Table 1.

List of de novo RNA secondary structure prediction tools presented in this paper that are currently available.

	Model	TM	ML	MEA	PK	Ref.	Year	Related URL
RNAstructure	NN	✓				[11, 18]	1999	https://www.urmc.rochester.edu/rna/
PKNOTS	NN	✓			✓	[55]	1999	https://github.com/EddyRivasLab/PKNOTS
Mfold/UNAfold	NN	✓				[14, 15]	2003	http://www.unafold.org/
RNAfold	NN	✓				[16, 17]	2003	https://www.tbi.univie.ac.at/RNA/
NUPACK	NN	✓			✓	[56]	2003	https://www.nupack.org/
CONUS	PG		MLE			[66]	2004	http://eddylab.org/software/conus/
HotKnots	NN	✓	CG		✓	[58, 59]	2005	https://www.cs.ubc.ca/labs/algorithms/Software/HotKnots/
CONTRAfold	NN		CLLM	✓		[21, 22]	2006	http://contra.stanford.edu/contrafold/
SimFold	NN	✓	CG, BL			[47, 48]	2007	https://www.cs.ubc.ca/labs/algorithms/Projects/RNA-Params/
CentroidFold	NN	✓		✓		[53, 54]	2009	https://github.com/satoken/centroid-rna-package
ContextFold	NN		MM			[23]	2011	https://www.cs.bgu.ac.il/~negevcb/contextfold/
IPknot	NN	✓		✓	✓	[60, 61]	2011	https://github.com/satoken/ipknot
TORNADO	PG		MLE	✓		[24]	2012	https://github.com/EddyRivasLab/tornado
MXfold	NN	✓	MM			[49]	2018	https://github.com/mxfold/mxfold
LinearFold	NN	✓				[45]	2019	https://github.com/LinearFold/LinearFold
SPOT-RNA	DL		DL		✓	[74]	2019	https://github.com/jaswindersingh2/SPOT-RNA
E2Efold	DL		DL		✓	[71]	2020	https://github.com/ml4bio/e2efold
MXfold2	NN	✓	DL+MM			[50]	2021	https://github.com/mxfold/mxfold2
EternaFold	NN		multitask	✓		[77]	2022	https://github.com/eternagame/eternafold
Ufold	DL		DL		✓	[72]	2022	https://github.com/uci-cbcl/UFold
NeuralFold	DL		DL		✓	[73]	2022	https://github.com/keio-bioinformatics/Neuralfold

The column labeled ‘Model’ indicates the category to which the method belongs (NN: nearest neighbor model, PG: probabilistic generative model, DL: deep learning-based model). The ‘TM’ column indicates whether the method uses thermodynamic parameters. The column labeled ‘ML’ indicates whether the method can train its parameters using machine learning, and if so, which training method is used (MLE: maximum likelihood estimation, CG: constraint generation, CLLM: conditional log-linear models, BL: Boltzmann likelihood, MM: max-margin framework, DL: deep larning). The ‘MEA’ column indicates whether the method predicts secondary structure by default (✓) or optionally () with the maximum expected accuracy. The column labeled ‘PK’ indicates whether the method can predict pseudoknotted structures.