Side Projects That Land Bioinformatics Jobs

By Dr. Zubair Khalid, DVM, MS, PhD · 2026-08-27

Quick Answer

• Build a portfolio around re-analyzing public genomic or transcriptomic datasets, then document each step in a public repository with clear README files.
• Create one small automated pipeline that takes raw sequencing reads through quality control, alignment, and variant calling, and publish the workflow configuration.
• Employers evaluate demonstrated skills over formal credentials, but you still need domain knowledge in biology and statistics to interpret results correctly.

At a Glance

Why Side Projects Matter for Bioinformatics Hiring

Bioinformatics sits at the intersection of biology, computer science, and statistics. Employers in this field face a hiring problem that differs from many other technical roles. They need people who can handle messy biological data, write reliable code, and communicate findings to scientists who may not have computational training. A degree alone rarely proves all three abilities.

The Occupational Outlook Handbook from the U.S. Bureau of Labor Statistics describes the broader context for careers in this area. Many related occupations require at least a bachelor's degree, and some positions in research and development prefer graduate education. However, the handbook also notes that relevant work experience can substitute for formal education in some settings. This creates an opening for career changers who can demonstrate competence through concrete work.

Side projects serve as evidence of what you can actually do. A hiring manager can look at your GitHub repository and see whether you write clean code, document your process, and interpret results correctly. This matters more than a transcript because bioinformatics work is inherently project-based. You are not asked to recall facts during an interview. You are asked to explain how you solved a problem and what you learned from the process.

The Office of Intramural Training and Education at the National Institutes of Health emphasizes that research careers develop through practical training and mentorship. Their materials describe how scientists build skills through hands-on experience instead of through coursework alone. This aligns with what side projects accomplish. They give you a structured way to practice the skills that matter in real bioinformatics work.

Core Skills Employers Look For

Before choosing a side project, understand what skills you need to demonstrate. Bioinformatics roles vary widely, but most share a common skill set that you can develop through project work.

Command Line Proficiency

Most bioinformatics work happens in a Unix-like environment. You need to navigate directories, run scripts, manage files, and use text-based tools efficiently. The O*NET OnLine database lists computer and information technology skills as important for many occupations in this field. You can build this skill by working through tutorials and then applying what you learn to real data.

Scripting and Programming

Python and R are the two most common languages in bioinformatics. Python handles data processing, pipeline development, and machine learning tasks. R excels at statistical analysis and visualization. You do not need to master both immediately, but you should be comfortable with at least one. Your side project should use the language that matches the type of work you want to do.

Statistical Literacy

Bioinformatics involves analyzing data with biological variability and technical noise. You need to understand basic statistical concepts such as hypothesis testing, multiple testing correction, and regression. You also need to know when a result is biologically meaningful versus statistically significant by chance. The American Society for Microbiology career resources describe how microbiology professionals apply quantitative skills to interpret experimental data. The same principle applies to bioinformatics.

Biological Domain Knowledge

You cannot interpret bioinformatics results without understanding the biology behind the data. If you are analyzing gene expression data, you need to know what a gene is, how expression is measured, and what biological processes might explain your findings. If you are working with genomic variants, you need to understand inheritance patterns and functional consequences. The Association of American Veterinary Medical Colleges career resources illustrate how domain knowledge shapes professional practice in animal health. The same logic applies to bioinformatics in any biological field.

Reproducibility and Documentation

Bioinformatics projects fail when others cannot reproduce the analysis. Employers look for people who version their code, document their steps, and package their workflows so others can run them. This skill separates serious practitioners from people who just ran some scripts once. Your side project should demonstrate that you take reproducibility seriously.

Five Side Projects That Build a Competitive Portfolio

The following projects are ordered from simplest to most complex. Each one builds on the previous project's skills. You can complete the first two without a formal bioinformatics background if you are willing to learn through free online resources.

Project 1: Re-Analyze a Public Gene Expression Dataset

Public repositories such as the Gene Expression Omnibus and ArrayExpress contain thousands of datasets waiting for analysis. Pick a dataset that interests you and re-analyze it from raw data to biological interpretation.

