Update readme and setup.py

Author: AmandaBirmingham

README.md

@@ -7,23 +7,36 @@ Metagenomic Sequencing", Zaramela et al., mSystems 2022)
 ## Overview
 
 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).
+metagenomic shotgun sequencing data that includes synthetic DNA (synDNA) spike-ins by 
+implementing and extending the technique of [Zaramela et al](https://pubmed.ncbi.nlm.nih.gov/36317886/). 
+The calculation is performed in two parts: first, the known 
+spiked-in mass of pooled synDNAs and the known concentrations of each synDNA 
+within the pool are used to calculate the mass of each synDNA sequenced from a given sample. 
+These mass values are paired with the sequenced counts of each synDNA in the sample, and 
+a regression model is fitted to predict mass from counts within that sample 
+as shown in the figure below.
+
+![pySynDNA regression fit workflow](https://raw.githubusercontent.com/biocore/pysyndna/main/docs/absolute_quant_fit_models_workflow.png?raw=true)
+
+The counts for each microbial genome in the sample are then translated into 
+masses via the regression model, and the masses are converted to genome counts 
+using genome lengths and Avogadro's number 
+(see Zaramela et al [equation 2](https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9765022/#FD2)).
+Assuming approximately one genome per cell, the calculated genome counts are
+treated as approximate cell counts.  pySynDNA extends this approach further by 
+using the known starting mass of each sample to calculate the approximate cell 
+counts per gram of each microbe in the input sample material (before gDNA extraction).
+These calculations are outlined in the figure below.
+
+![pySynDNA OGU cell counts workflow](https://raw.githubusercontent.com/biocore/pysyndna/main/docs/absolute_quant_calc_cell_counts_workflow.png?raw=true)
+
 
 ## Installation
 
 To install this package, first clone the repository from GitHub:
 
 ```
-git clone https://github.com/AmandaBirmingham/pysyndna.git
+git clone https://github.com/biocore/pysyndna.git
 ```
 
 Change directory into the new `pysndna` folder and create a 

setup.py

@@ -16,10 +16,10 @@
                        "Quantification of Shotgun Metagenomic Sequencing', "
                        "Zaramela et al., mSystems 2022)",
       license='BSD-3-Clause',
-      description='Python implementation of the SynDNA algorithm',
+      description='Python implementation of absolute cell count estimation algorithm',
       author="Amanda Birmingham",
       author_email="abirmingham@ucsd.edu",
-      url='https://github.com/AmandaBirmingham/pysyndna',
+      url='https://github.com/biocore/pysyndna',
       packages=find_packages(),
       include_package_data=True,
       package_data={