readme: update bib entry and links - GENA in NAR

README.md

@@ -19,7 +19,7 @@ Differences between GENA-LM (`gena-lm-bigbird-base-sparse-t2t`) and DNABERT:
 
 Source code and data: https://github.com/AIRI-Institute/GENA_LM
 
-Paper: https://www.biorxiv.org/content/10.1101/2023.06.12.544594v1
+Paper: https://academic.oup.com/nar/article/53/2/gkae1310/7954523
 
 ## Installation
 `gena-lm-bigbird-base-sparse-t2t` sparse ops require DeepSpeed.
@@ -103,19 +103,22 @@ GENA-LM (`gena-lm-bigbird-base-sparse-t2t`) model is trained in a masked languag
 We pre-trained `gena-lm-bigbird-base-sparse-t2t` using the latest T2T human genome assembly (https://www.ncbi.nlm.nih.gov/assembly/GCA_009914755.3/). The data was augmented by sampling mutations from 1000-genome SNPs (gnomAD dataset). Pre-training was performed for 800,000 iterations with batch size 256. We modified Transformer with [Pre-Layer normalization](https://arxiv.org/abs/2002.04745).
 
 ## Evaluation
-For evaluation results, see our paper: https://www.biorxiv.org/content/10.1101/2023.06.12.544594v1
+For evaluation results, see our paper: https://academic.oup.com/nar/article/53/2/gkae1310/7954523
 
 ## Citation
 ```bibtex
 @article{GENA_LM,
-	author = {Veniamin Fishman and Yuri Kuratov and Maxim Petrov and Aleksei Shmelev and Denis Shepelin and Nikolay Chekanov and Olga Kardymon and Mikhail Burtsev},
-	title = {GENA-LM: A Family of Open-Source Foundational Models for Long DNA Sequences},
-	elocation-id = {2023.06.12.544594},
-	year = {2023},
-	doi = {10.1101/2023.06.12.544594},
-	publisher = {Cold Spring Harbor Laboratory},
-	URL = {https://www.biorxiv.org/content/early/2023/06/13/2023.06.12.544594},
-	eprint = {https://www.biorxiv.org/content/early/2023/06/13/2023.06.12.544594.full.pdf},
-	journal = {bioRxiv}
+    author = {Fishman, Veniamin and Kuratov, Yuri and Shmelev, Aleksei and Petrov, Maxim and Penzar, Dmitry and Shepelin, Denis and Chekanov, Nikolay and Kardymon, Olga and Burtsev, Mikhail},
+    title = {GENA-LM: a family of open-source foundational DNA language models for long sequences},
+    journal = {Nucleic Acids Research},
+    volume = {53},
+    number = {2},
+    pages = {gkae1310},
+    year = {2025},
+    month = {01},
+    issn = {0305-1048},
+    doi = {10.1093/nar/gkae1310},
+    url = {https://doi.org/10.1093/nar/gkae1310},
+    eprint = {https://academic.oup.com/nar/article-pdf/53/2/gkae1310/61443229/gkae1310.pdf},
 }
 ```