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OPTIONS.txt
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Protinfo_cluster -i <input_file with decoy files or names> -o <output_file basename> --<options>
options (can be all upper case or all lower case)
Compute_type
--cpu CPU is used for computations - (default)
--prune_cpu CPU is used for pruning computations
--cluster_cpu CPU is used for cluster computations
--gpu GPU is used for computations - this is the default
--prune_gpu GPU is used for pruning computations
--cluster_gpu GPU is used for cluster computations
Score_type
--rmsd RMSD is used as the similarity metric (default)
--prune_rmsd RMSD is used as the similarity metric for pruning
--cluster_rmsd RMSD is used as the similarity metric for clustering
--tmscore TMSCORE is used as the similarity metric
--prune_tmscore TMSCORE is used as the similarity metric for pruning
--cluster_tmscore TMSCORE is used as the similarity metric for clustering
simd_type
--scalar normal scalar operations will be used (default)
--sse2 SSE2 vectorization will be used when possible (Pentium 4 or newer, AMDK8 or newer)
--sse3 SSE3 vectorization will be used when possible (Pentium 4 Prescott or newer, AMD Athlon 64 or newer)
--avx AVX vectorization will be used when possible (Sandy Bridge, Ivy Bridge, AMD Bulldozer/Piledriver)
distance_matrix_storage
--float Use float (single precision 4 bytes) to represent distances (default)
--prune_float Use float (single precision 4 bytes) to represent distances in pruning step
--cluster_float Use float (single precision 4 bytes) to represent distances in cluster step
--compact Use unsigned char (1 byte) to represent distances
--prune_compact Use unsigned char (1 byte) to represent distances in prune step
--cluster_compact Use unsigned char (1 byte) to represent distances in cluster step
Input/output
write matrix in binary form for clustering
--write_matrix <matrix_file>
--write_binary_matrix <matrix_file>
--cluster_write_matrix <matrix_file>
--cluster_write_binary_matrix <matrix_file>
write distance matrix in binary form for pruning
--PRUNE_WRITE_MATRIX <matrix_file> --prune_write_matrix <matrix_file>
--PRUNE_WRITE_BINARY_MATRIX <matrix_file> --prune_write_binary_matrix <matrix_file>
write distance matrix in text form
--cluster_write_text_matrix <matrix_file>
--prune_write_text_matrix <matrix_file>
write distance matrix in compact form
--cluster_write_compact_matrix <matrix_file>
--prune_write_compact_matrix <matrix_file>
write cluster distance matrix in binary form
--write_matrix <matrix_file>
--write_binary_matrix <matrix_file>
--cluster_write_matrix <matrix_file>
--cluster_write_binary_matrix <matrix_file>
write prune distance matrix in binary form
--prune_write_matrix <matrix_file>
--prune_write_binary_matrix <matrix_file>
input binary coordinates instead pdb_list (application will look for <input_file>.coords and <input_file>.names
--binary_coords --BINARY_COORDS
--cluster_binary_coords --CLUSTER_BINARY_COORDS
--prune_binary_coords --PRUNE_BINARY_COORDS
Cluster methods
--nclusters number of clusters
--prune_nclusters number of clusters to be used in pruning
Hierarchical
--hcomplete complete linkage hierarchical clustering (max distance between clusters used)
--haverage average linkage hierarchical clustering (average distance between clusters used)
--hsingle single linkage hierarchical clustering (min distance between clusters used)
--cluster_hcomplete clustering step: complete linkage hierarchical clustering (max distance between clusters used)
--cluster_haverage clustering step: average linkage hierarchical clustering (average distance between clusters used)
--cluster_hsingle clustering step: single linkage hierarchical clustering (min distance between clusters used)
--prune_hcomplete prune using average distance to centers of clusters from complete linkage hierarchical clustering
--prune_haverage prune using average distance to centers of clusters from average linkage hierarchical clustering
--prune_hsingle prune using average distance to centers of clusters from single linkage hierarchical clustering
kmeans
--kmeans --cluster_kmeans use kmeans for clustering step (default) -cannot use for pruning
--min_cluster_size <n> minimum cluster size (kmeans only)
--max_iterations <n> max iterations for each random starting partition to converge to a final partition
--total_seeds <n> number of different starting partitions to try
--converge_seeds <n> number of starting partitions without improvement kmeans is said to have converged
--percentile <P> used to calculate when the partition score is in the P percentile with confidence pvalue p
--pvalue <p> P is a float value between 0-1 (higher is better value) and p is a positive float value
--fixed centers <n> the final n clusters of starting partition are not random but the most distant from the other clusters
--fine_parallel uses finer level of parallelism for kmeans rather than the default seed level parallelism - useful for large sets and small numbers of seeds
density
--density --cluster_density treat ensemble as single cluster and use min average distance to other structures to find center
--prune_density use single cluster density as criteron for pruning (default)
--density_only calculate density only - does not create similarity matrix so it is very memory efficient O(n)
--sort_density sort the densities in the density_only mode
kcenters
--kcenters --cluster_kcenters use kcenters algorithm for clustering
--prune_kcenters use distance from centers of kcenters to prune
pruning options
--prune_until_size<size> only option for all but density based pruning - prunes until the size of the ensemble is <size>
--prune_stop_at_size differs from prune_until_size by defining a minimum ensemble size regardless of other criteria
--prune_outlier_ratio <r> instead of removing the worst structure at each step use a ratio i.e. a ratio of .9 means that 99% best structures are kept after each step
--prune_to_density <density> prune until a threshhold average density <density> is reached
--prune_stop_density <density> stop pruning when average density reaches <density>
--prune_zmode used in conjunction with --prune_to_density/--prune_stop_density for stopping criterion
instead of <density> absolute values of density - <density> is number of standard deviations from mean
gpu_options
--gpu_id <id> specifies gpu whem there is more than one available
--prune_gpu_id <id> specifies gpu whem there is more than one available
--cluster_gpu_id <id> specifies gpu whem there is more than one available
misc
-i <filename> file with list of PDBs in normal (PDB_LIST) mode
file with list of names when similarity matrix is being read in
file with basename for binary_coords mode - program expects files <file>.coords <file>.names
-o <output_basename> default is "output"
-S <seed> define integer seed to be used by random number generators
-p <path> define the path to the tmscore.cl and rmsd.cl kernels
--help output this text