Start by finding a dataset with a clear research question. For example, you might choose a study comparing gene expression in healthy and diseased tissue. Download the raw data files and the metadata describing the samples. Write a script that reads the data, performs quality control, normalizes the expression values, and identifies differentially expressed genes.

Document every step in a Jupyter notebook or R Markdown file. Explain why you made each analytical choice. Include visualizations of your quality control metrics and your final results. Write a short summary of the biological interpretation, even if you are not certain your interpretation is correct. The goal is to show that you can think through the analysis process.

Present this project on your resume with a link to your repository and a one-sentence description of what you did. For example, "Re-analyzed a public RNA-seq dataset comparing tumor and normal tissue, identified 247 differentially expressed genes, and documented the full workflow in a public repository."

Project 2: Build a Small Variant Calling Pipeline

A pipeline is a series of steps that processes data from one format to another. In bioinformatics, a common pipeline takes raw sequencing reads and produces a list of genetic variants. Building one yourself teaches you how the pieces fit together.

Use a small public dataset, such as a bacterial genome sequenced with Illumina technology. Install the standard tools for quality trimming, read alignment, and variant calling. Write a shell script or use a workflow manager like Snakemake or Nextflow to run the steps in order. Include a configuration file that lets someone change parameters without editing the main script.

The key is to make your pipeline reproducible. Someone else should be able to clone your repository, install the dependencies, and run the pipeline on their own data. Include a README that explains what each step does and what the output files mean.

This project demonstrates workflow management, scripting, and attention to detail. It also gives you something concrete to discuss in interviews when asked about your technical experience.

Project 3: Create an Interactive Data Visualization Dashboard

Bioinformatics generates complex results that are hard to communicate. An interactive dashboard lets you explore data visually and share your findings with others. This project combines data analysis with user interface design.

Choose a public dataset that lends itself to exploration. For example, you might use a dataset of antimicrobial resistance genes found in different bacterial species. Build a dashboard using R Shiny or Python Dash that lets users filter by species, resistance gene, or geographic location. Include plots that update based on user selections.

The dashboard should be more than a collection of charts. Think about what questions a user might want to answer and design the interface around those questions. Add a short written explanation of the data source and the limitations of the analysis.

This project shows that you can communicate technical results to a broader audience. It also demonstrates skills in data cleaning, exploratory analysis, and web-based visualization. These abilities are valuable in industry settings where scientists and non-scientists need to understand the data.

Project 4: Contribute to an Open-Source Bioinformatics Tool

Open-source projects need contributors who can fix bugs, improve documentation, and add features. Contributing to an existing project teaches you how to work with a codebase, follow community standards, and collaborate with other developers.

Start by finding a project that you use or that aligns with your interests. Look at the issue tracker for tasks labeled as good first issue or beginner friendly. Read the contribution guidelines carefully. Make a small contribution, such as fixing a documentation error or adding a test for an existing function.

The National Science Foundation careers page describes how scientific and technical work often involves collaboration across teams and disciplines. Contributing to open source mirrors this experience. You learn to communicate with maintainers, respond to code review feedback, and integrate your work with existing code.

This project is particularly valuable if you lack formal computer science training. It shows that you can work effectively in a professional software development environment, which is a common concern for career changers.

Project 5: Write a Reproducible Research Report

A reproducible research report combines code, data, and narrative in a single document. The reader can see your analysis steps, run them themselves, and verify your conclusions. This format is increasingly expected in academic and industry settings.

Choose a research question that you can answer with public data. Write a report that includes your code, your results, and your interpretation. Use a tool like R Markdown or Jupyter Book that combines text and code in one document. Include all the data files and code needed to reproduce your analysis from scratch.

The report should read like a scientific paper but with the code embedded. Explain your hypothesis, your methods, your results, and your conclusions. Include figures that illustrate your findings. Make sure the entire document is self-contained and runs without errors.

