reviewing the medical literature.

Infectious Disease Genomics

Genomic data science is also applied to infectious diseases. For example, cryptococcosis is a major worldwide disseminated invasive fungal infection, and cryptococcal meningitis accounts for substantial mortality and morbidity. Genomic analysis can help understand the epidemiology of such infections and guide treatment decisions.

Chronic Disease Research

Genomic data science contributes to research on chronic diseases. For example, inflammatory bowel disease has evolved across epidemiologic stages, with incidence increasing in newly industrialized regions. Transcriptome analysis of intestinal biopsies has been used to generate molecular inflammation scores that reflect the depth of inflammation and may predict treatment response and disease flares.

Professional Escalation Criteria

Knowing when to escalate a problem is important in genomic data science. The following situations warrant escalation to a supervisor, collaborator, or other professional.

Data Quality Issues

If you encounter data quality problems that you cannot resolve, escalate the issue. This might include unexpected patterns in sequencing metrics, evidence of sample contamination, or discrepancies between expected and observed results. Document what you observed and what you tried before escalating.

Ethical or Privacy Concerns

If you become aware of a potential privacy breach or ethical concern related to genomic data, escalate immediately. This might include unauthorized access to data, inappropriate data sharing, or research that raises ethical questions. Follow your institution's policies for reporting such concerns.

Clinical Interpretation Uncertainties

If you are involved in clinical variant interpretation and encounter a variant that is difficult to classify, escalate to a more experienced colleague or a multidisciplinary team. Misclassification can have serious consequences for patients.

Reproducibility Failures

If you cannot reproduce a published analysis or your own earlier results, escalate the issue. This might indicate a problem with the original analysis, a change in software or data, or an error in your current approach. Investigating reproducibility failures can prevent the spread of incorrect results.

Building a Career Portfolio

A strong portfolio demonstrates your skills and experience to potential employers. The following elements are valuable in a genomic data science portfolio.

Code Repositories

Maintain public code repositories on platforms such as GitHub. Include well-documented scripts, pipelines, and analysis notebooks. Ensure that your code is clean, readable, and includes instructions for running it.

Project Write-Ups

Write summaries of your projects that describe the biological question, your approach, the results, and the limitations. These write-ups demonstrate your ability to communicate about your work.

Publications and Presentations

If you have published papers or presented at conferences, include these in your portfolio. They provide evidence of your ability to conduct rigorous research and communicate findings to the scientific community.

Certifications and Training Records

Document any certifications, completed courses, or training programs. These records show your commitment to professional development and provide evidence of specific skills.

Conclusion

Genomic data science offers diverse career paths for people with interests spanning biology, statistics, and computing. The field requires a combination of technical skills, biological knowledge, and communication abilities. Entry typically involves building foundational programming and statistical skills, gaining hands-on experience through projects or internships, and pursuing formal training where appropriate. The field faces challenges including data volume, privacy concerns, and workforce diversity, but it also offers opportunities to contribute to important advances in medicine and biology. By understanding the career roles, developing the essential skills, and following practical steps to build experience, you can position yourself for a rewarding career in this growing field.

Frequently Asked Questions

What is the difference between a bioinformatics analyst and a genomic data scientist?

A bioinformatics analyst primarily processes and analyzes genomic data using established pipelines and tools. They perform quality control, run standard analyses, and generate reports. A genomic data scientist develops new analytical methods, applies advanced statistical modeling and machine learning, and integrates multiple data types. The data scientist role typically requires deeper training in statistics and computer science and involves more method development than the analyst role.

Do I need a PhD to work in genomic data science?

No, a PhD is not always required. Many positions, particularly analyst and software developer roles, are accessible with a master's degree or even a bachelor's degree combined with relevant experience. However, a PhD is often expected for research scientist positions at academic institutions and for leading independent research. The appropriate level of education depends on the specific role and career goals.

What programming language should I learn first for genomic data science?

