Quickstart ========== Installation ------------ Polaris is developed and tested on Linux machines with python3.9 and relies on several libraries including pytorch, scipy, etc. We ​**strongly recommend** that you install Polaris in a virtual environment. We suggest users using `conda `_ to create a virtual environment for it (It should also work without using conda, i.e. with pip). You can run the command snippets below to install Polaris: .. code-block:: bash git clone https://github.com/ai4nucleome/Polaris.git cd Polaris conda create -n polaris python=3.9 conda activate polaris ------- Install Polaris: .. code-block:: bash ./setup.sh It will automatically download Polaris model's weights from `Hugging Face `_ and install Polaris. You can also download model's weights file manually from `there `_ and put it in ``Polaris/polaris/model`` and change the file name to ``sft_loop.pt``. The installation requires network access to download libraries. Usually, the installation will finish within 3 minutes. The installation time is longer if network access is slow and/or unstable. Quick Usage ----------- **See** `Jupyter Notebook CLI walkthrough `_ **and the** `CLI Reference `_ **for more information.** Polaris takes submatrices of contact map as input and outputs predicted loops. .. code-block:: bash polaris loop pred -i [input mcool file] -o [output path of annotated loops] Output format: It contains tab separated fields as follows: .. csv-table:: :header: "Field", "Detail" :widths: 20, 30 "Chr1/Chr2", "chromosome names" "Start1/Start2", "start genomic coordinates" "End1/End2", "end genomic coordinates (i.e. End1=Start1+resol)" "Score", "Polaris's loop score [0~1]"