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	author = {Harder, Tim and Boomsma, Wouter and Paluszewski, Martin and Frellsen, Jes and Johansson, Kristoffer E. and Hamelryck, Thomas},
	year = {2010},
	month = {06},
	date = {2010-06-05},
	journal = {BMC bioinformatics},
	pages = {306},
	volume = {11},
	doi = {10.1186/1471-2105-11-306},
	note = {PMID: 20525384
PMCID: PMC2902450},
	langid = {eng}
}

@article{janson2021,
	title = {PyMod 3: a complete suite for structural bioinformatics in PyMOL},
	author = {Janson, G. and Paiardini, A.},
	year = {2021},
	month = {06},
	date = {2021-05-15},
	journal = {Bioinformatics},
	pages = {1471-1472},
	volume = {37},
	doi = {10.1093/bioinformatics/btaa849},
	note = {PMID: 33010156},
	langid = {eng}
}

@article{burley2021,
    author = {Burley, Stephen K and Bhikadiya, Charmi and Bi, Chunxiao and Bittrich, Sebastian and Chen, Li and Crichlow, Gregg V and Christie, Cole H and Dalenberg, Kenneth and Di Costanzo, Luigi and Duarte, Jose M and Dutta, Shuchismita and Feng, Zukang and Ganesan, Sai and Goodsell, David S and Ghosh, Sutapa and Green, Rachel Kramer and Guranović, Vladimir and Guzenko, Dmytro and Hudson, Brian P and Lawson, Catherine L and Liang, Yuhe and Lowe, Robert and Namkoong, Harry and Peisach, Ezra and Persikova, Irina and Randle, Chris and Rose, Alexander and Rose, Yana and Sali, Andrej and Segura, Joan and Sekharan, Monica and Shao, Chenghua and Tao, Yi-Ping and Voigt, Maria and Westbrook, John D and Young, Jasmine Y and Zardecki, Christine and Zhuravleva, Marina},
    title = "{RCSB Protein Data Bank: powerful new tools for exploring 3D structures of biological macromolecules for basic and applied research and education in fundamental biology, biomedicine, biotechnology, bioengineering and energy sciences}",
    journal = {Nucleic Acids Research},
    volume = {49},
    number = {D1},
    pages = {D437-D451},
    year = {2020},
    month = {11},
    abstract = "{The Research Collaboratory for Structural Bioinformatics Protein Data Bank (RCSB PDB), the US data center for the global PDB archive and a founding member of the Worldwide Protein Data Bank partnership, serves tens of thousands of data depositors in the Americas and Oceania and makes 3D macromolecular structure data available at no charge and without restrictions to millions of RCSB.org users around the world, including \\>660 000 educators, students and members of the curious public using PDB101.RCSB.org. PDB data depositors include structural biologists using macromolecular crystallography, nuclear magnetic resonance spectroscopy, 3D electron microscopy and micro-electron diffraction. PDB data consumers accessing our web portals include researchers, educators and students studying fundamental biology, biomedicine, biotechnology, bioengineering and energy sciences. During the past 2 years, the research-focused RCSB PDB web portal (RCSB.org) has undergone a complete redesign, enabling improved searching with full Boolean operator logic and more facile access to PDB data integrated with \\>40 external biodata resources. New features and resources are described in detail using examples that showcase recently released structures of SARS-CoV-2 proteins and host cell proteins relevant to understanding and addressing the COVID-19 global pandemic.}",
    issn = {0305-1048},
    doi = {10.1093/nar/gkaa1038},
    url = {https://doi.org/10.1093/nar/gkaa1038},
    eprint = {https://academic.oup.com/nar/article-pdf/49/D1/D437/35364241/gkaa1038.pdf},
}

@article{song2013,
title = {High-Resolution Comparative Modeling with RosettaCM},
journal = {Structure},
volume = {21},
number = {10},
pages = {1735-1742},
year = {2013},
issn = {0969-2126},
doi = {https://doi.org/10.1016/j.str.2013.08.005},
url = {https://www.sciencedirect.com/science/article/pii/S0969212613002979},
author = {Yifan Song and Frank DiMaio and Ray Yu-Ruei Wang and David Kim and Chris Miles and TJ Brunette and James Thompson and David Baker},
abstract = {Summary
We describe an improved method for comparative modeling, RosettaCM, which optimizes a physically realistic all-atom energy function over the conformational space defined by homologous structures. Given a set of sequence alignments, RosettaCM assembles topologies by recombining aligned segments in Cartesian space and building unaligned regions de novo in torsion space. The junctions between segments are regularized using a loop closure method combining fragment superposition with gradient-based minimization. The energies of the resulting models are optimized by all-atom refinement, and the most representative low-energy model is selected. The CASP10 experiment suggests that RosettaCM yields models with more accurate side-chain and backbone conformations than other methods when the sequence identity to the templates is greater than ∼15%.}
}