This project demonstrates scientific writing, analytical rigor, and attention to reproducibility. It is particularly useful if you are transitioning from academia to industry, where the ability to communicate clearly is highly valued.

How to Choose the Right Project for Your Goals

The best project depends on your current skills and your target role. Consider the following factors before committing to a project.

Match the Project to the Job You Want

If you want to work in a clinical genomics lab, focus on variant calling and interpretation. If you want to work in drug discovery, focus on gene expression analysis and pathway interpretation. If you want to work in a software company that builds bioinformatics tools, focus on pipeline development and open-source contributions.

Look at job postings for roles you want and note the specific skills they mention. The Occupational Outlook Handbook describes how job requirements vary by occupation and industry. Use this information to choose a project that directly addresses the skills employers in your target field are asking for.

Assess Your Current Skill Level Honestly

If you have never written a line of code, start with Project 1 and use a guided tutorial. If you are comfortable with Python or R but lack biological knowledge, start with a dataset in a familiar biological area. If you have strong programming skills but no biology background, consider Project 4 to learn from experienced developers.

The Office of Intramural Training and Education at the National Institutes of Health provides resources for career development that emphasize building skills incrementally. Their approach suggests starting with manageable tasks and gradually increasing complexity. Apply the same principle to your side project.

Consider the Time You Can Commit

A side project that takes six months to complete is less useful than one you finish in six weeks. Choose a project that you can complete with the time you have available. A small, finished project demonstrates more discipline than a large, unfinished one.

Set a specific goal with a deadline. For example, "I will complete a variant calling pipeline on a public bacterial dataset within four weeks." Break the work into weekly tasks and track your progress. This approach keeps the project manageable and gives you a clear endpoint.

Practical Steps to Complete Your Project

The following workflow applies to any of the five projects. Follow these steps to ensure your project is useful for your job search.

Step 1: Define the Scope and Success Criteria

Write down what you want to accomplish and how you will know you succeeded. For a data analysis project, success might mean producing a set of differentially expressed genes with a documented workflow. For a pipeline project, success might mean running the pipeline end to end on a test dataset.

Keep the scope narrow. A project that tries to answer too many questions will become unfocused and difficult to complete. Choose one clear goal and design your project around it.

Step 2: Set Up Your Development Environment

Create a directory for your project and initialize a version control repository. Install the software tools you need, such as Python, R, or a workflow manager. Use a package manager to track your dependencies so someone else can reproduce your environment.

The O*NET OnLine database lists the technical skills required for occupations in this field, including working with computers and using software tools. Setting up a proper development environment is the first step in demonstrating these skills.

Step 3: Work Through the Analysis Incrementally

Do not try to write the entire project at once. Work through the analysis step by step, testing each component as you go. Save your progress frequently and commit your changes to version control with clear messages.

If you get stuck, search for solutions online or ask for help in community forums. The American Society for Microbiology career resources emphasize the importance of networking and seeking guidance from experienced professionals. The same principle applies to technical problem solving.

Step 4: Document Everything as You Go

Write your README file early and update it as your project evolves. Document the purpose of each script, the input and output formats, and the parameters that can be changed. Include a section on how to reproduce your analysis from scratch.

Good documentation is a skill that employers value highly. It shows that you think about how others will use your work. It also makes your project more useful to you when you need to revisit it later.

Step 5: Review and Refine Before Publishing

Before you share your project publicly, review it as if you were a hiring manager. Check that the code runs without errors, the documentation is clear, and the results are presented logically. Remove any files that contain sensitive data or that are not needed for the analysis.

Ask someone else to review your project and give you feedback. This could be a colleague, a mentor, or someone from an online community. The Office of Intramural Training and Education at the National Institutes of Health describes how mentorship and feedback are essential for professional development. Use the feedback to improve your project before you present it to employers.

How to Present Your Projects on a Resume and in Interviews

Completing a side project is only half the work. You also need to present it effectively to employers.

