README: Update it.

README.md

@@ -7,6 +7,12 @@ library_name: sklearn
 
 Prediction of aerobicity (whether an bacteria or archaeon is aerobic) based on gene copy numbers. The prediction problem is posed as a 2-class problem (the prediction is either aerobic or anaerobic).
 
+This predictor was used in this (currently pre-publication) manuscript, please cite it if appropriate:
+
+Davin, Adrian A., Ben J. Woodcroft, Rochelle M. Soo, Ranjani Murali, Dominik Schrempf, James Clark, Bastien Boussau et al. "An evolutionary timescale for Bacteria calibrated using the Great Oxidation Event." bioRxiv (2023): 2023-08.
+https://www.biorxiv.org/content/10.1101/2023.08.08.552427v1.full
+
+## Installation
 To apply the predictor, first setup the conda environment:
 
 ```
@@ -15,13 +21,14 @@ $ mamba env create -p env -f env-apply.yml
 $ conda activate ./env
 ```
 
-and download the eggNOG database (we use version 2.1.3 for repro)
+and download the eggNOG database (we use version 2.1.3, as specified in the `env-apply.yml` conda environment file)
 
 ```
 $ download_eggnog_data.py 
 ```
 
-Then, to apply the predictor, run against a test genome, replacing `EGGNOG_DATA_DIR` with the path to the eggNOG data directory:
+## Usage
+To apply the predictor, run against a test genome, replacing `EGGNOG_DATA_DIR` with the path to the eggNOG data directory:
 
 ```
 $ ./17_apply_to_proteome.py --protein-fasta data/RS_GCF_000515355.1_protein.faa --eggnog-data-dir EGGNOG_DATA_DIR