def main():
# read command line options
opt = parse_args()
# print logo
if opt.logo:
ccmpred.logo.logo()
# set OMP environment variable for number of threads
os.environ['OMP_NUM_THREADS'] = str(opt.num_threads)
print("Using {0} threads for OMP parallelization.".format(
os.environ["OMP_NUM_THREADS"]))
# instantiate CCMpred
ccm = CCMpred()
# specify possible file paths
ccm.set_alignment_file(opt.alnfile)
ccm.set_matfile(opt.matfile)
ccm.set_pdb_file(opt.pdbfile)
ccm.set_initraw_file(opt.initrawfile)
# read alignment and possible remove gapped sequences and positions
ccm.read_alignment(opt.aln_format, opt.max_gap_pos, opt.max_gap_seq)
# compute sequence weights (in order to reduce sampling bias)
ccm.compute_sequence_weights(opt.weight, opt.wt_cutoff)
# compute amino acid counts and frequencies adding pseudo counts for non-observed amino acids
ccm.compute_frequencies(opt.pseudocounts, opt.pseudocount_single,
opt.pseudocount_pair)
# read pdb file if CCMpred is setup as a constrained run
if opt.pdbfile:
ccm.read_pdb(opt.contact_threshold)
# if alternative scores are specified: compute these and exit
if opt.omes:
ccm.compute_omes(opt.omes_fodoraldrich)
ccm.write_matrix()
sys.exit(0)
if opt.mi:
ccm.compute_mutual_info(opt.mi_normalized, opt.mi_pseudocounts)
ccm.write_matrix()
sys.exit(0)
# setup L2 regularization
ccm.specify_regularization(opt.lambda_single,
opt.lambda_pair_factor,
reg_type="L2",
scaling="L",
single_prior=opt.single_prior)
# intialise single and pair potentials either:
# - according to regularization priors
# - from initrawfile (accounting for removal of many gapped positions, if applicable)
ccm.intialise_potentials()
# optimize objective function (pLL or CD/PCD) with optimization algorithm (LBFGS, CG, GD or ADAM)
if opt.optimize:
#initialize log object
ccm.initiate_logging(opt.plot_opt_progress)
#minimize objective function with corresponding optimization algorithm
ccm.minimize(opt)
else:
print("\nDo not optimize but use model parameters provided by {0}\n".
format(opt.initrawfile))
### Post Processing
#specify meta data, and write (corrected) contact matrices to files
if opt.matfile:
# Compute contact score (frobenius norm) by recentering potentials
# TODO: other scores can be added ...
ccm.compute_contact_matrix(recenter_potentials=True, frob=True)
# compute corrected contact maps (removing entropy/phylogenetic biases)
# TODO: other corrections can be added ...
ccm.compute_correction(
apc_file=opt.apc_file,
entropy_correction_file=opt.entropy_correction_file)
ccm.write_matrix()
# write model parameters in binary format
if opt.out_binary_raw_file:
ccm.write_binary_raw(opt.out_binary_raw_file)
exitcode = 0
if opt.optimize:
if ccm.algret['code'] < 0:
exitcode = -ccm.algret['code']
sys.exit(exitcode)
def main():
# read command line options
opt = parse_args()
ccmpred.logo.logo(what_for="ccmgen")
# set OMP environment variable for number of threads
os.environ['OMP_NUM_THREADS'] = str(opt.num_threads)
print("Using {0} threads for OMP parallelization.".format(
os.environ["OMP_NUM_THREADS"]))
# instantiate CCMpred
ccm = CCMpred()
# specify possible file paths
ccm.set_initraw_file(opt.rawfile)
ccm.set_pdb_file(opt.pdbfile)
# read alignment and remove gapped sequences and positions
if opt.alnfile:
ccm.set_alignment_file(opt.alnfile)
ccm.read_alignment(opt.aln_format, opt.max_gap_pos, opt.max_gap_seq)
#read potentials from binary raw file (possibly remove positions with many gaps)
ccm.intialise_potentials()
x = ccmpred.parameter_handling.structured_to_linear(ccm.x_single,
ccm.x_pair,
nogapstate=True,
padding=False)
ncol = ccm.x_single.shape[0]
#if MCMC sampling is specified (requires alignment file)
if opt.mcmc:
msa_sampled, neff = ccmpred.sampling.generate_mcmc_sample(
x,
ccm.msa,
size=opt.nseq,
burn_in=opt.mcmc_burn_in,
sample_type=opt.mcmc_sample_type)
ids = ["seq {0}".format(i) for i in range(msa_sampled.shape[0])]
else:
tree = ccmpred.trees.CCMTree()
#prepare tree topology
if opt.tree_file:
tree.load_tree(opt.tree_file)
elif opt.tree_source is not None:
tree.specify_tree(opt.nseq, opt.tree_source)
ids = tree.ids
# sample alignment with Neff similar to alignment Neff (requires alignment file and burn-in)
if opt.mutation_rate_neff:
msa_sampled, neff = ccmpred.sampling.sample_to_neff_increasingly(
tree, ccm.neff_entropy, ncol, x, opt.seq0_mrf)
# sample alignment with specified mutation rate
elif opt.mutation_rate:
if opt.seq0_mrf:
seq0 = ccmpred.trees.get_seq0_mrf(x, ncol, opt.seq0_mrf)
print(
"Ancestor sequence (polyA --> {0} gibbs steps --> seq0) : {1}"
.format(
opt.seq0_mrf, "".join([
ccmpred.io.alignment.AMINO_ACIDS[c]
for c in seq0[0]
])))
elif opt.seq0_file:
seq0 = ccmpred.io.alignment.read_msa(opt.seq0_file,
opt.aln_format)
print("Ancestor sequence: {0}".format("".join(
[ccmpred.io.alignment.AMINO_ACIDS[c] for c in seq0[0]])))
else:
seq0 = np.zeros((1, ncol), dtype="uint8")
msa_sampled, neff = ccmpred.sampling.sample_with_mutation_rate(
tree, seq0, x, opt.mutation_rate)
# if gappy positions have been removed
# insert columns with gaps at that position
if ccm.max_gap_pos < 100:
msa_sampled = ccmpred.gaps.backinsert_gapped_positions_aln(
msa_sampled, ccm.gapped_positions)
print("\nWriting sampled alignment to {0}".format(opt.outalnfile))
with open(opt.outalnfile, "w") as f:
descs = [
"synthetic sequence generated with CCMgen"
for _ in range(msa_sampled.shape[0])
]
ccmpred.io.alignment.write_msa(f,
msa_sampled,
ids,
is_indices=True,
format=opt.aln_format,
descriptions=descs)