Python and R are the two most important languages. Python is widely used for data processing, pipeline development, and machine learning. R is popular for statistical analysis and visualization, particularly in the Bioconductor ecosystem. Many professionals learn both. Starting with Python is often recommended because of its general-purpose utility, but the choice depends on your specific interests and the requirements of the roles you are targeting.

How can I get experience in genomic data science without a formal degree?

You can gain experience through online courses, personal projects, and contributions to open-source projects. Analyze public datasets available through repositories such as the National Center for Biotechnology Information. Document your work in a public code repository. Internships and volunteer positions in research laboratories also provide valuable hands-on experience.

What are the most important skills for a genomic data scientist?

The most important skills include programming in Python or R, statistical analysis, machine learning, Unix command-line proficiency, and biological knowledge. Communication skills are also critical because genomic data scientists must explain their work to collaborators and stakeholders. Data management and reproducibility practices are increasingly important as well.

Is genomic data science the same as bioinformatics?

The terms overlap but are not identical. Bioinformatics is a broader field that includes the application of computational methods to all areas of biology. Genomic data science focuses specifically on genomic data and often emphasizes statistical modeling and machine learning. In practice, many professionals work across both areas, and the boundaries between them are not rigid.

What career advancement opportunities exist in genomic data science?

Advancement can take several forms. You might move from an analyst role to a data scientist or computational biologist role with more responsibility for method development. You might specialize in a particular area such as cancer genomics or clinical variant interpretation. You might move into management, leading a team or directing a core facility. Some professionals move between academia, industry, and government settings.

How important is domain knowledge in biology for genomic data science?

Domain knowledge is important but the required depth varies by role. Analysts and data engineers may need only a working understanding of genomics concepts. Computational biologists and variant scientists need deeper biological knowledge. Even in more computational roles, understanding the biology helps you ask better questions, design better analyses, and interpret results more accurately.

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References and Further Reading

• EMBL-EBI Training. European Bioinformatics Institute.
• NCBI Data Resources. National Center for Biotechnology Information.
• Genomic Data Sharing Policy. National Institutes of Health.
• The FAIR Guiding Principles. Scientific Data.
• PubMed. National Library of Medicine.
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• Diversifying the genomic data science research community.. Genome research, 2022.
• Artificial intelligence for predictive biomarker discovery in immuno-oncology: a systematic review.. Annals of oncology : official journal of the European Society for Medical Oncology, 2024.
• Global evolution of inflammatory bowel disease across epidemiologic stages.. Nature, 2025.
• Global burden of chronic respiratory diseases and risk factors, 1990-2019: an update from the Global Burden of Disease Study 2019.. EClinicalMedicine, 2023.
• Medical digital twins: enabling precision medicine and medical artificial intelligence.. The Lancet. Digital health, 2025.
• Global, regional, and national burden of stroke and its risk factors, 1990-2021: a systematic analysis for the Global Burden of Disease Study 2021.. The Lancet. Neurology, 2024.
• Biopsy and blood-based molecular biomarker of inflammation in IBD.. Gut, 2023.
• Grand challenges in bioinformatics education and training.. 2023.
• The ISCB competency framework v. 3: a revised and extended standard for bioinformatics education and training. 2024.
• Dual channel drug-drug interactions extraction based on cross attention.. 2026.
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• Unlocking the genomic landscape: Results of the Beyond 1 Million Genomes (B1MG) pilot in Belgium towards Genomic Data Infrastructure (GDI).. Health Policy, 2024.
• Fostering bioinformatics education through skill development of professors: Big Genomic Data Skills Training for Professors. PLoS Comput. Biol., 2019.
• Extending standards for genomics and metagenomics data: A research coordination network for the genomic standards consortium (RCN4GSC). Standards in Genomic Sciences, 2009.
• Survey of Demographics, Training, Duties, and Professional Development for Variant Scientists in Genomic Medicine. Journal of Molecular Diagnostics, 2025.

This article is educational and does not replace institutional policy, professional advice, or applicable safety and regulatory requirements.