Add BibTeX entry and citation to MSA Transformer paper

Author: ulupo

README.md

@@ -1,5 +1,6 @@
 # Phylogeny-MSA-Transformer
-Supporting repository for "Protein language models trained on multiple sequence alignments learn phylogenetic relationships" (preprint: https://doi.org/10.1101/2022.03.29.486219)
+
+Supporting repository for ["Protein language models trained on multiple sequence alignments learn phylogenetic relationships" (Lupo, Sgarbossa, and Bitbol, 2022)](https://doi.org/10.1101/2022.03.29.486219). The MSA Transformer model used here was introduced in [(Rao el al, 2021)](https://proceedings.mlr.press/v139/rao21a.html).
 
 ## Getting started
 
@@ -15,6 +16,7 @@ python -m pip install -U -r requirements.txt
 ```
 
 ## Requirements
+
 In order to run the notebooks, the following python packages are required:
 
 - tqdm
@@ -26,3 +28,19 @@ In order to run the notebooks, the following python packages are required:
 
 ``prody`` and ``HMMER`` are required to run the Python script ``data/Pfam_Seed/fetch_seed_MSAs.py``, if you wish to create new
 Pfam full MSAs instead of using the ones provided.
+
+### Citation
+
+Our work can be cited using the following BibTeX entry:
+
+```bibtex
+@misc{lupo2022protein,
+      title={Protein language models trained on multiple sequence alignments learn phylogenetic relationships},
+      author={Lupo, Umberto and Sgarbossa, Damiano and Bitbol, Anne-Florence},
+      year={2022},
+      doi={10.1101/2022.03.29.486219},
+      url={https://www.biorxiv.org/content/early/2022/03/30/2022.03.29.486219},
+      eprint={https://www.biorxiv.org/content/early/2022/03/30/2022.03.29.486219.full.pdf},
+      journal={bioRxiv}
+}
+```