Quicklist

• Train a bmDCA model with default arguments:

  $ ./adabmDCA.sh train -d <fasta_file> -o <output_folder>
  

• Restore the training of a bmDCA model:

   $ ./adabmDCA.sh train -d <fasta_file> -o <output_folder> -p <file_params> -c <file_chains>
  

• Train an eaDCA model with default arguments:

  $ ./adabmDCA.sh train -m eaDCA -d <fasta_file> -o <output_folder> --nsweeps 5
  

• Restore the training of an eaDCA model:

  $ ./adabmDCA.sh train -m eaDCA -d <fasta_file> -o <output_folder> -p <file_params> -c <file_chains>
  

• Decimate a bmDCA model at 2% of density:

  ./adabmDCA.sh train -m edDCA -d <fasta_file> -p <file_params> -c <file_chains>
  

• Train and decimate a bmDCA model at 2% of density:

  $ ./adabmDCA.sh train -m edDCA -d <fasta_file> 
  

• Sample from a previously trained DCA model:

  $ ./adabmDCA.sh sample -p <file_params> -d <fasta_file> -o <output_folder> --ngen <num_gen>
  

• Scoring a sequence set:

  $ ./adabmDCA.sh  energies -d <fasta_file>  -p <file_params>  -o <output_folder>
  

• Generating a single mutant library starting from a wild type:

  $ ./adabmDCA.sh DMS -d <WT> -p <file_params> -o <output_folder>
  

• Computing the matrix of Frobenius norms for the contact prediction:

  $ ./adabmDCA.sh contacts -p <file_params> -o <output_folder>