--- a
+++ b/README.rst
@@ -0,0 +1,140 @@
+Compgen Course 2025
+===================
+
+Here I collect relevant course material for the Computational Genomics Course (10-16 March 2025), Module 3.
+In this module, we learn about how to use deep learning models to integrate multi-omics data in the context of precision oncology applications.
+
+Info
+===================
+
+
+In this course, we are using two resources to organize the course:
+
+1. **The Google Classroom** for private course-related information, coursework, meeting announcements:
+   `Google Classroom <https://classroom.google.com/c/NzQ5MTExMDU2Njkz>`_
+
+   If you have to share private information such as your email address, please use Google Classroom.
+   For any other problem that you run into, please use the GitHub Discussions (see below).
+
+2. **GitHub discussions** on this repository to provide help to each other:
+   `GitHub Discussions <https://github.com/BIMSBbioinfo/compgen_course_2025_module3/discussions>`_
+
+   We have created different categories for potential issues you may come across.
+   Please try to use the relevant category to open a discussion.
+   Before opening a new discussion topic, please check if something similar is already open.
+   If you know the answer to a question someone else raised, feel free to help your classmates! We appreciate your support.
+
+
+Course Material
+======================
+
+Here is the course material we have developed during the workshop. Feel free to share and re-use. 
+
+1. Day-1: 
+
+   - `Slides <https://docs.google.com/presentation/d/1Z3m8JOQY0JidM7gIJNFWaOfCaTH-rU47y4zCu5Bk6mE/edit?usp=sharing>`_
+   - `Live session <https://youtu.be/7QxRqhFDJiY?feature=shared>`_
+   - `Homework <https://github.com/BIMSBbioinfo/compgen_course_2025_module3/tree/main/homeworks/hw1>`_
+   - `Homework solutions <https://github.com/BIMSBbioinfo/compgen_course_2025_module3/blob/main/solutions/day1_hw_brca_subtypes_solutions.ipynb>`_
+
+2. Day 2: 
+
+   - `Slides <https://docs.google.com/presentation/d/1a31RoNIiZYdZFL9cc4OZ3TpgBGrk1IH1brW9VeHo3dQ/edit?usp=sharing>`_
+   - `Live session <https://youtu.be/CjTjcu_k2EI?feature=shared>`_
+   - `Homework <https://github.com/BIMSBbioinfo/compgen_course_2025_module3/tree/main/homeworks/hw2>`_
+   - `Homework solutions <https://github.com/BIMSBbioinfo/compgen_course_2025_module3/blob/main/solutions/day2_hw_lgg_gbm_solutions.ipynb>`_ 
+
+3. Day 3: 
+
+   - `Slides <https://docs.google.com/presentation/d/1OvXK4H5W7qbD4jeru8pwnkQdiGz0RfjrW4Omd8kd0dg/edit?usp=sharing>`_
+   - `Live session <https://youtu.be/WM4VkjFHOwI?feature=shared>`_
+   - `Homework <https://github.com/BIMSBbioinfo/compgen_course_2025_module3/tree/main/homeworks/hw3>`_
+   - `Homework solutions <https://github.com/BIMSBbioinfo/compgen_course_2025_module3/tree/main/solutions/hw3>`_ 
+
+4. Day 4: 
+
+   - `Live session <https://youtu.be/jYzKw4rF-ck?feature=shared>`_
+   - We didn't use any slides and we didn't have any more homeworks. 
+   
+
+Compute Environment
+===================
+
+Cloud - Rolv.io
+---------------
+
+You will be provided usernames and passwords to access the `rolv.io` platform, which comes with prebuilt packages that you will need throughout the course. With this option, you won't need to install any software yourself.
+
+Please check your email that you signed up for the course, use your credentials to sign in: `Rolv.io Platform <https://platform.dev.cloud.rolv.io/>`_.
+Then, click on **Compute -> Launch -> Launch JupyterLab**. Wait for the session to be ready (takes a few minutes).
+There you will have a JupyterLab environment with all packages installed.
+In this session, you can also use the **terminal**.
+
++++++++++++++++++++++
+
+**Important Note**: Each session you create on Rolv is **limited** to a **total of 3 hours**. 
+Please make sure to **backup your work** before terminating your session. 
+We recommend creating a github repo and have a backup of your work there. 
+
++++++++++++++++++++++
+
+Docker Desktop
+---------------
+
+We have also built a Docker image that contains the tools you will need.
+To be able to use this, you need Docker Desktop, which you can install from here: `Docker Desktop <https://www.docker.com/products/docker-desktop/>`_.
+
+After you install Docker, open a terminal and execute the following code to open a JupyterLab session with all the tools you need:
+
+.. code-block:: bash
+
+   docker pull borauyar/flexynesis_image:latest
+   docker run -it -p 8888:8888 borauyar/flexynesis_image
+   jupyter lab --ip=0.0.0.0 --no-browser --allow-root
+
+This will create a link that looks like this:
+
+   http://127.0.0.1:8888/lab?token=<.......>
+
+Copy-paste that link into your browser to open a JupyterLab session.
+
+Mamba/pip
+---------------
+
+If you want to have more control over your system and you know what you are doing, you can also install **flexynesis** on your system using `pip`.
+
+.. code-block:: bash
+
+   mamba create -n flexenv python==3.11
+   mamba activate flexenv
+   pip install flexynesis jupyterlab snakemake
+   jupyter lab --ip=0.0.0.0 --no-browser --allow-root
+
+This will create a link that looks like this:
+
+   http://127.0.0.1:8888/lab?token=<.......>
+
+Copy-paste that link into your browser to open a JupyterLab session.
+
+Further Learning
+===================
+
+Here are some resource I found useful: 
+
+- Fastai: https://course19.fast.ai/part2
+- Pytorch: https://pytorch.org/tutorials/index.html
+- Lightning: https://www.datacamp.com/tutorial/pytorch-lightning-tutorial
+- Pytorch-Geometric for GNNs: https://pytorch-geometric.readthedocs.io/en/latest/ 
+- Graph Neural Networks: https://www.youtube.com/watch?v=fOctJB4kVlM&list=PLV8yxwGOxvvoNkzPfCx2i8an--Tkt7O8Z&ab_channel=DeepFindr
+- Elements of statistical learning (Rob Tibshirani, Trevor Hastie): https://www.youtube.com/watch?v=LvySJGj-88U&list=PLoROMvodv4rPP6braWoRt5UCXYZ71GZIQ&ab_channel=StanfordOnline
+- Computational Genomics in R (Akalin, Franke, Ronen, Uyar): https://compgenomr.github.io/book/
+
+
+
+
+
+
+
+
+
+