How to Showcase Your Side Projects on Your Bioinformatics Resume

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

Quick Answer

• Present side projects in a dedicated Projects section with each entry showing the biological question, dataset, tools, and measurable outcome.
• Use action verbs and specific technical terms that match job postings, such as "assembled," "annotated," "benchmarked," or "visualized."
• Limit each project description to three to five bullet points and place the section above less relevant experience when applying for bioinformatics roles.

Why Side Projects Matter for Bioinformatics Careers

Bioinformatics sits at the intersection of biology, statistics, and computer science. Employers in this field look for evidence that you can handle real biological data, write reproducible code, and interpret results in a biological context. The U.S. Bureau of Labor Statistics Occupational Outlook Handbook describes related occupations as requiring strong analytical skills and familiarity with scientific computing, which means hiring managers often evaluate candidates on demonstrated technical work instead of coursework alone.

Side projects serve as proof of your ability to apply computational methods to biological questions. For career changers, these projects bridge the gap between a degree in one field and the skills required in another. For students and early-career researchers, they show initiative beyond required coursework. For laboratory staff moving into dry-lab work, they demonstrate that you can handle the data analysis side of experiments.

The National Institutes of Health Office of Intramural Training and Education emphasizes that research careers depend on building a portfolio of skills through hands-on experience. Side projects function as that portfolio when formal work experience in bioinformatics is limited. They allow you to show hiring managers what you can do before they invest in training you.

What Employers Actually Look For in Project Descriptions

Hiring managers in bioinformatics typically scan resumes for specific signals. They want to see that you understand the biological problem, that you chose appropriate computational methods, and that you can communicate results clearly. The O*NET OnLine database lists tasks for bioinformatics-related occupations that include analyzing biological data, developing software tools, and collaborating with researchers. Your project descriptions should mirror these tasks.

Technical Skills and Tools

List the specific programming languages, software packages, and databases you used. Common tools include Python, R, Bioconductor, BLAST, SAMtools, and workflow managers like Snakemake or Nextflow. If you used a specific genome assembly tool or statistical package, name it. Generic phrases like "used bioinformatics tools" do not help an employer assess your skill level.

Biological Context

Explain why the project matters. A project that analyzes RNA sequencing data from cancer cell lines should state the biological question clearly. Employers want to see that you can connect computational output to biological meaning. The American Society for Microbiology Career Services notes that successful scientists communicate their work to diverse audiences, and your resume is the first test of that ability.

Measurable Outcomes

Quantify what you accomplished whenever possible. Did you process 50 samples? Did your pipeline reduce analysis time by a specific amount? Did you identify a certain number of differentially expressed genes? Numbers give employers a sense of scale and rigor. If you cannot quantify, describe the quality of the result, such as "achieved 95 percent mapping rate" or "validated findings against published datasets."

Reproducibility and Documentation

Show that your work follows good practices. Mention version control with Git, containerization with Docker, or public availability of your code on GitHub. The National Science Foundation Careers page highlights that scientific work in federal research organizations depends on transparent methods and data management. Employers in industry and academia increasingly expect the same standards.

Structuring the Projects Section on Your Resume

The Projects section should be easy to scan and should follow a consistent format. Use reverse chronological order, with your most recent or most relevant project first. Each project entry should include a title, a one-line context statement, and bullet points that describe your specific contributions.

Project Title and Context Line

The title should be descriptive, not generic. Instead of "Bioinformatics Project," use "Differential Expression Analysis of Drug-Resistant Breast Cancer Cell Lines." Below the title, add a context line that states the biological question and the dataset source if relevant. This line helps a hiring manager understand the project in seconds.

Bullet Points That Show Your Work

Each bullet point should start with an action verb and include a specific tool or method. Compare these two examples:

Weak: "Worked with RNA sequencing data and found interesting genes."

Strong: "Processed raw RNA sequencing reads from 24 samples using fastp and STAR, quantified gene expression with featureCounts, and identified 312 differentially expressed genes with DESeq2."