@article{kelley2015,
	title = {The Phyre2 web portal for protein modeling, prediction and analysis},
	author = {Kelley, L. A. and Mezulis, S. and Yates, C. M. and Wass, M. N. and Sternberg, M. J.},
	year = {2015},
	month = {06},
	date = {2015-06},
	journal = {Nat Protoc},
	pages = {845--58},
	volume = {10},
	number = {6},
	doi = {10.1038/nprot.2015.053}
}

@book{rigden2017,
	title = {From Protein Structure to Function with Bioinformatics},
	author = {Rigden, D.J.},
	year = {2017},
	date = {2017},
	publisher = {Springer Netherlands},
	url = {https://books.google.hn/books?id=l4LynAAACAAJ},
	note = {LCCN: 2017932416}
}

@book{rigden2017a,
	title = {From Protein Structure to Function with Bioinformatics},
	author = {Rigden, D.J.},
	year = {2017},
	date = {2017},
	publisher = {Springer Netherlands},
	url = {https://books.google.hn/books?id=l4LynAAACAAJ},
	note = {LCCN: 2017932416}
}

@article{atkins2015,
	title = {Disorder Prediction Methods, Their Applicability to Different Protein Targets and Their Usefulness for Guiding Experimental Studies},
	author = {Atkins, Jennifer D. and Boateng, Samuel Y. and Sorensen, Thomas and McGuffin, Liam J.},
	year = {2015},
	month = {08},
	date = {2015-08-13},
	journal = {International Journal of Molecular Sciences},
	pages = {19040--19054},
	volume = {16},
	number = {8},
	doi = {10.3390/ijms160819040},
	note = {PMID: 26287166
PMCID: PMC4581285},
	langid = {eng}
}

@inbook{kelley2009a,
	title = {Fold Recognition},
	author = {Kelley, Lawrence A.},
	editor = {Rigden, Daniel John},
	year = {2009},
	date = {2009},
	publisher = {Springer Netherlands},
	pages = {27--55},
	doi = {10.1007/978-1-4020-9058-5_2},
	url = {https://doi.org/10.1007/978-1-4020-9058-5_2},
	note = {DOI: 10.1007/978-1-4020-9058-5{\_}2},
	address = {Dordrecht},
	langid = {en}
}

@article{quaglia2022,
	title = {DisProt in 2022: improved quality and accessibility of protein intrinsic disorder annotation},
	author = {Quaglia, Federica and {Mészáros}, {Bálint} and Salladini, Edoardo and Hatos, {András} and Pancsa, Rita and Chemes, {Lucía B.} and Pajkos, {Mátyás} and Lazar, Tamas and {Peña-Díaz}, Samuel and Santos, Jaime and {Ács}, Veronika and Farahi, Nazanin and {Fichó}, {Erzsébet} and Aspromonte, Maria Cristina and Bassot, Claudio and Chasapi, Anastasia and Davey, Norman E. and {Davidovi{\'{c}}}, Radoslav and Dobson, Laszlo and Elofsson, Arne and {Erd{{\H o}}s}, {Gábor} and Gaudet, Pascale and Giglio, Michelle and Glavina, Juliana and Iserte, Javier and Iglesias, {Valentín} and {Kálmán}, {Zsófia} and Lambrughi, Matteo and Leonardi, Emanuela and Longhi, Sonia and Macedo-Ribeiro, Sandra and Maiani, Emiliano and Marchetti, Julia and Marino-Buslje, Cristina and {Mészáros}, Attila and Monzon, Alexander Miguel and Minervini, Giovanni and Nadendla, Suvarna and Nilsson, Juliet F. and {Novotný}, Marian and Ouzounis, Christos A. and Palopoli, {Nicolás} and Papaleo, Elena and Pereira, {Pedro José Barbosa} and Pozzati, Gabriele and Promponas, Vasilis J. and Pujols, Jordi and Rocha, Alma Carolina Sanchez and Salas, Martin and Sawicki, Luciana Rodriguez and Schad, Eva and Shenoy, Aditi and {Szaniszló}, {Tamás} and Tsirigos, Konstantinos D. and Veljkovic, Nevena and Parisi, Gustavo and Ventura, Salvador and {Dosztányi}, Zsuzsanna and Tompa, Peter and Tosatto, Silvio C. E. and Piovesan, Damiano},
	year = {2022},
	month = {01},
	date = {2022-01-07},
	journal = {Nucleic Acids Research},
	pages = {D480--D487},
	volume = {50},
	number = {D1},
	doi = {10.1093/nar/gkab1082},
	note = {PMID: 34850135
PMCID: PMC8728214},
	langid = {eng}
}

