Script Arguments

In this section we list all the possible command-line arguments for the main routines of adabmDCA 2.0.

Train a DCA model

Command	Default value	Description
-d, --data	N/A	Filename of the dataset to be used for training the model.
-o, --output	DCA_model	Path to the folder where to save the model.
-m, --model	bmDCA	Type of model to be trained. Possible options are bmDCA, eaDCA, and edDCA.
-w, --weights	None	Path to the file containing the weights of the sequences. If None, the weights are computed automatically.
--clustering_seqid	0.8	Sequence identity threshold to be used for computing the sequence weights.
--no_reweighting	N/A	If this flag is used, the routine assigns uniform weights to the sequences.
-p, --path_params	None	Path to the file containing the model’s parameters. Required for restoring the training.
-c, --path_chains	None	Path to the FASTA file containing the model’s chains. Required for restoring the training.
-l, --label	None	A label to identify different algorithm runs. It prefixes the output files with this label.
--alphabet	protein	Type of encoding for the sequences. Choose among protein, rna, dna, or a user-defined string of tokens.
--lr	0.05	Learning rate.
--nsweeps	10	Number of sweeps for each gradient estimation.
--sampler	gibbs	Sampling method to be used. Possible options are gibbs and metropolis.
--nchains	10000	Number of Markov chains to run in parallel.
--target	0.95	Pearson correlation coefficient on the two-sites statistics to be reached.
--nepochs	50000	Maximum number of epochs allowed.
--pseudocount	None	Pseudo count for the single and two-sites statistics. Acts as a regularization. If None, it is set to \(1/M_{\mathrm{eff}}\).
--seed	0	Random seed.
--nthreads¹	1	Number of threads used in the Julia multithreaded version.
--device¹	cuda	Device to be used between cuda (GPU) and CPU. Used in the Python version.
--dtype¹	float32	Data type to be used between float32 and float64. Used in the Python version.

eaDCA options

Command	Default value	Description
--gsteps	10	Number of gradient updates to be performed on a given graph.
--factivate	0.001	Fraction of inactive couplings to try to activate at each graph update.

edDCA options

Command	Default value	Description
--gsteps	10	The number of gradient updates applied at each step of the graph convergence process.
--density	0.02	Target density to be reached.
--drate	0.01	Fraction of remaining couplings to be pruned at each decimation step.

Sampling from a DCA model

Command	Default value	Description
-p, --path_params	N/A	Path to the file containing the parameters of the DCA model to sample from.
-d, --data	N/A	Filename of the dataset MSA.
-o, --output	N/A	Path to the folder where to save the output.
--ngen	None	Number of samples to generate.
-l, --label	None	A label to identify different algorithm runs. It prefixes the output files with this label.
-w, --weights	None	Path to the file containing the weights of the sequences. If None, the weights are computed automatically.
--clustering_seqid	0.8	Sequence identity threshold to be used for computing the sequence weights.
--no_reweighting	N/A	If this flag is used, the routine assigns uniform weights to the sequences.
--nmeasure	10000	Number of data sequences to use for computing the mixing time. The value min(nmeasure, len(data)) is taken.
--nmix	2	Number of mixing times used to generate ‘ngen’ sequences starting from random.
--max_nsweeps	10000	Maximum number of sweeps allowed.
--alphabet	protein	Type of encoding for the sequences. Choose among protein, rna, dna, or a user-defined string of tokens.
--sampler	gibbs	Sampling method to be used. Possible options are gibbs and metropolis.
--beta	1.0	Inverse temperature to be used for the sampling.
--pseudocount	None	Pseudo count for the single and two-sites statistics. Acts as a regularization. If None, it is set to \(1/M_{\mathrm{eff}}\).
--device¹	cuda	Device to be used between cuda (GPU) and CPU. Used in the Python version.
--dtype¹	float32	Data type to be used between float32 and float64. Used in the Python version.

Computing DCA energies of a MSA

Command	Default value	Description
-d, --data	N/A	Filename of the input MSA.
-p, --path_params	N/A	Path to the file containing the parameters of the DCA model.
-o, --output	N/A	Path to the folder where to save the output.
--alphabet	protein	Type of encoding for the sequences. Choose among protein, rna, dna, or a user-defined string of tokens.
--device¹	cuda	Device to be used between cuda (GPU) and CPU. Used in the Python version.
--dtype¹	float32	Data type to be used between float32 and float64. Used in the Python version.

Generate a Deep Mutational Scan (DMS) from a wild type

Command	Default value	Description
-d, --data	N/A	Filename of the input MSA containing the wild type. If multiple sequences are present, the first one is used.
-p, --path_params	N/A	Path to the file containing the parameters of the DCA model.
-o, --output	N/A	Path to the folder where to save the output.
--alphabet	protein	Type of encoding for the sequences. Choose among protein, rna, dna, or a user-defined string of tokens.
--device¹	cuda	Device to be used between cuda (GPU) and CPU. Used in the Python version.
--dtype¹	float32	Data type to be used between float32 and float64. Used in the Python version.

Compute the Frobenius contact matrix

Command	Default value	Description
-p, --path_params	N/A	Path to the file containing the parameters of the DCA model.
-o, --output	N/A	Path to the folder where to save the output.
-l, --label	None	If provided, adds a label to the output files inside the output folder.
--alphabet	protein	Type of encoding for the sequences. Choose among protein, rna, dna, or a user-defined string of tokens.
--device¹	cuda	Device to be used between cuda (GPU) and CPU. Used in the Python version.
--dtype¹	float32	Data type to be used between float32 and float64. Used in the Python version.

¹ Used in specific versions of the software.