def plot_alignment_statistics(alignment_file, sample_aln_file, aln_format, max_gap_pos, plot_file):
#read alignment
try:
alignment = io.read_msa(alignment_file, aln_format)
except OSError as e:
print("Problems reading alignment file {0}: {1}!".format(alignment_file, e))
sys.exit(0)
try:
sampled_alignment = io.read_msa(sample_aln_file, aln_format)
except OSError as e:
print("Problems reading alignment file {0}: {1}!".format(sample_aln_file, e))
sys.exit(0)
#Remove positions with > MAX_GAP_POS % gaps
if max_gap_pos < 100:
alignment, gapped_positions = gaps.remove_gapped_positions(alignment, max_gap_pos)
non_gapped_positions = [i for i in range(sampled_alignment.shape[1]) if i not in gapped_positions]
sampled_alignment = np.ascontiguousarray(sampled_alignment[:, non_gapped_positions])
# compute sequence weights for observed sequences
weights = ccmpred.weighting.weights_simple(alignment, 0.8)
# compute observed amino acid frequencies
pseudocounts = PseudoCounts(alignment, weights)
pseudocounts.calculate_frequencies(
'uniform_pseudocounts', 1, 1, remove_gaps=False
)
single_freq_observed, pairwise_freq_observed = pseudocounts.freqs
# compute sequence weights for sampled sequences (usually all sampled sequences obtain weight = 1 )
weights_sampled = ccmpred.weighting.weights_simple(sampled_alignment, 0.8)
# compute sampled amino acid frequencies
pseudocounts = PseudoCounts(sampled_alignment, weights_sampled)
pseudocounts.calculate_frequencies(
'uniform_pseudocounts', 1, 1, remove_gaps=False
)
single_freq_sampled, pairwise_freq_sampled = pseudocounts.freqs
# degap the frequencies (ignore gap frequencies)
single_freq_observed = pseudocounts.degap(single_freq_observed, False)
single_freq_sampled = pseudocounts.degap(single_freq_sampled, False)
pairwise_freq_observed = pseudocounts.degap(pairwise_freq_observed, False)
pairwise_freq_sampled = pseudocounts.degap(pairwise_freq_sampled, False)
# plot
plot.plot_empirical_vs_model_statistics(
single_freq_observed, single_freq_sampled,
pairwise_freq_observed, pairwise_freq_sampled,
plot_file)
def plot_aminoacid_distribution(alignment_file, aln_format, plot_file):
protein = os.path.basename(alignment_file).split(".")[0]
#read alignment
try:
alignment = io.read_msa(alignment_file, aln_format)
except OSError as e:
print("Problems reading alignment file {0}: {1}!".format(alignment_file, e))
sys.exit(0)
N = alignment.shape[0]
L = alignment.shape[1]
diversity = np.sqrt(N) / L
# compute sequence weights
weights = ccmpred.weighting.weights_simple(alignment, 0.8, False)
# compute frequencies
pseudocounts = PseudoCounts(alignment, weights)
pseudocounts.calculate_frequencies(
'uniform_pseudocounts', 1, 1, remove_gaps=False
)
#plot
plot.plot_alignment(
pseudocounts.counts[0],
"Amino Acid Distribution in Alignment for {0} (N={1}, L={2}, diversity={3})".format(
protein, N, L, np.round(diversity, decimals=3)), plot_file
)
def plot_contact_map(alignment_file, aln_format, braw_file, mat_file, pdb_file, plot_file,
entropy_correction, apc, seqsep, contact_threshold):
pseudocounts = None
mat = None
gaps_percentage_plot = None
protein = None
if entropy_correction and (alignment_file is None or braw_file is None):
print("Entropy correction requires specification of alignment file and binary raw couplign file!")
