Inspiration
Life in Orbit was inspired by the growing need to understand how spaceflight impacts human health and microbiomes. As humanity prepares for longer missions, from lunar bases to Mars exploration, monitoring life at the microbial and genetic level becomes critical. We wanted to build a tool that bridges biology and space science to ensure astronaut safety.
What it does
Our project analyzes biological sequencing data from spaceflight and compares it to Earth-based samples. It highlights how factors like environment, temperature, and space conditions affect DNA and RNA readouts. The system provides clear visualizations and metrics so scientists can track microbial activity, human read contamination, and overall sequencing quality across different phases of a mission.
How we built it
We integrated raw sequencing metadata, cleaned and standardized it, and applied preprocessing pipelines to extract key metrics such as read depth, read length, and percentage of human reads removed. Using Python with Pandas and Matplotlib, we built scripts to automate data cleaning and generate comparative visualizations. This let us explore results by spaceflight stage, host sex, and sample location.
Challenges we ran into
We faced difficulties handling inconsistent metadata — values like “Room Temperature” or “Not Applicable” required custom parsing. Another challenge was balancing DNA (x) and RNA (y) sequencing data while keeping visualizations intuitive. Finally, scaling the plots to highlight both small and very large read depths without losing detail required careful adjustments.
Accomplishments that we're proud of
We’re proud that we created a fully automated workflow that turns raw CSV sequencing metadata into high-quality, mission-ready plots. The pipeline successfully differentiates microbial vs. human contamination and provides side-by-side insights into DNA vs. RNA sequencing results.
What we learned
We learned the importance of robust data cleaning when working with space biology datasets. Sequencing data can vary widely, so flexible scripts are essential. We also deepened our understanding of how sequencing metrics (read depth, read length, contamination rates) impact downstream biological interpretation, especially in a spaceflight context.
What's next for Life in Orbit
We plan to expand Life in Orbit into a full dashboard with interactive visualizations. Future work includes integrating machine learning to detect anomalies automatically, supporting more data types (proteomics, metabolomics), and collaborating with space agencies to test the system on real astronaut missions.
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