The strong version tells the employer exactly what you did, what tools you used, and what you found. It also demonstrates that you understand the standard workflow for RNA sequencing analysis.

Including Links and Artifacts

If your code is publicly available, include a link to the repository. If you wrote a report or created a visualization, mention it. Some employers will click through to verify your work. The National Institutes of Health Office of Intramural Training and Education advises researchers to maintain a professional online presence that showcases their work. A link to a well-organized GitHub repository or a published notebook strengthens your resume considerably.

At a Glance

Writing Bullet Points That Resonate with Hiring Managers

The bullet points under each project are the most important part of your resume. They need to show both technical competence and biological understanding. Use the structure of task, tool, and result for each bullet.

Task, Tool, Result Framework

Start with what you did, name the tool you used, and state the outcome. This framework keeps each bullet focused and informative. Here are examples across different bioinformatics areas:

Genomics: "Assembled a bacterial genome from Illumina reads using SPAdes, annotated the assembly with Prokka, and identified 4,812 coding sequences including two novel antimicrobial resistance genes."

Transcriptomics: "Built a differential expression pipeline in R using edgeR to compare treated and untreated Arabidopsis samples, identifying 1,204 genes with significant expression changes and validating results against published microarray data."

Phylogenetics: "Constructed a maximum likelihood phylogenetic tree from 16S rRNA sequences of 45 environmental isolates using RAxML, revealing three distinct clades consistent with geographic sampling sites."

Metagenomics: "Classified taxonomic composition of soil metagenomic samples using Kraken2 and Bracken, comparing diversity metrics across three land-use types with the vegan package in R."

Matching Language to Job Postings

Read the job descriptions you are targeting and note the specific skills they mention. If a posting asks for experience with single-cell RNA sequencing, highlight any project where you worked with Seurat or Scanpy. If a posting emphasizes machine learning, describe any project where you trained a classifier on biological data. The O*NET OnLine database shows that bioinformatics roles require a mix of biology knowledge and programming skill, so your bullets should demonstrate both.

Avoiding Common Bullet Point Mistakes

Do not write bullets that only list tools without context. "Used Python and R" tells an employer nothing about your ability. Do not claim skills you cannot defend in an interview. If you mention a tool, be prepared to explain what you did with it. Do not use passive voice. "The data was analyzed" is weaker than "I analyzed the data." Do not pad bullets with filler words. Every word should carry information.

Where to Place the Projects Section

The placement of your Projects section depends on your overall resume structure and your level of experience. For career changers and students, projects often deserve a prominent position near the top. For experienced researchers, projects may appear after publications or professional experience.

Career Changers and Students

If you are transitioning from another field or you are a current student, place the Projects section directly after your education or summary. This positions your most relevant work where hiring managers will see it first. Your formal job history may not reflect bioinformatics skills, so the projects section carries the weight of demonstrating your capabilities.

Experienced Researchers

If you have several years of relevant experience, integrate projects into your experience section or place them after it. Your job bullets already demonstrate your skills, so the projects section can highlight independent work or collaborations that go beyond your job duties. This approach avoids redundancy and keeps the resume focused.

Academic Positions and Industry Roles

Academic hiring committees may value projects that show methodological rigor and biological insight. Industry employers may value projects that show practical problem-solving and efficient workflows. Tailor your project descriptions to emphasize the aspects most relevant to each audience. The American Society for Microbiology Career Services provides guidance on tailoring application materials to different career paths, and the same principle applies to how you frame your projects.

Describing Projects for Different Bioinformatics Specializations

Bioinformatics is a broad field, and the way you describe a project should match the specialization you are targeting. A project that demonstrates genome assembly skills may not resonate with an employer looking for a statistical geneticist. Tailor your descriptions to the specific role.

Genomics and Genome Assembly

Focus on the sequencing platform, the assembly strategy, and the quality metrics. Mention coverage, N50 values, and how you assessed assembly completeness. If you compared multiple assemblers, describe the benchmarking process and the criteria you used to select the best result.

Transcriptomics and Gene Expression

Describe the experimental design, the alignment and quantification approach, and the statistical methods for identifying differentially expressed genes. Mention how you handled batch effects or other confounders. If you performed functional enrichment analysis, name the database and the tool you used.

