Merge pull request #1 from chill868686/chill868686-patch-1

Update README.md

README.md

@@ -1,2 +1,116 @@
-# adaptive-coder
-DNA storage adaptive coder using neural network
+# Adaptive Coder
+
+Transform digital data to ATCG sequences for DNA storage in high logical density,
+while output sequences comply with arbitrary user-defined constraints.
+
+
+## First time setup
+
+The following steps are required in order to run Adaptive Coder:
+
+1.  Install [Docker](https://www.docker.com/).
+    *   Install
+        [NVIDIA Container Toolkit](https://docs.nvidia.com/datacenter/cloud-native/container-toolkit/install-guide.html)
+        for GPU support.
+    *   Setup running
+        [Docker as a non-root user](https://docs.docker.com/engine/install/linux-postinstall/#manage-docker-as-a-non-root-user).
+1.  Check GPUs are avaliable by running:
+
+    ```bash
+    docker run --rm --gpus all nvidia/cuda:11.0-base nvidia-smi
+    ```
+
+    The output of this command should show a list of your GPUs.
+
+## Running Adaptive Coder
+
+**The simplest way to run Adaptive Coder is using the provided Docker script.** This
+was tested with 20 vCPUs, 64 GB of RAM, and a 3090 GPU.
+
+1.  Clone this repository and `cd` into it.
+
+    ```bash
+    git clone https://github.com/chill868686/adaptive-coder.git
+    ```
+
+1.  Build the Docker image:
+
+    ```bash
+    docker build -f docker/Dockerfile -t adacoder .
+    ```
+
+1.  Install the `run_docker.py` dependencies. Note: You may optionally wish to
+    create a
+    [Python Virtual Environment](https://docs.python.org/3/tutorial/venv.html)
+    to prevent conflicts with your system's Python environment.
+
+    ```bash
+    pip3 install -r docker/requirements.txt
+    ```
+
+1.  Make sure that the output directory exists (the default is `/tmp/alphafold`)
+    and that you have sufficient permissions to write into it. You can make sure
+    that is the case by manually running `mkdir /tmp/alphafold` and
+    `chmod 770 /tmp/alphafold`.
+
+1.  Run `run_docker.py` pointing to a FASTA file containing the protein
+    sequence(s) for which you wish to predict the structure. If you are
+    predicting the structure of a protein that is already in PDB and you wish to
+    avoid using it as a template, then `max_template_date` must be set to be
+    before the release date of the structure. You must also provide the path to
+    the directory containing the downloaded databases. For example, for the
+    T1050 CASP14 target:
+
+    ```bash
+    python3 docker/run_docker.py \
+      --fasta_paths=T1050.fasta \
+      --max_template_date=2020-05-14 \
+      --data_dir=$DOWNLOAD_DIR
+    ```
+
+    By default, Alphafold will attempt to use all visible GPU devices. To use a
+    subset, specify a comma-separated list of GPU UUID(s) or index(es) using the
+    `--gpu_devices` flag. See
+    [GPU enumeration](https://docs.nvidia.com/datacenter/cloud-native/container-toolkit/user-guide.html#gpu-enumeration)
+    for more details.
+
+1.  You can control which AlphaFold model to run by adding the
+    `--model_preset=` flag. We provide the following models:
+
+    * **monomer**: This is the original model used at CASP14 with no ensembling.
+
+    * **monomer\_casp14**: This is the original model used at CASP14 with
+      `num_ensemble=8`, matching our CASP14 configuration. This is largely
+      provided for reproducibility as it is 8x more computationally
+      expensive for limited accuracy gain (+0.1 average GDT gain on CASP14
+      domains).
+
+    * **monomer\_ptm**: This is the original CASP14 model fine tuned with the
+      pTM head, providing a pairwise confidence measure. It is slightly less
+      accurate than the normal monomer model.
+
+    * **multimer**: This is the [AlphaFold-Multimer](#citing-this-work) model.
+      To use this model, provide a multi-sequence FASTA file. In addition, the
+      UniProt database should have been downloaded.
+
+1.  You can control MSA speed/quality tradeoff by adding
+    `--db_preset=reduced_dbs` or `--db_preset=full_dbs` to the run command. We
+    provide the following presets:
+
+    *   **reduced\_dbs**: This preset is optimized for speed and lower hardware
+        requirements. It runs with a reduced version of the BFD database.
+        It requires 8 CPU cores (vCPUs), 8 GB of RAM, and 600 GB of disk space.
+
+    *   **full\_dbs**: This runs with all genetic databases used at CASP14.
+
+    Running the command above with the `monomer` model preset and the
+    `reduced_dbs` data preset would look like this:
+
+    ```bash
+    python3 docker/run_docker.py \
+      --fasta_paths=T1050.fasta \
+      --max_template_date=2020-05-14 \
+      --model_preset=monomer \
+      --db_preset=reduced_dbs \
+      --data_dir=$DOWNLOAD_DIR
+    ```