@article{hallgren,
	title = {DeepTMHMM predicts alpha and beta transmembrane proteins using deep neural networks},
	author = {Hallgren, Jeppe and Tsirigos, Konstantinos D. and Pedersen, Mads Damgaard and Armenteros, {José Juan Almagro} and Marcatili, Paolo and Nielsen, Henrik and Krogh, Anders and Winther, Ole},
	year = {2022},
	doi = {10.1101/2022.04.08.487609},
	langid = {en}
}

@article{studer2021,
	title = {ProMod3-A versatile homology modelling toolbox},
	author = {Studer, Gabriel and Tauriello, Gerardo and Bienert, Stefan and Biasini, Marco and Johner, Niklaus and Schwede, Torsten},
	year = {2021},
	month = {01},
	date = {2021-01},
	journal = {PLoS computational biology},
	pages = {e1008667},
	volume = {17},
	number = {1},
	doi = {10.1371/journal.pcbi.1008667},
	note = {PMID: 33507980
PMCID: PMC7872268},
	langid = {eng}
}

@article{wang2017a,
	title = {Accurate De Novo Prediction of Protein Contact Map by Ultra-Deep Learning Model},
	author = {Wang, Sheng and Sun, Siqi and Li, Zhen and Zhang, Renyu and Xu, Jinbo},
	year = {2017},
	month = {01},
	date = {2017-01},
	journal = {PLoS computational biology},
	pages = {e1005324},
	volume = {13},
	number = {1},
	doi = {10.1371/journal.pcbi.1005324},
	note = {PMID: 28056090
PMCID: PMC5249242},
	langid = {eng}
}

@article{jones2015a,
	title = {MetaPSICOV: combining coevolution methods for accurate prediction of contacts and long range hydrogen bonding in proteins},
	author = {Jones, David T. and Singh, Tanya and Kosciolek, Tomasz and Tetchner, Stuart},
	year = {2015},
	month = {04},
	date = {2015-04-01},
	journal = {Bioinformatics (Oxford, England)},
	pages = {999--1006},
	volume = {31},
	number = {7},
	doi = {10.1093/bioinformatics/btu791},
	note = {PMID: 25431331
PMCID: PMC4382908},
	langid = {eng}
}

@article{bittrich2019,
	title = {StructureDistiller: Structural relevance scoring identifies the most informative entries of a contact map},
	author = {Bittrich, Sebastian and Schroeder, Michael and Labudde, Dirk},
	year = {2019},
	month = {12},
	date = {2019-12-06},
	journal = {Scientific Reports},
	pages = {18517},
	volume = {9},
	number = {1},
	doi = {10.1038/s41598-019-55047-4},
	note = {PMID: 31811259
PMCID: PMC6898053},
	langid = {eng}
}

@article{wilson2022,
	title = {AlphaFold2: A Role for Disordered Protein/Region Prediction?},
	author = {Wilson, Carter J. and Choy, Wing-Yiu and Karttunen, Mikko},
	year = {2022},
	month = {01},
	date = {2022-01},
	journal = {International Journal of Molecular Sciences},
	pages = {4591},
	volume = {23},
	number = {9},
	doi = {10.3390/ijms23094591},
	url = {https://www.mdpi.com/1422-0067/23/9/4591},
	note = {Number: 9
Publisher: Multidisciplinary Digital Publishing Institute},
	langid = {en}
}