Phylogenetics and Evolutionary Biology

Describe the sequence data, the alignment method, the model selection process, and the tree-building algorithm. Mention bootstrap support or other measures of confidence. If you estimated divergence times or ancestral states, describe those analyses as well.

Metagenomics and Microbiome Analysis

Describe the sample type, the sequencing approach, and the taxonomic classification method. Mention how you handled contamination and how you compared diversity across samples. If you performed functional profiling, describe the database and tools you used.

Machine Learning and Predictive Modeling

Describe the biological question, the features you engineered, the model you trained, and the evaluation metrics. Mention how you split the data and how you avoided overfitting. If you compared multiple models, describe the comparison process and the criteria for selecting the best performer.

Including Side Projects When You Have Limited Formal Experience

Career changers often worry that their lack of formal bioinformatics experience will disqualify them. Side projects can offset this concern, but only if they are presented effectively. The key is to show that your projects are substantial enough to demonstrate real skill.

How Many Projects to Include

Three to five well-developed projects are usually sufficient. Quality matters more than quantity. One project that shows a complete workflow from raw data to biological interpretation is more impressive than five projects that each show only a single step. If you have more projects, select the ones most relevant to the jobs you are targeting.

What to Do If Your Projects Are Coursework-Based

Coursework projects can appear on your resume, but you should frame them as independent work. Describe what you did beyond the assignment requirements. Did you add additional analyses? Did you clean up the code and make it reproducible? Did you apply the methods to a new dataset? These additions show initiative and genuine interest.

What to Do If You Have No Projects Yet

If you have no side projects, start with a small, well-defined analysis that you can complete in a few weeks. The National Institutes of Health Office of Intramural Training and Education offers resources for research skill development that can help you identify appropriate starting points. Public datasets are widely available through repositories like NCBI GEO, and many published papers include supplementary data you can reanalyze. A focused project that you complete and document is more valuable than an ambitious project you abandon.

Practical Steps for Building a Project Portfolio

Building a portfolio of side projects takes time, but a structured approach makes the process manageable. These steps can help you create projects that will strengthen your resume.

Step 1: Choose a Biological Question

Select a question that interests you and that you can address with publicly available data. The question should be specific enough that you can complete the analysis in a reasonable timeframe. A question like "How does gene expression differ between normal and tumor tissue in a specific cancer type?" is more tractable than "What causes cancer?"

Step 2: Find and Download Data

Identify a suitable dataset from a public repository. Document where you obtained the data and any relevant metadata. Keep notes on data processing steps so you can describe them accurately on your resume.

Step 3: Perform the Analysis

Work through the analysis systematically. Write clean, commented code. Save intermediate results. Track your decisions and the rationale behind them. This documentation will help you write accurate resume bullets later.

Step 4: Interpret the Results

Connect your computational results to the biological question. What do your findings mean in biological terms? Are they consistent with published literature? This interpretation step is what separates a bioinformatics project from a programming exercise.

Step 5: Document and Share Your Work

Write a brief report or create a notebook that describes your methods and results. Upload your code to a public repository with a clear README. This documentation serves as evidence of your skills and gives hiring managers a way to verify your claims.

Step 6: Write the Resume Entry

Use your documentation to write the resume entry. Follow the task, tool, result framework for each bullet point. Include the link to your repository or notebook.

Records and Measurements That Strengthen Your Resume

Specific numbers and concrete details make your project descriptions more credible. Keep records of the following types of information as you work on projects.

Dataset Characteristics

Record the number of samples, the sequencing depth, the number of reads, or the number of genes in your dataset. These details give employers a sense of the scale of your work.

Computational Performance

If you optimized a pipeline or reduced runtime, record the before and after numbers. If you used high-performance computing resources, note the specifications. These details show that you can work with real-world computational constraints.

Quality Metrics

Record quality metrics such as mapping rates, assembly N50 values, or classifier accuracy scores. These numbers demonstrate that you evaluated your work rigorously.

