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WholeLungvBALFluid

The purpose of this repository is to provide public access to the R code and data files used in the following publication:

Baker JM, Hinkle KJ, McDonald RA, Brown CA, Falkowski NR, Huffnagle GB, Dickson RP. Whole lung tissue is the preferred sampling method for amplicon-based characterization of murine lung microbiota. Microbiome 9, 99 (2021). https://doi.org/10.1186/s40168-021-01055-4

Outline of Analytical Approach for Low-Biomass Specimens

  1. Specimen collection and processing
  • Goal: Selection of appropriate numbers and types of negative and positive controls - sample all potential source communities
  • Method: Thoughtful experimental design and sterile specimen collection
  1. Quantification
  • Goal: Confirm sufficient quantities of DNA (bacterial and total) have beeen isolated from low-biomass samples
  • Method: droplet digital PCR or qPCR for bacterial DNA quantification, Qubit or Nanodrop for total DNA quantification
  1. Sequencing, Identification, and Quantification of Bacterial Reads
  • Goal: Amplify, sequence, identify, and quantify bacterial reads in low-biomass specimens
  • Method: PCR amplification, 16S rRNA gene amplicon sequencing, mothur, calculation of read counts per specimen
  1. Diversity of Identified Bacterial Taxa
  • Goal: Determine bacterial signal is distinct from controls and reproducible among biological replicates
  • Method: Calculation of alpha (e.g. rarified richness, Shannon diversity index) and beta (e.g. Bray-Curtis dissimilarity index) diversity metrics
  1. Relative Abundance of Identified Bacterial Taxa
  • Goal: Determine the biological plausibility of bacterial taxa identified in low-biomass samples
  • Method: Principal component analysis and ranked relative abundance of bacterial taxa

Description of Files included in Repository

  • Rmarkdown with code (defaults to final dataset version)
  • Taxonomy file (contains taxonomy assignments for each OTU from mothur)
  • Shared file (contains OTU counts from mothur)
  • 16S rRNA gene ddPCR quantification data
  • Read count data from MiSeq run
  • Knitted reports (raw html and pdf) for untrimmed, trimmed, and final datasets for comparison
  • PDF versions of each figure generated for the final publication, & additional figures that did not make the final publication
  • License information
  • Hidden files (.RData and .Rhistory from local sessions)
  • .gitignore file (ignores cache and other hidden files when pushing directory contents to GitHub)

External Links:

Packages and dependencies required to run code in the repository can be found in session info in the knitted Rmarkdown pdf and html.

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