We study how microorganisms — particularly fungi — shape and are shaped by biogeochemical processes in tree-dominated terrestrial ecosystems, from forest soils to foliage in urban green spaces. This organization hosts the code, analysis pipelines, and (where licensing permits) data associated with our published and in-progress work.
PI: Dr. Jennifer Bhatnagar, Associate Professor of Biology, Boston University
Lab website: https://microbesatbu.wordpress.com/
Contact: [email protected]
Our research integrates microbial ecology, soil biogeochemistry, -omics, and global change biology. Current and recent themes include:
- Mycorrhizal ecology and biogeochemistry — community assembly, function, and biogeography of ectomycorrhizal and arbuscular mycorrhizal fungi across forested and urban systems.
- Global change science — microbial controls on carbon and nitrogen cycling, including responses to elevated CO₂, nitrogen deposition, warming, and wildfire.
- Urban ecology — microbiomes of street trees, urban wilds, and restoration plantings (including Miyawaki-style mini-forests).
- Computational and AI methods in ecology — integration of -omics with community-level ODE models and biogeochemical process models.
Most repositories here are tied to a specific manuscript, dataset, or methods development effort. See pinned repositories below for entry points.
To keep things navigable, repositories in this organization generally follow these conventions:
Naming. Project repos are named for either the project_manuscript short title_journal_year (e.g., "UNE_soil_microbiome_PNAS_2023") or the analytical tool (e.g., "FUN2FITS"). Forks and teaching materials are prefixed "fork-" or "teaching-".
Structure. Analysis repos typically contain:
├── README.md # Project description, citation, data availability
├── data/ # Raw or processed data (or pointers to archived data)
├── code/ or scripts/ # Analysis scripts, organized by figure/table or pipeline step
├── results/ or output/ # Generated figures, tables, intermediate files
└── env/ or renv/ # Environment specification (conda, renv, Docker)
Languages. Most analyses are written in R (tidyverse, vegan, phyloseq, DESeq2) or Python (pandas, scikit-learn, biopython). Amplicon and metagenomic pipelines typically use QIIME2, DADA2, or Nextflow-based workflows. Bash and Snakemake appear for HPC pipelines run on BU's SCC.
You are welcome to use, adapt, and build on code in this organization, subject to the license of each individual repository. If you use our code or derived data in published work, please:
- Cite the associated manuscript (linked in the relevant repo's README).
- Cite the code/data archive DOI (Zenodo) where one exists.
- Note the specific commit or release tag you used.
If you find a bug, have a question about a method, or want to flag a reproducibility issue, please open an issue on the relevant repository rather than emailing directly — that way the answer is available to others as well.
Members of the lab who contribute here include graduate students, postdoctoral researchers, undergraduates, and collaborators. See the lab website for the current roster and individual project pages.
Work in this organization has been supported by NSF, DOE, and foundations — including the Keck Foundation and the Leap of Faith Corporation, Inc.
For scientific inquiries, collaboration proposals, or questions about specific repositories, please contact [email protected]. Prospective graduate students should consult the lab website for application guidance before reaching out.