Overview
My master's thesis focused on developing computational methods to analyze complex marine bacterial communities using metagenomic sequencing data. The project combined wet-lab work, bioinformatics, and statistical analysis to understand how bacterial diversity varies across different marine environments.
Research Questions
The thesis addressed several key questions:
- How do bacterial community compositions differ between coastal and open ocean environments?
- What environmental factors most strongly correlate with changes in bacterial diversity?
- Can we identify novel bacterial taxa that are characteristic of specific marine habitats?
- What computational approaches are most effective for analyzing large metagenomic datasets?
Methodology
The research involved multiple phases:
Sample Collection: Water samples were collected from 15 locations across a salinity gradient, from freshwater river mouths to open ocean sites. Environmental parameters (temperature, salinity, pH, nutrient concentrations) were measured at each site.
Laboratory Work: DNA extraction, quality assessment, and library preparation for Illumina sequencing. This phase took approximately 3 months and involved optimization of extraction protocols for different sample types.
Bioinformatics Analysis: Development of a custom analysis pipeline combining existing tools (QIIME2, mothur) with custom Python scripts. The pipeline handled quality filtering, taxonomic assignment, diversity analysis, and statistical testing.
Statistical Analysis: Multivariate statistics (PERMANOVA, NMDS ordination) to identify relationships between environmental variables and bacterial community structure. Machine learning approaches to predict community composition from environmental parameters.
Key Findings
The research yielded several significant findings:
1. Salinity as a primary driver: Salinity emerged as the single strongest predictor of bacterial community composition, explaining approximately 45% of the observed variation. This confirmed previous studies but with higher resolution data.
2. Novel bacterial lineages: We identified 8 previously undescribed bacterial operational taxonomic units (OTUs) that were consistently present across multiple sampling sites. These represent potentially novel species adapted to estuarine environments.
3. Functional redundancy: Despite taxonomic differences, communities from similar environmental conditions showed similar predicted metabolic capabilities, suggesting functional redundancy in bacterial ecosystems.
4. Seasonal stability: Follow-up sampling at 5 sites over 4 seasons showed that core community members (representing ~30% of reads) remained stable, while rare taxa showed high temporal variability.
Technical Contributions
Beyond the biological findings, the thesis made several technical contributions:
- A reproducible analysis pipeline (available on GitHub) that has been used by other lab members
- Custom visualization tools for displaying community composition across environmental gradients
- A database of marine bacterial sequences from the study region
- Documentation and tutorials for future students working with metagenomic data
Challenges and Solutions
The project faced several challenges that required creative solutions:
Challenge 1: Computational resources. Initial analyses on a local workstation were impractically slow. Solution: Migrated pipeline to university HPC cluster, reducing analysis time from weeks to days.
Challenge 2: DNA extraction from low-biomass samples. Open ocean samples had very low bacterial concentrations. Solution: Optimized extraction protocol and increased filtration volumes, though this required additional fieldwork.
Challenge 3: Contamination detection. Some samples showed signs of kit contamination. Solution: Implemented rigorous negative controls and developed statistical methods to identify and remove contaminant sequences.
Publications and Presentations
The thesis research resulted in:
- One first-author publication in a peer-reviewed journal
- Two presentations at international conferences
- Contributions to a collaborative paper on marine microbial diversity
- Open-source code repository with 50+ stars on GitHub
What I Learned
This project was transformative for my development as a researcher. Beyond the technical skills in bioinformatics and molecular biology, I learned:
- The importance of reproducible research practices
- How to manage a long-term project with multiple interconnected components
- The value of open science and sharing methods and data
- How to communicate complex computational methods to biologists and vice versa
- The necessity of flexibility when experiments don't go as planned
Future Directions
The thesis opened several avenues for future research:
- Deeper investigation of the novel bacterial lineages we identified
- Metatranscriptomic analysis to understand active metabolic processes
- Expansion to more geographic locations to test generalizability
- Integration with oceanographic models to predict bacterial distributions
Some of these directions have been pursued in my current research, building directly on the foundations established in this thesis work.