Validation Results

If you validated your findings against published data or experimental results, record the outcome. Validation shows that you understand the importance of confirming computational predictions.

Common Failure Patterns in Project Descriptions

Many resumes fail to showcase side projects effectively. Recognizing these common patterns can help you avoid them.

The Tool List Resume

This resume lists every tool the candidate has ever touched without any context. "Python, R, BLAST, SAMtools, GATK, Bioconductor, Linux" tells an employer nothing about your ability to use these tools effectively. Replace tool lists with project descriptions that show tools in action.

The Vague Description

This resume describes projects in general terms without specifics. "Analyzed genomic data to identify important genes" could describe almost any bioinformatics project. Add specifics about the data, the methods, and the findings.

The Overclaim

This resume claims skills the candidate cannot defend. If you mention a tool or method, be prepared to explain it in an interview. Overclaiming damages your credibility when the interviewer probes your experience.

The Disconnected Project

This resume includes projects that have no connection to the target role. A project on website development does not strengthen an application for a genomics position. Select projects that demonstrate relevant skills.

The Unverifiable Claim

This resume describes impressive results without any way for the employer to verify them. If you mention a repository or publication, make sure the link works and the content matches your description.

Tailoring Projects for Different Application Types

The way you present your projects should vary depending on the type of position you are applying for. Academic, industry, government, and clinical roles each value different aspects of your work.

Academic Research Positions

Academic hiring committees value methodological rigor and biological insight. Emphasize the scientific question, the analytical approach, and the interpretation of results. Mention any novel methods you developed or adapted. The National Institutes of Health Office of Intramural Training and Education provides guidance on academic research career development that emphasizes the importance of demonstrating independent scientific thinking.

Industry and Biotechnology Roles

Industry employers value efficiency, reproducibility, and practical problem-solving. Emphasize how you handled real-world data challenges, how you documented your work, and how your analysis could scale to larger datasets. Mention any experience with version control, containerization, or workflow management.

Government and Federal Research Positions

Government research organizations value transparent methods and clear communication. The National Science Foundation Careers page describes scientific work that involves collaboration, data management, and public accountability. Emphasize your documentation practices and your ability to communicate results to diverse audiences.

Clinical and Translational Roles

Clinical bioinformatics roles value accuracy, validation, and attention to regulatory considerations. Emphasize how you validated your findings, how you handled patient-related data, and how you ensured reproducibility. Mention any experience with clinical data formats or standards.

The Role of Networking and Professional Development

Side projects can also support your networking efforts. The American Society for Microbiology Career Services highlights the importance of professional networking for career advancement. When you attend conferences or connect with professionals online, your projects give you something concrete to discuss.

Using Projects in Informational Interviews

When you request an informational interview, mention a specific project that relates to the person's work. This approach shows that you have done your homework and gives the conversation a natural starting point. The person can offer feedback on your approach or suggest resources for further learning.

Sharing Projects in Professional Communities

Consider sharing your projects in professional communities such as bioinformatics forums, conference poster sessions, or local meetups. Presenting your work publicly builds confidence and invites feedback that can improve your skills. The Association of American Veterinary Medical Colleges notes that professional development involves engaging with the broader community, and the same principle applies across life science careers.

Building a Professional Online Presence

A professional online presence that showcases your projects can supplement your resume. The National Institutes of Health Office of Intramural Training and Education advises researchers to maintain visibility for their work. A GitHub profile with well-documented projects, a personal website with project summaries, or a blog describing your analytical approaches all serve this purpose.

Limitations and Honest Framing

While side projects strengthen your resume, they have limitations that you should acknowledge honestly. Employers know the difference between a side project and professional experience.

Side Projects Are Not Professional Experience

A side project does not replace the experience of working in a team, meeting deadlines, or handling the constraints of a professional environment. Be honest about the scope of your projects. Do not describe a course assignment as an independent research project.

Project Quality Varies

The quality of your projects depends on the data you used and the rigor of your analysis. A project that reanalyzes published data is valuable, but it is different from a project that generates new biological insights. Be clear about what you did and what you did not do.