Resume Presentation

Create a Projects section on your resume that lists each project with a title, a one-sentence description, and a link to the repository. Use action verbs to describe what you did. For example, "Built a reproducible RNA-seq analysis pipeline that processes raw reads into differentially expressed gene lists."

Include the technologies you used, such as Python, R, Snakemake, or Docker. This helps hiring managers quickly assess whether your skills match their needs. The Occupational Outlook Handbook notes that employers often look for specific technical skills when filling positions. Make it easy for them to see that you have those skills.

GitHub Profile Presentation

Your GitHub profile is often the first thing a hiring manager looks at. Pin your best projects to the top of your profile so they are immediately visible. Write a clear README for each project that explains what it does and how to run it.

Use a consistent naming convention for your repositories and include a license file. This shows that you understand professional software development practices. The National Science Foundation careers page describes how technical work in research organizations requires attention to quality and documentation. Apply the same standards to your public profile.

Interview Discussion

When asked about your projects in an interview, focus on the decisions you made and what you learned. Explain why you chose a particular approach, what problems you encountered, and how you solved them. This demonstrates critical thinking and problem-solving ability.

Be honest about the limitations of your projects. If you did not validate your results against a gold standard, say so. If you are unsure about the biological interpretation, acknowledge that uncertainty. Employers value intellectual honesty over false confidence.

Common Failure Patterns and How to Avoid Them

Many career changers make the same mistakes when building side projects. Recognizing these patterns can help you avoid them.

Failure Pattern 1: Choosing a Project That Is Too Ambitious

A project that requires months of work and advanced skills will likely remain unfinished. This is worse than completing a smaller project because it demonstrates a lack of judgment about scope.

Avoid this by choosing a project that you can complete in four to eight weeks. If you are unsure whether a project is feasible, start with a smaller version and expand it later.

Failure Pattern 2: Copying Tutorials Without Understanding

Following a tutorial step by step can teach you new skills, but copying code without understanding it does not demonstrate competence. Hiring managers can tell when someone does not understand their own code.

Avoid this by modifying the tutorial code to answer a different question or work with different data. This forces you to understand what each step does and why it is necessary.

Failure Pattern 3: Neglecting Documentation

A repository with code but no README is nearly useless to an employer. It shows that you do not think about how others will use your work.

Avoid this by writing your README early and updating it as you go. Include instructions for installation, usage, and reproduction of your results.

Failure Pattern 4: Ignoring Biological Interpretation

A bioinformatics project that stops at the computational analysis is incomplete. Employers want to see that you can connect your results to biological meaning.

Avoid this by including a section in your project that interprets your findings in biological terms. Even if your interpretation is tentative, it shows that you understand the purpose of the analysis.

Failure Pattern 5: Not Seeking Feedback

Working in isolation means you miss opportunities to improve your project and learn from others. The American Society for Microbiology career resources emphasize the value of professional networking and mentorship. Apply this to your project work.

Avoid this by sharing your project with others before you present it to employers. Ask for specific feedback on your code, documentation, and interpretation. Use the feedback to make your project stronger.

Limitations of Side Projects in the Hiring Process

Side projects are valuable, but they have limitations that you should understand.

Side Projects Do Not Replace Formal Education

Some employers require a specific degree for bioinformatics positions. The Occupational Outlook Handbook notes that education requirements vary by occupation and employer. A side project will not substitute for a degree if the employer has a hard requirement.

Research the requirements for the roles you want before investing time in side projects. If a degree is required, consider whether you are willing to pursue one or whether you should target roles that do not have this requirement.

Side Projects Do Not Prove You Can Work in a Team

Bioinformatics work is collaborative. You need to work with biologists, clinicians, and other computational scientists. A side project completed alone does not demonstrate this ability.

Address this limitation by contributing to open-source projects or collaborating with others on a project. This gives you experience working with a team and evidence that you can do so effectively.

Side Projects Do Not Demonstrate Experience With Real-World Constraints

Real bioinformatics work involves deadlines, limited resources, and imperfect data. A side project that you complete on your own schedule does not show how you perform under these constraints.

