A python implementation and extension of SynDNA ("a Synthetic DNA Spike-in Method for Absolute Quantification of Shotgun Metagenomic Sequencing", Zaramela et al., mSystems 2022)
The package provides an API for generating absolute cell count estimates from metagenomic sequencing data that includes synthetic DNA (synDNA) spike-ins. The general approach, as outlined in Zaramela et al., is to use counts of specific synDNAs present in known masses to fit a linear regression model for each sample that predicts the gDNA mass of a sequence based on its counts in that sample. These linear models are subsequently applied to the counts of microbial genomes in each sample to calculate the mass of each such genome, and those masses are then transformed into the number of instances of each genome (that is, the approximate cell count of that microbe) in the input gDNA. pySynDNA extends this approach by also calculating the approximate cell count of each microbe in the input sample material (before gDNA extraction).
To install this package, first clone the repository from GitHub:
git clone https://github.com/AmandaBirmingham/pysyndna.git
Change directory into the new pysndna folder and create a
Python3 Conda environment in which to run the software:
conda env create -n pysyndna -f environment.yml
Activate the Conda environment and install the package:
conda activate pysyndna
pip install -e .
First, calculate linear regression models per sample by calling the
fit_linear_regression_models function in the pysyndna module
(or, for qiita-based usage, the fit_linear_regression_models_for_qiita
function).
Second, apply these models to counts of microbial genomes in the
samples by calling the calc_ogu_cell_counts_biom function in the
pysyndna module, specifying whether to return the cell counts
per gram of gDNA or the cell counts per gram of un-extracted sample material
(the latter is usually the more relevant metric). For qiita-based usage,
call the calc_ogu_cell_counts_per_g_of_sample_for_qiita function (which
always returns the cell counts per gram of un-extracted sample material.)
The documentation for these functions offer details on the specifics of their parameters, and the unit tests provide functional examples of inputs and outputs.