Skills Transfer Is Not Automatic

Completing a project with a specific tool does not mean you have mastered that tool. Be prepared to discuss the limitations of your experience and your willingness to learn. Employers value honesty about skill levels.

Professional Escalation Criteria

Knowing when to seek additional help or training can improve your project portfolio and your resume. Consider these situations as signals that you should escalate your efforts.

When to Seek Formal Training

If you find yourself repeatedly struggling with the same technical concepts, consider formal training. The National Institutes of Health Office of Intramural Training and Education offers training resources for research skills. Structured courses or workshops can fill gaps that self-directed learning has not addressed.

When to Seek Mentorship

If you are unsure whether your projects are at the right level or focused on the right topics, seek mentorship. The American Society for Microbiology Career Services provides career development resources that can connect you with experienced professionals. A mentor can review your resume and give you honest feedback on your project descriptions.

When to Reassess Your Career Direction

If you have completed several projects but still feel unqualified for target roles, reassess your direction. The Occupational Outlook Handbook describes the education and experience requirements for related occupations. You may need additional coursework, a certificate, or a different specialization within bioinformatics.

Frequently Asked Questions

How many side projects should I include on my bioinformatics resume?

Include three to five projects that are most relevant to the jobs you are targeting. Quality matters more than quantity. One complete project that shows a full workflow from raw data to biological interpretation is more valuable than several partial projects. If you have more than five projects, select the ones that best demonstrate the skills employers in your target roles are seeking.

Should I include coursework projects on my resume?

Yes, but frame them as independent work. Describe what you did beyond the assignment requirements, such as additional analyses, improved code documentation, or application of methods to a new dataset. This framing shows initiative and genuine interest in bioinformatics instead of simply completing required coursework.

What if my side projects are not related to bioinformatics?

Select projects that demonstrate transferable skills. If your projects are in other fields, emphasize the analytical, programming, or data handling skills that apply to bioinformatics. If you have no relevant projects, start building one using publicly available biological data. A focused project completed and documented is more valuable than an ambitious project you abandon.

How do I describe a project that did not produce significant results?

Describe the process and what you learned. Employers value rigorous analysis even when results are negative or inconclusive. Explain the biological question, the methods you used, and what you concluded from the analysis. If you identified problems with the data or the approach, describe how you addressed them. This honesty demonstrates scientific maturity.

Should I include links to my code repositories on my resume?

Yes, if the repositories are well organized and documented. A link to a GitHub repository with a clear README, commented code, and reproducible instructions strengthens your resume. Before including a link, make sure the repository is public, the code runs, and the documentation matches your resume claims. An unorganized repository can hurt your application.

How do I tailor my project descriptions for different job applications?

Read each job description and identify the specific skills and tools mentioned. Adjust your project descriptions to emphasize the aspects most relevant to each role. For academic positions, emphasize methodological rigor and biological insight. For industry roles, emphasize efficiency, reproducibility, and practical problem-solving. Keep a master version of your resume and create tailored versions for each application.

What if I have no side projects at all?

Start with a small, well-defined analysis that you can complete in a few weeks. Use publicly available datasets from repositories like NCBI GEO or reanalyze supplementary data from published papers. Document your work carefully and share it publicly. The National Institutes of Health Office of Intramural Training and Education offers resources that can help you identify appropriate starting points for building research skills.

How do I explain side projects in an interview?

Be prepared to discuss the biological question, the data, the methods, and the results in detail. Explain why you made specific analytical decisions and what you would do differently with more time or resources. The O*NET OnLine database describes the analytical and communication skills required for bioinformatics roles, and your interview responses should demonstrate both.

Using the Evidence

Related Articles

• Clinical Research Coordinator Resume: Key Skills and Action Verbs to Stand Out
• Bioinformatics Career Path: From Entry-Level to Leadership
• Bioinformatics Impact Factor: What It Means for Your Career
• Bioinformatics vs Biostatistics: Choosing the Right Career Path
• Bioinformatics vs Biostatistics: Which Career Fits You?

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.