Merge remote-tracking branch 'origin/main'

README.md

@@ -102,3 +102,13 @@ ligo specs.yaml output_folder
 ```
 
 Note that `output_folder` (user-defined name) should not exist before the run.
+
+
+## Citing LIgO
+
+If you are using LIgO in any published work, please cite:
+
+Chernigovskaya, M.; Pavlović, M.; Kanduri, C.; Gielis, S.; Robert, P. A.; Scheffer, L.; Slabodkin, A.; Haff, I. H.; Meysman, P.; Yaari, G.; Sandve, G. K.; Greiff, V
+“Simulation of Adaptive Immune Receptors and Repertoires with Complex Immune Information to Guide the Development and Benchmarking of AIRR Machine Learning” 
+bioRxiv, 2023, 2023.10.20.562936. https://doi.org/10.1101/2023.10.20.562936.
+

docs_source/index.rst

@@ -3,6 +3,8 @@ Welcome to the LIgO documentation!
 
 LIgO is a Python tool for simulation of adaptive immune receptors (AIRs) and repertoires (AIRRs) with known ground-truth immune signals for the development and benchmarking of AIRR-based machine learning. To get started using LigO right now, check out our :doc:`quickstart` tutorial.
 
+.. image:: ./_static/figures/ligo_pipeline.png
+
 Why should you use LIgO?
 ---------------------------------
 * LIgO makes **simulations reproducible**, all simulation parameters are specified through a YAML file. 
@@ -12,22 +14,18 @@ Why should you use LIgO?
 * LIgO **guides the user** and helps to set optimal simulation parameters.
 * LIgO outputs detailed information about presence and position(s) of immune signal(s) for every AIR in AIRR-compliant format.  
 
-**Please check out LIgO manuscript (link will be added soon) for more information!**
+Please **check out LIgO manuscript** `(biorxiv link) <https://www.biorxiv.org/content/10.1101/2023.10.20.562936v2>`_ for more information!
 
+Contents
+---------------------------------
 
 .. toctree::
   :maxdepth: 1
-  :caption: Contents
 
   quickstart
   installation
   tutorials
+  usecases
   specification
 
 
-Indices and tables
-==================
-
-* :ref:`genindex`
-* :ref:`modindex`
-* :ref:`search`

docs_source/installation.rst

@@ -42,13 +42,13 @@ Alternatively, to install LIgO from GitHub run the following:
 
   pip install git+https://github.com/uio-bmi/ligo.git
 
-4. To be able to export full-length sequences, it is necessary to also download the reference data using Stitchr:
+4. To be able to export full-length TCR sequences, it is necessary to also download the reference data using Stitchr:
 
 .. code-block:: console
 
   stitchrdl -s human
 
-For more information on downloading data using Stitchr, see `Stitcher documentation <https://jamieheather.github.io/stitchr/installation.html>`_.
+For more information on downloading data using Stitchr, see `Stitcher documentation <https://jamieheather.github.io/stitchr/installation.html>`_. Once the Stitchr reference data has been downloaded, LigO will automatically include full-length TCR sequences in the output.
 
 
 Use LIgO with Docker