Address this limitation by setting deadlines for yourself and documenting how you managed your time. If possible, find a volunteer opportunity or internship that gives you experience with real-world projects.

Professional Escalation Criteria

If you are struggling to complete a side project or are unsure about your career direction, consider seeking professional guidance.

When to Seek Mentorship

If you have been working on a project for several weeks and are not making progress, seek help from someone with more experience. The Office of Intramural Training and Education at the National Institutes of Health provides resources for finding mentors and building professional relationships. A mentor can help you identify what is blocking your progress and suggest a path forward.

When to Consider Formal Training

If you find that you lack the foundational knowledge to complete even a basic project, consider formal training. This could be a certificate program, a bootcamp, or a graduate degree. The Occupational Outlook Handbook describes the education pathways for various occupations. Choose a program that matches your career goals and your current skill level.

When to Reassess Your Career Direction

If you complete a side project and find that you do not enjoy the work, this is valuable information. It is better to discover this before you invest years in a career path. The Association of American Veterinary Medical Colleges career resources illustrate how career exploration helps people make informed decisions about their professional path. The same principle applies to bioinformatics.

Frequently Asked Questions

How long should I spend on a bioinformatics side project?

A focused project should take four to eight weeks of part-time work. This is enough time to complete a meaningful analysis without losing momentum. If a project takes longer, you may have chosen something too ambitious for your current skill level.

Do I need a biology degree to do bioinformatics side projects?

No, but you need to learn enough biology to interpret your results. Many successful bioinformaticians come from computer science or statistics backgrounds. Focus on learning the biological concepts relevant to your project instead of trying to master all of biology.

What programming language should I learn first for bioinformatics?

Python is the most common choice for beginners because it is widely used and has extensive libraries for data analysis. R is also valuable, particularly for statistical analysis and visualization. Choose one language and become comfortable with it before learning the other.

How do I find public datasets for my side project?

Public repositories such as the Gene Expression Omnibus, the Sequence Read Archive, and ArrayExpress contain thousands of datasets. Look for datasets with clear metadata and a research question that interests you. Start with a small dataset that you can process on a standard laptop.

Should I publish my side project even if it is not perfect?

Yes. A finished project with clear documentation is more valuable than a perfect project that never gets completed. Publish your work, note its limitations, and be open to feedback. This demonstrates intellectual honesty and a commitment to improvement.

Can side projects help me transition from academia to industry?

Yes, but you need to choose projects that demonstrate industry-relevant skills. Industry employers value reproducibility, documentation, and communication. Choose projects that show these skills instead of projects that only demonstrate academic analysis techniques.

How many side projects should I complete before applying for jobs?

One or two well-documented projects are sufficient to demonstrate your skills. Quality matters more than quantity. A single project that shows careful analysis, clear documentation, and biological interpretation is more impressive than five projects that are incomplete or poorly documented.

What should I do if I get stuck on my side project?

First, try to solve the problem yourself by searching online or reading documentation. If you are still stuck after a reasonable effort, ask for help in community forums or from a mentor. The American Society for Microbiology career resources emphasize the value of professional networks for career development. Use these networks when you need technical or career guidance.

Using the Evidence

Related Articles

• Bioinformatics Portfolio: Projects That Demonstrate Real Research Judgment
• Bioinformatics Scientist Jobs
• Bioinformatics Entry Level Jobs
• Genomic Data Internship: How to Land and Succeed in a Bioinformatics Internship
• Bioinformatics Career Path: From Entry-Level to Leadership

References and Further Reading

• Occupational Outlook Handbook. U.S. Bureau of Labor Statistics.
• O*NET OnLine. U.S. Department of Labor.
• Office of Intramural Training and Education. National Institutes of Health.
• Careers at NSF. U.S. National Science Foundation.
• Career Services. American Society for Microbiology.
• AAVMC Career Resources. Association of American Veterinary Medical Colleges.
• NCBI Literature Resources. National Center for Biotechnology Information.
• PubMed. National Library of Medicine.

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