Update README to reflect current installation and testing

README.md

@@ -1,57 +1,100 @@
 # AAV
 
-Last Updated: 11/20/2023
 
-## Disclaimer
+## Installation
 
-This is not an official PacBio GitHub repo. The scripts here are being actively developed. Use at your own risk.
+You can directly download/clone the repo to use the scripts directly. 
 
+```
+$ git clone https://github.com/formbio/AAV.git
+```
 
-## Pre-requisites
+You can install the dependencies on your own or use one of the following conda-based options.
 
-* Python 3.7
-* R
+### Option 1: Conda
 
-Python libaries required:
-* [pysam](https://anaconda.org/bioconda/pysam)
+The conda (or mamba) channels and dependencies are in the configuration file `AAV.conda_env.yml`. 
 
-R packages required:
-* ggplot2
-* dplyr
-* grid
-* gridExtra
+First, install conda via Miniconda or Anaconda. Then, for example, suppose you have [anaconda](https://docs.anaconda.com/anaconda/install/linux/) installed and the binary is in `$HOME/anaCogentPy37/bin`. To make the installed scripts available in your environment, you would add the binary to $PATH if it isn't there already:
 
-## Installation
+```
+$ export PATH=$HOME/anaCogentPy37/bin:$PATH
+```
 
-You can directly download/clone the repo to use the scripts directly. 
+Next, create a new conda environment called `AAV.env` and install these dependencies from the YAML configuration file:
 
 ```
-$ git clone https://github.com/Magdoll/AAV.git
+$ conda env create -f AAV.conda_env.yml
 ```
 
-You can install the dependencies on your own or use one of the following conda-based options.
+Finally, once installation completes, activate the new environment:
+
+```
+$ source activate AAV.env
+```
+
+At this point the prompt should change to `(AAV.env) $` and the executable AAV scripts should be available in your PATH.
+
+
+### Option 2: Docker
+
+The `Dockerfile` in this repo installs the scripts and all their dependencies into a Docker container image that you can then use to run the scripts.
+
+To build the container image with the name `aavqc` (you can use another name if you prefer):
+
+```
+docker build -t aavqc:latest -f Dockerfile .
+```
 
-### Option1: Use Conda and install the pre-requisites individually
+To run the container in the current working directory:
 
 ```
-conda install -c bioconda pysam
-conda install -c r ggplot2
-conda install -c r dpylr
-conda install -c r grid
-conda install -c r gridExtra
+docker run -v $(pwd):$(pwd) -w $(pwd) -it aavqc:latest bash
 ```
 
-### Option2: Use Conda with yml
+This opens a Bash shell with the scripts in the PATH, and the original working directory mounted in place.
+
+
+### Option 3: Manual installation
+
+The prerequisites to run these scripts include:
+
+* Python 3.7 or later
+* R 3.6 or later
+
+Python packages:
+* [biopython](https://anaconda.org/bioconda/biopython)
+* [pysam](https://anaconda.org/bioconda/pysam)
+* [parasail-python](https://anaconda.org/bioconda/parasail-python)
+
+R packages:
+* tidyverse
+* flextable
+* Rmarkdown
+
+
+## Testing
+
+The `test/` subdirectory in this repo contains a Makefile that can fetch example PacBio datasets from a public server and run the scripts on them to reanalyze them and produce example HTML and PDF reports.
 
-Suppose you have [anaconda](https://docs.anaconda.com/anaconda/install/linux/) installed and the binary is in `$HOME/anaCogentPy37/bin`. You would add the binary to $PATH and create a new conda environment called `AAV.env`.
+Once you've completed installation (above), activate your conda environment or Docker container and change to the test directory:
 
 ```
-$ export PATH=$HOME/anaCogentPy37/bin:$PATH
-$ conda env create -f AAV.conda_env.yml
-$ source activate AAV.env
+cd test
+```
+
+To fetch the public datasets and run the complete analysis (this takes some time):
+
+```
+make slow
+```
+
+Or, to just generate the HTML and PDF reports from the existing intermediate files (this takes about 2 minutes):
+
+```
+make fast
 ```
 
-At this point the prompt should change to `(AAV.env) $`
 
 ## Usage