This repository contains scripts and documentation from a graduate school RB-TnSeq (Random Barcode Transposon Sequencing) project using a mutant pool of the methylotroph Methylotenera sp. JLW8 in planktonic co-culture experiments with the methanotroph Methylomonas sp. LW13.
The repository is maintained as an archive of the analysis workflow used for BarSeq count processing, exploratory data analysis, and gene fitness estimation. Raw sequencing data, count tables, and metadata are not included. Scripts are preserved primarily for reference and may require modification before reuse.
Methane-consuming bacteria (methanotrophs) play an important role in the environment by removing methane, a potent greenhouse gas, from the atmosphere. In nature, methanotrophs rarely exist alone and instead form communities with other bacteria that depend on the compounds produced during methane consumption.
Previous work showed that the methylotroph Methylotenera sp. JLW8 forms a close association with the methanotroph Methylomonas sp. LW13 and grows much better when paired with LW13 than when grown alone, likely due to cross-feeding of methanol and other compounds produced during methane oxidation. However, many other heterotrophic and methylotrophic bacteria coexist in the same environment and have access to similar resources, yet do not form the same strong association with LW13. This observation suggested that additional genetic factors may contribute to the specificity of the interaction and motivated efforts to identify the genes involved.
The goal of this project was to identify the genes in JLW8 that contribute to its association with LW13. To accomplish this, a barcoded transposon mutant library of JLW8 was generated by the Karasov Lab at the University of Utah and analyzed under co-culture conditions with LW13 using RB-TnSeq. By measuring the fitness of thousands of JLW8 mutants simultaneously, we aimed to identify genes that become important when JLW8 grows alongside the methanotroph.
More broadly, this work sought to better understand the genetic basis of interactions within methane-oxidizing microbial communities and the factors that allow different community members to coexist.
data_exploration.Rmd
Exploratory analysis of BarSeq gene count data, including
quality control, clustering, PCA, heatmaps, and DESeq2-based
differential abundance analyses.
FEBA.R
Main RB-TnSeq gene fitness analysis workflow.
FEBA/
Experiment-specific FEBA analyses for individual datasets.
loop.sh
Shell script used during secondary barcode demultiplexing.
demultiplexing_strategy.md
Documentation of the dual-index BarSeq library design,
barcode structure, demultiplexing workflow, and required
software tools.
README.md
Repository documentation.
- Demultiplex sequencing reads (loop.sh)
- Generate gene count table
- Explore count data (data_exploration.Rmd)
- Estimate gene fitness (FEBA.R)
Raw sequencing reads, count tables, and metadata are not included in this repository. File paths in the scripts reflect the original local or cluster environment and may need to be modified before reuse.
This project was conducted as part of my PhD research and is preserved as an archive of exploratory analyses and workflow development. The work was not developed into a standalone publication but is shared as an example of RB-TnSeq data analysis in R and bash. For additional information regarding the underlying biological questions, experimental methods, or original datasets, please contact Aaron Puri at the University of Utah.