README: Update as per Rizky's suggestions.

README.md

@@ -14,32 +14,33 @@ https://www.biorxiv.org/content/10.1101/2023.08.08.552427v1.full
 
 ## Installation
 
-First clone this repository, using 
+First ensure you have installed git-lfs (including running `git lfs install`), as described at https://www.atlassian.com/git/tutorials/git-lfs#installing-git-lfs
+
+Then clone this repository, using 
 
 ```
-$ git lfs install
-$ git clone https://huggingface.co/wwood/aerobicity
+git clone https://huggingface.co/wwood/aerobicity
 ```
 
 Then setup the conda environment:
 
 ```
-$ cd aerobicity
-$ mamba env create -p env -f env-apply.yml
-$ conda activate ./env
+cd aerobicity
+mamba env create -p env -f env-apply.yml
+conda activate ./env
 ```
 
 and download the eggNOG database. We use version 2.1.3, as specified in the `env-apply.yml` conda environment file, because this is what the predictor was trained on. The eggNOG database is large, so it is not included in the repository. To download it, run:
 
 ```
-$ download_eggnog_data.py 
+download_eggnog_data.py 
 ```
 
 ## 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  
+./17_apply_to_proteome.py --protein-fasta data/RS_GCF_000515355.1_protein.faa --eggnog-data-dir EGGNOG_DATA_DIR  
 --models XGBoost.model --output-predictions predictions.csv
 ```