@article{paiva2022,
	title = {Protein structural bioinformatics: An overview},
	author = {Paiva, {Vinícius de Almeida} and Gomes, Isabela de Souza and Monteiro, Cleiton Rodrigues and {Mendonça}, Murillo Ventura and Martins, {Pedro Magalhães} and Santana, Charles Abreu and {Gonçalves-Almeida}, Valdete and Izidoro, Sandro Carvalho and Melo-Minardi, Raquel Cardoso de and Silveira, Sabrina de Azevedo},
	year = {2022},
	month = {08},
	date = {2022-08-01},
	journal = {Computers in Biology and Medicine},
	pages = {105695},
	volume = {147},
	doi = {10.1016/j.compbiomed.2022.105695},
	url = {https://www.sciencedirect.com/science/article/pii/S0010482522004784}
}

@article{ismi2022,
	title = {Deep learning for protein secondary structure prediction: Pre and post-AlphaFold},
	author = {Ismi, Dewi Pramudi and Pulungan, Reza and Afiahayati, null},
	year = {2022},
	date = {2022},
	journal = {Computational and Structural Biotechnology Journal},
	pages = {6271--6286},
	volume = {20},
	doi = {10.1016/j.csbj.2022.11.012},
	note = {PMID: 36420164
PMCID: PMC9678802},
	langid = {eng}
}

@article{paysan-lafosse2023,
	title = {InterPro in 2022},
	author = {Paysan-Lafosse, Typhaine and Blum, Matthias and Chuguransky, Sara and Grego, Tiago and Pinto, {Beatriz Lázaro} and Salazar, {Gustavo A} and Bileschi, {Maxwell L} and Bork, Peer and Bridge, Alan and Colwell, Lucy and Gough, Julian and Haft, {Daniel H} and {Letuni{\'{c}}}, Ivica and Marchler-Bauer, Aron and Mi, Huaiyu and Natale, {Darren A} and Orengo, {Christine A} and Pandurangan, {Arun P} and Rivoire, Catherine and Sigrist, Christian J A and Sillitoe, Ian and Thanki, Narmada and Thomas, Paul D and Tosatto, Silvio C E and Wu, {Cathy H} and Bateman, Alex},
	year = {2023},
	month = {01},
	date = {2023-01-06},
	journal = {Nucleic Acids Research},
	pages = {D418--D427},
	volume = {51},
	number = {D1},
	doi = {10.1093/nar/gkac993},
	url = {https://doi.org/10.1093/nar/gkac993}
}

@article{høie2022,
	title = {NetSurfP-3.0: accurate and fast prediction of protein structural features by protein language models and deep learning},
	author = {{Høie}, Magnus Haraldson and Kiehl, Erik Nicolas and Petersen, Bent and Nielsen, Morten and Winther, Ole and Nielsen, Henrik and Hallgren, Jeppe and Marcatili, Paolo},
	year = {2022},
	month = {07},
	date = {2022-07-05},
	journal = {Nucleic Acids Research},
	pages = {W510--W515},
	volume = {50},
	number = {W1},
	doi = {10.1093/nar/gkac439},
	url = {https://doi.org/10.1093/nar/gkac439}
}

@article{lee,
	title = {Petascale Homology Search for Structure Prediction},
	author = {Lee, Sewon and Kim, Gyuri and Karin, Eli Levy and Mirdita, Milot and Park, Sukhwan and Chikhi, Rayan and Babaian, Artem and Kryshtafovych, Andriy and Steinegger, Martin},
	doi = {10.1101/2023.07.10.548308},
	langid = {en}
}

@article{callaway2022,
	title = {AlphaFold{\textquoteright}s new rival? Meta AI predicts shape of 600 million proteins},
	author = {Callaway, Ewen},
	year = {2022},
	month = {11},
	date = {2022-11-01},
	journal = {Nature},
	pages = {211--212},
	volume = {611},
	number = {7935},
	doi = {10.1038/d41586-022-03539-1},
	url = {https://www.nature.com/articles/d41586-022-03539-1},
	note = {Bandiera{\_}abtest: a
Cg{\_}type: News
Number: 7935
Publisher: Nature Publishing Group
Subject{\_}term: Computational biology and bioinformatics, Proteomics, Structural biology},
	langid = {en}
}

@article{elofsson2023,
	title = {Progress at protein structure prediction, as seen in CASP15},
	author = {Elofsson, Arne},
	year = {2023},
	month = {06},
	date = {2023-06},
	journal = {Current Opinion in Structural Biology},
	pages = {102594},
	volume = {80},
	doi = {10.1016/j.sbi.2023.102594},
	note = {PMID: 37060758},
	langid = {eng}
}