sys.exit(1)
if alignment_file is not None:
protein = os.path.basename(alignment_file).split(".")[0]
alignment = io.read_msa(alignment_file, aln_format)
# compute sequence weights
weights = ccmpred.weighting.weights_simple(alignment, 0.8)
# compute frequencies
pseudocounts = PseudoCounts(alignment, weights)
pseudocounts.calculate_frequencies(
'uniform_pseudocounts', 1, 1, remove_gaps=False
)
gaps_percentage_plot = plot.plot_percentage_gaps_per_position(pseudocounts.counts[0], plot_file=None)
if braw_file is not None:
protein = os.path.basename(braw_file).split(".")[0]
braw = raw.parse_msgpack(braw_file)
meta_info = braw.meta
# compute frobenius score from couplings
mat = io_cm.frobenius_score(braw.x_pair)
if entropy_correction:
scaling_factor_eta, mat = io_cm.compute_local_correction(
pseudocounts.freqs[0],
braw.x_pair,
meta_info['workflow'][0]['msafile']['neff'],
meta_info['workflow'][0]['regularization']['lambda_pair'],
mat,
entropy=True
)
elif apc:
mat = io_cm.apc(mat)
if mat_file is not None:
protein = os.path.basename(mat_file).split(".")[0]
mat, meta_info = io_cm.read_matrix(mat_file)
if apc:
mat = io_cm.apc(mat)
L = len(mat)
indices_upper_tri_i, indices_upper_tri_j = np.triu_indices(L, seqsep)
plot_matrix = pd.DataFrame()
plot_matrix['residue_i'] = indices_upper_tri_i + 1
plot_matrix['residue_j'] = indices_upper_tri_j + 1
plot_matrix['confidence'] = mat[indices_upper_tri_i, indices_upper_tri_j]
if pdb_file is not None:
# compute distance map from pdb file
observed_distances = io.distance_map(pdb_file, L)
plot_matrix['distance'] = observed_distances[indices_upper_tri_i, indices_upper_tri_j]
plot_matrix['contact'] = ((plot_matrix.distance < contact_threshold) * 1).tolist()
plot_title="Contact Map for protein {0}".format(protein)
# Plot Contact Map
plot.plot_contact_map_someScore_plotly(plot_matrix, plot_title, seqsep, gaps_percentage_plot, plot_file)
def plot_alignment_statistics(alignment_file, sample_aln_file, aln_format,
plot_file):
protein = os.path.basename(alignment_file).split(".")[0]
#read alignment
try:
alignment = io.read_msa(alignment_file, aln_format)
except OSError as e:
print("Problems reading alignment file {0}: {1}!".format(
alignment_file, e))
sys.exit(0)
try:
sampled_alignment = io.read_msa(sample_aln_file, aln_format)
except OSError as e:
print("Problems reading alignment file {0}: {1}!".format(
sampled_alignment, e))
sys.exit(0)
#get alignment statistics
N_o = alignment.shape[0]
N_s = sampled_alignment.shape[0]
L = alignment.shape[1]
div = np.round(np.sqrt(N_o) / L, decimals=3)
### alignment
# compute sequence weights
weights = ccmpred.weighting.weights_simple(alignment, 0.8, False)
neff_weights_o = np.round(np.sum(weights), decimals=3)
neff_entropy_o = np.round(ccmpred.weighting.get_HHsuite_neff(alignment),
decimals=3)
# compute frequencies
pseudocounts = PseudoCounts(alignment, weights)
pseudocounts.calculate_frequencies('uniform_pseudocounts',
1,
1,
remove_gaps=False)
# get original amino acid frequencies
single_freq_observed, pairwise_freq_observed = pseudocounts.freqs
### sampled alignment
# compute sequence weights
weights_sampled = ccmpred.weighting.weights_simple(sampled_alignment, 0.8,
False)
neff_weights_s = np.round(np.sum(weights_sampled), decimals=3)
neff_entropy_s = np.round(
ccmpred.weighting.get_HHsuite_neff(sampled_alignment), decimals=3)
# compute frequencies
pseudocounts = PseudoCounts(sampled_alignment, weights_sampled)
pseudocounts.calculate_frequencies('uniform_pseudocounts',
1,
1,
remove_gaps=False)
# get amino acid frequencies
single_freq_sampled, pairwise_freq_sampled = pseudocounts.freqs
# degap the frequencies (ignore gap frequencies)
single_freq_observed = pseudocounts.degap(single_freq_observed, False)
single_freq_sampled = pseudocounts.degap(single_freq_sampled, False)
pairwise_freq_observed = pseudocounts.degap(pairwise_freq_observed, False)
pairwise_freq_sampled = pseudocounts.degap(pairwise_freq_sampled, False)
# Define plot title
title = "Observed and model alignment statistics for {0}".format(protein)
title += "<br>original: N={0}, L={1}, div={2}, neff(weights)={3}, neff(entropy)={4}".format(
N_o, L, div, neff_weights_o, neff_entropy_o)
title += "<br>sampled: N={0}, L={1}, neff(weights)={2}, neff(entropy)={3}".format(
N_s, L, neff_weights_s, neff_entropy_s)
# plot
plot.plot_empirical_vs_model_statistics(single_freq_observed,
single_freq_sampled,
pairwise_freq_observed,
pairwise_freq_sampled,
title,
plot_file,
log=False)