The convergence of IoT (Internet of Things) with bioinformatics and genomics is opening exciting new frontiers. From environmental DNA (eDNA) monitoring to wearable biosensors and lab automation, IoT enables new forms of data collection, continuous monitoring, and automated analysis. In this article, we’ll explore what this integration means, a detailed use case, benefits & challenges, and how organizations can adopt IoT-powered genomics workflows.
IoT + Genomics & Bioinformatics
Bioinformatics is the field where biological data (especially large-scale “omics” data like genomics, transcriptomics, epigenomics) is collected, stored, analyzed and interpreted. IoT refers to networks of sensors, devices, and connected instruments that autonomously capture data in real time and send it to computational systems for processing.
When combined, IoT provides continuous, context-rich measurements that enhance genomic and bioinformatic analysis. Examples include:
- Wearable biosensors that track physiological markers, microbiome changes, or metabolites over time [14].
- Environmental monitoring (e.g. soil DNA, water eDNA) via autonomous sensors that track biodiversity or pathogen presence [13].
- Automated lab devices—microfluidic chips, sequencing devices, microscopes—connected via IoT for remote monitoring and data collection [15].
Use Case: Environmental DNA (eDNA) Monitoring via IoT
Problem Statement
Monitoring biodiversity or detecting invasive/pathogenic species in ecosystems is crucial for conservation and public health. Traditional sampling has limitations: time lag, manual labour, and low resolution.
Solution
An IoT network of eDNA sensors can autonomously filter water, extract DNA, and amplify genetic markers (via portable PCR). Data streams are sent via wireless networks to a cloud bioinformatics backend where automated pipelines align sequences, identify species, and detect ecological changes [13].
Real-World Example
A recent review of biosensors for eDNA analysis shows how IoT devices can help detect species presence, measure DNA concentration, and track biodiversity shifts in near real-time [13].
Benefits of IoT-Enabled Genomics
- High temporal resolution: Continuous sampling enables detection of rapid changes [13].
- Spatial coverage: Distributed sensors can cover large or remote terrain [14].
- Real-time alerts: Early warning of disease outbreaks or invasive species [13].
- Automation: Reduced human labour and faster data turnaround [15].
- Multi-modal data: Combining genomic with environmental metadata enhances analysis [14].
Challenges & Considerations
- Designing robust sensors with high sensitivity in variable conditions [13].
- DNA contamination risks and false positives [14].
- Large data volumes requiring strong computational infrastructure [15].
- Connectivity and power issues in remote areas.
- Ethical/legal concerns about environmental DNA collection [13].
Conclusion
The integration of IoT with genomics and bioinformatics is transformative. Use cases like eDNA monitoring demonstrate how distributed data collection can improve ecological understanding, accelerate pathogen detection, and support conservation efforts. As IoT hardware, AI, and bioinformatics pipelines advance, real-time genomic insights will become more accessible and impactful.
References
[13] Yoon, H. J., Seo, J. H., Shin, S. H., Abdelhamid, M. A. A., & Pack, S. P. (2025). Bioinformation and Monitoring Technology for Environmental DNA Analysis: A Review. Biosensors, 15(8), 494. https://doi.org/10.3390/bios15080494
[14] Alansari, Z., Anuar, N. B., Kamsin, A., Soomro, S., & Belgaum, M. R. (2019). Evaluation of IoT-Based Computational Intelligence Tools for DNA Sequence Analysis in Bioinformatics. Progress in Advanced Computing and Intelligent Engineering. Springer. https://doi.org/10.1007/978-981-13-0224-4_31
[15] Lee, S. H., et al. (2023). IoT Cloud Laboratory: Internet of Things Architecture for Cellular Biology. PubMed. https://pubmed.ncbi.nlm.nih.gov/37383277/