@article{lin2023,
	title = {Evolutionary-scale prediction of atomic-level protein structure with a language model},
	author = {Lin, Zeming and Akin, Halil and Rao, Roshan and Hie, Brian and Zhu, Zhongkai and Lu, Wenting and Smetanin, Nikita and Verkuil, Robert and Kabeli, Ori and Shmueli, Yaniv and dos Santos Costa, Allan and Fazel-Zarandi, Maryam and Sercu, Tom and Candido, Salvatore and Rives, Alexander},
	year = {2023},
	month = {03},
	date = {2023-03-17},
	journal = {Science},
	pages = {1123--1130},
	volume = {379},
	number = {6637},
	doi = {10.1126/science.ade2574},
	url = {https://www.science.org/doi/10.1126/science.ade2574},
	note = {Publisher: American Association for the Advancement of Science}
}

@article{peng2023,
	title = {Improved protein structure prediction with trRosettaX2, AlphaFold2, and optimized MSAs in CASP15},
	author = {Peng, Zhenling and Wang, Wenkai and Wei, Hong and Li, Xiaoge and Yang, Jianyi},
	year = {2023},
	month = {08},
	date = {2023-08-10},
	journal = {Proteins},
	doi = {10.1002/prot.26570},
	note = {PMID: 37565699},
	langid = {eng}
}

@article{yu2023,
	title = {AlphaPulldown{\textemdash}a python package for protein{\textendash}protein interaction screens using AlphaFold-Multimer},
	author = {Yu, Dingquan and Chojnowski, Grzegorz and Rosenthal, Maria and Kosinski, Jan},
	year = {2023},
	month = {01},
	date = {2023-01-01},
	journal = {Bioinformatics},
	pages = {btac749},
	volume = {39},
	number = {1},
	doi = {10.1093/bioinformatics/btac749},
	url = {https://doi.org/10.1093/bioinformatics/btac749}
}

@article{sehnal2021,
	title = {Mol* Viewer: modern web app for 3D visualization and analysis of large biomolecular structures},
	author = {Sehnal, David and Bittrich, Sebastian and Deshpande, Mandar and {Svobodová}, Radka and Berka, Karel and Bazgier, {Václav} and Velankar, Sameer and Burley, Stephen K. and {Ko{\v{c}}a}, Jaroslav and Rose, Alexander S.},
	year = {2021},
	month = {07},
	date = {2021-07-02},
	journal = {Nucleic Acids Research},
	pages = {W431--W437},
	volume = {49},
	number = {W1},
	doi = {10.1093/nar/gkab314},
	note = {PMID: 33956157
PMCID: PMC8262734},
	langid = {eng}
}

@article{baek,
	title = {Efficient and accurate prediction of protein structure using RoseTTAFold2},
	author = {Baek, Minkyung and Anishchenko, Ivan and Humphreys, Ian R. and Cong, Qian and Baker, David and DiMaio, Frank},
	doi = {10.1101/2023.05.24.542179},
	langid = {en}
}

@article{rosignoli2022,
	title = {Boosting the Full Potential of PyMOL with Structural Biology Plugins},
	author = {Rosignoli, Serena and Paiardini, Alessandro},
	year = {2022},
	month = {12},
	date = {2022-12},
	journal = {Biomolecules},
	pages = {1764},
	volume = {12},
	number = {12},
	doi = {10.3390/biom12121764},
	url = {https://www.mdpi.com/2218-273X/12/12/1764},
	note = {Number: 12
Publisher: Multidisciplinary Digital Publishing Institute},
	langid = {en}
}

@article{adiyaman2023,
	title = {Improvement of protein tertiary and quaternary structure predictions using the ReFOLD refinement method and the AlphaFold2 recycling process},
	author = {Adiyaman, Recep and Edmunds, Nicholas S and Genc, Ahmet G and Alharbi, Shuaa M A and McGuffin, Liam J},
	year = {2023},
	month = {01},
	date = {2023-01-01},
	journal = {Bioinformatics Advances},
	pages = {vbad078},
	volume = {3},
	number = {1},
	doi = {10.1093/bioadv/vbad078},
	url = {https://doi.org/10.1093/bioadv/vbad078}
}

