Household Dust Metagenomics and Allergy Links

ISEF Category: Computational Biology and Bioinformatics

Subcategory: Genomics  ·  Difficulty: Advanced  ·  Setup: University Lab  ·  Time: Full Year

The Hook

A teaspoon of dust can hold a tiny ecosystem. That mix may differ from house to house, and those differences may connect to allergy or asthma symptoms. You can ask whether the microbes people live with track with the symptoms they report. That makes your project part biology, part data science, and part public health.

What Is It?

Metagenomic profiling means reading DNA from a mixed sample, then asking which organisms are in it. Instead of trying to grow microbes one by one, you sequence everything together and sort the reads with tools like Kraken2. Think of it like taking a giant pile of puzzle pieces, then using a reference image to decide which puzzle each piece belongs to.

Household dust works as a natural sampler. It collects skin cells, pollen, fungal spores, bacteria, and bits from pets, food, and outdoor air. If you compare dust from different homes, you may find different microbial patterns. Then you can compare those patterns with survey data about allergy or asthma severity. The main idea is not to prove cause. The goal is to test whether certain microbial signatures appear more often in some homes than others.

Why This Is a Good Topic

This topic is strong because you can turn messy real-world material into measurable data. You can study microbial diversity, compare homes, and link those results to survey responses with statistics. The project connects to indoor air quality, allergy triggers, and environmental health, so the real-world stakes are clear. You can also learn skills that matter in modern biology, like sequence analysis, database use, and careful data cleaning.

Research Questions

• How does household dust microbial diversity differ between homes with and without reported allergy symptoms?
• What is the effect of pet ownership on the relative abundance of major microbial groups in household dust?
• Does reported asthma severity correlate with the proportion of fungal versus bacterial reads in dust samples?
• To what extent does room type, such as bedroom versus living room, change the dust microbiome profile?
• Which microbial taxa are most strongly associated with homes that report frequent allergy symptoms?
• How does cleaning frequency affect alpha diversity in household dust samples?

Basic Materials

• Citizen-science dust collection kit or sterile swabs with collection envelopes.
• Disposable gloves and masks.
• Pre-labeled sample bags or tubes.
• Smartphone or camera for sample tracking.
• Web survey form for allergy and asthma responses.
• Computer with internet access.
• External hard drive or cloud storage for raw sequence files.
• Spreadsheet software for sample metadata.
• Kraken2 database access or a local reference database.
• Basic statistics software or notebook environment.

Advanced Materials

• DNA extraction kit for low-biomass environmental samples.
• Library prep kit for shotgun metagenomic sequencing.
• Access to an Illumina sequencing platform or archived public dust metagenomes.
• Negative extraction controls.
• Mock community standards.
• Bioanalyzer or TapeStation for library quality checks.
• qPCR setup for DNA yield checks.
• High-memory workstation or server for taxonomic classification.
• R or Python environment for downstream analysis.
• Microbiome analysis packages such as phyloseq or vegan.

Software & Tools

• Kraken2: Classifies metagenomic reads against reference databases to estimate taxonomic composition.
• Bracken: Refines Kraken2 output to improve abundance estimates at each taxonomic level.
• QIIME 2: Supports microbiome-style diversity analysis and sample comparisons.
• R: Fits statistical models and makes plots for diversity and survey data.
• Python: Helps you clean metadata, automate file handling, and merge sequence results with survey responses.

Experiment Steps

1. Define the exact question you will test, the sample type you will collect, and the survey fields you need.
2. Plan a metadata sheet that records cleaning habits, pet exposure, room type, and symptom severity in the same format for every home.
3. Choose your analysis path, either fresh sequencing, public metagenomes, or a mix of both, and make sure the comparison groups are balanced.
4. Design controls that catch contamination, batch effects, and sample handling errors before you trust the results.
5. Build a taxonomic pipeline from raw reads to diversity metrics, then decide which summary statistics answer your research question.
6. Set your statistical test plan before looking at the results, so you know how you will compare homes and symptom groups.

Common Pitfalls

• Mixing survey answers from different people in the same home, which makes the symptom data impossible to interpret.
• Skipping negative controls, which lets kit contamination look like a real dust microbe signal.
• Comparing homes with very different sampling effort, which can make one microbiome seem richer just because you sequenced more.
• Using outdated or mismatched Kraken2 databases, which can misclassify common indoor microbes.
• Treating correlation as causation, which can lead you to claim dust microbes cause allergy symptoms when the data only show association.

What Makes This Competitive

A stronger project goes past simple diversity counts. You can compare multiple taxonomic levels, test several symptom groups, and control for confounders like pets, cleaning frequency, and home age. You can also use a clearer statistical plan, such as mixed models or multivariate analysis, instead of only making bar charts. A competitive entry often shows that you understand both the bioinformatics pipeline and the limits of the survey data.

Project Variations

• Compare dust microbiomes from homes with pets versus homes without pets, then test whether the biggest shifts come from bacteria, fungi, or both.
• Swap survey data for a room-by-room comparison, such as bedrooms, kitchens, and living rooms, to see whether indoor niches shape the microbial profile.
• Add an urban versus suburban comparison, then ask whether outdoor exposure seems to influence indoor dust diversity more than reported cleaning habits.

Learn More

• NCBI BioProject and Sequence Read Archive: Search for public metagenomic datasets and download raw reads for reanalysis.
• PubMed: Search for review articles on indoor dust microbiomes, allergies, and asthma.
• NIH Office of Dietary Supplements and related NIH pages: Look for plain-language background on immune health and allergy-related topics.
• NASA GeneLab: Explore how large biological datasets are organized and analyzed in public repositories.
• MIT OpenCourseWare, Computational Biology courses: Use free lecture notes to learn sequence analysis and statistical thinking.
• USDA ARS microbiome resources: Read about microbiome methods and contamination control in environmental samples.