From 62772d2c066ba5a8ca513e9d49a6879a09903382 Mon Sep 17 00:00:00 2001
From: =?UTF-8?q?Ter=C3=A9zia=20Slanin=C3=A1kov=C3=A1?= <445526@mail.muni.cz>
Date: Tue, 1 Oct 2024 20:42:29 +0200
Subject: [PATCH] improve installation instructions, add CI badge

---
 README.md | 39 ++++++++++++++++++++++++++++++++++-----
 1 file changed, 34 insertions(+), 5 deletions(-)

diff --git a/README.md b/README.md
index 00382a4..2392d9d 100644
--- a/README.md
+++ b/README.md
@@ -9,6 +9,7 @@
 # AlphaFind: Discover structure similarity across the entire known proteome
 
 [![DOI](https://zenodo.org/badge/732580263.svg)](https://zenodo.org/doi/10.5281/zenodo.11085862)
+![GitHub Actions](https://github.com/Coda-Research-Group/AlphaFind/actions/workflows/ci.yml/badge.svg)
 
 **[AlphaFind](https://alphafind.fi.muni.cz)** is a web-based search engine that allows for structure-based search of the entire [AlphaFold Protein Structure Database](https://alphafold.ebi.ac.uk). Uniprot ID, PDB ID, or Gene Symbol is accepted as input – the engine will return the most similar proteins found within AlphaFold DB, with an option for additional search to extend and refine the results. The search results are grouped by their source organism and displayed along with several similarity metrics. 3D visualizations of the structural superposition of the proteins are provided, and text filters can be used to find specific organisms or Uniprot IDs. For details about the methodology and usage, please see the [manual](https://github.com/Coda-Research-Group/AlphaFind/wiki/Manual). This website is free and open to all users and there is no login requirement.
 
@@ -24,19 +25,47 @@ The codebase is divided into three folders:
  
 See the `README.md` files in each folder for more details.
 
-## Installation
+## Installation and execution
 
 Prerequisites / Dependencies:
-- [Docker](https://docs.docker.com/get-docker/)
+- [Docker](https://docs.docker.com/get-docker/) (version 20.10 or later)
+- [Git](https://git-scm.com/book/en/v2/Getting-Started-Installing-Git)
+
+
+### Steps
+
+1. Clone this repository:
+```
+git clone https://github.com/Coda-Research-Group/AlphaFind.git
+```
+2. Add execute permissions to the `run.sh` script:
+```
+chmod +x run.sh
+```
+3. Run `run.sh` in your terminal, which will do the following:
+  - build the docker image for `api/`, `ui/` and `training/`
+  - run the `training/` container to prepare the necessary data structures
+  - run the `api/` container (the backend)
+  - run the `ui/` container (the frontend)
+
+```
+./run.sh
+```
 
-1. Clone this repository
-2. Add execute permissions to the `run.sh` script: `chmod +x run.sh`
-3. Run `./run.sh` in your terminal
 4. Open `http://localhost:8081` in your browser
 
+### Data use
+
 The `training/data/cifs` folder contains a small subset of the AlphaFold DB comprising 109 proteins.
 The full AlphaFold DB can be downloaded from [here](https://alphafold.ebi.ac.uk/download).
 
+To use your own protein data:
+1. Place your .cif files in the `training/data/cifs` directory before running `run.sh`.
+2. Ensure your files follow the naming convention: `AF-[UniProtID]-F1-model_v4.cif`.
+
+For the full AlphaFold DB, download it from [here](https://alphafold.ebi.ac.uk/download) and place the files in the same directory.
+
+
 **Tested on**: Ubuntu 22.04 LTS
 
 ## Cite Us