@inbook{webb2017,
	title = {Protein Structure Modeling with MODELLER},
	author = {Webb, Benjamin and Sali, Andrej},
	editor = {Kaufmann, Michael and Klinger, Claudia and Savelsbergh, Andreas},
	year = {2017},
	date = {2017},
	publisher = {Springer},
	pages = {39--54},
	series = {Methods in Molecular Biology},
	doi = {10.1007/978-1-4939-7231-9_4},
	url = {https://doi.org/10.1007/978-1-4939-7231-9_4},
	note = {DOI: 10.1007/978-1-4939-7231-9{\_}4},
	address = {New York, NY},
	langid = {en}
}

@article{pieper2014,
	title = {ModBase, a database of annotated comparative protein structure models and associated resources},
	author = {Pieper, Ursula and Webb, Benjamin M. and Dong, Guang Qiang and Schneidman-Duhovny, Dina and Fan, Hao and Kim, Seung Joong and Khuri, Natalia and Spill, Yannick G. and Weinkam, Patrick and Hammel, Michal and Tainer, John A. and Nilges, Michael and Sali, Andrej},
	year = {2014},
	month = {01},
	date = {2014-01},
	journal = {Nucleic Acids Research},
	pages = {D336--346},
	volume = {42},
	number = {Database issue},
	doi = {10.1093/nar/gkt1144},
	note = {PMID: 24271400
PMCID: PMC3965011},
	langid = {eng}
}

@article{ahdritz,
	title = {OpenFold: Retraining AlphaFold2 yields new insights into its learning mechanisms and capacity for generalization},
	author = {Ahdritz, Gustaf and Bouatta, Nazim and Floristean, Christina and Kadyan, Sachin and Xia, Qinghui and Gerecke, William and {O{\textquoteright}Donnell}, Timothy J. and Berenberg, Daniel and Fisk, Ian and Zanichelli, {Niccolò} and Zhang, Bo and Nowaczynski, Arkadiusz and Wang, Bei and Stepniewska-Dziubinska, Marta M. and Zhang, Shang and Ojewole, Adegoke and Guney, Murat Efe and Biderman, Stella and Watkins, Andrew M. and Ra, Stephen and Lorenzo, Pablo Ribalta and Nivon, Lucas and Weitzner, Brian and Ban, Yih-En Andrew and Sorger, Peter K. and Mostaque, Emad and Zhang, Zhao and Bonneau, Richard and AlQuraishi, Mohammed},
	doi = {10.1101/2022.11.20.517210},
	langid = {en}
}

@article{li,
	title = {Uni-Fold: An Open-Source Platform for Developing Protein Folding Models beyond AlphaFold},
	author = {Li, Ziyao and Liu, Xuyang and Chen, Weijie and Shen, Fan and Bi, Hangrui and Ke, Guolin and Zhang, Linfeng},
	doi = {10.1101/2022.08.04.502811},
	langid = {en}
}

@article{buchan2019,
	title = {The PSIPRED Protein Analysis Workbench: 20 years on},
	author = {Buchan, Daniel W A and Jones, David T},
	year = {2019},
	month = {07},
	date = {2019-07-02},
	journal = {Nucleic Acids Research},
	pages = {W402--W407},
	volume = {47},
	number = {W1},
	doi = {10.1093/nar/gkz297},
	url = {https://doi.org/10.1093/nar/gkz297}
}

@article{baeka,
	title = {Efficient and accurate prediction of protein structure using RoseTTAFold2},
	author = {Baek, Minkyung and Anishchenko, Ivan and Humphreys, Ian R. and Cong, Qian and Baker, David and DiMaio, Frank},
	doi = {10.1101/2023.05.24.542179},
	langid = {en}
}

@article{guo2022,
	title = {DeepUMQA: ultrafast shape recognition-based protein model quality assessment using deep learning},
	author = {Guo, Sai-Sai and Liu, Jun and Zhou, Xiao-Gen and Zhang, Gui-Jun},
	year = {2022},
	month = {03},
	date = {2022-03-28},
	journal = {Bioinformatics (Oxford, England)},
	pages = {1895--1903},
	volume = {38},
	number = {7},
	doi = {10.1093/bioinformatics/btac056},
	note = {PMID: 35134108},
	langid = {eng}
}

@article{olechnovic2019,
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