def evol_conserv(msa, **kwargs):
import prody
from prody import parseMSA, calcShannonEntropy, showShannonEntropy
from prody import writeArray
from os.path import splitext
prefix = kwargs.get('prefix')
if prefix is None:
prefix, _ = splitext(msa)
if _.lower() == '.gz':
prefix, _ = splitext(prefix)
prefix += '_conserv'
msa = parseMSA(msa)
entropy = calcShannonEntropy(msa, **kwargs)
writeArray(prefix + '.txt',
entropy, format=kwargs.get('numformat', '%12g'))
if kwargs.get('figent'):
try:
import matplotlib.pyplot as plt
except ImportError:
LOGGER.warn('Matplotlib could not be imported, '
'figures are not saved.')
else:
prody.SETTINGS['auto_show'] = False
width = kwargs.get('figwidth', 8)
height = kwargs.get('figheight', 6)
figargs = kwargs.get('figargs', ())
figure = plt.figure(figsize=(width, height))
show = showShannonEntropy(entropy, msa=msa, *figargs)
format = kwargs.get('figformat', 'pdf')
figure.savefig(prefix + '.' + format, format=format,
dpi=kwargs.get('figdpi', 300))
def evol_occupancy(msa, **kwargs):
from numpy import arange
import prody
from prody import parseMSA, calcMSAOccupancy, showMSAOccupancy, writeArray
from os.path import splitext
prefix = kwargs.get('prefix')
if prefix is None:
prefix, _ = splitext(msa)
if _.lower() == '.gz':
prefix, _ = splitext(prefix)
prefix += '_occupancy'
msa = parseMSA(msa)
numformat = kwargs.get('numformat', '%12g')
occupancy, suffix = [], []
occaxis = kwargs.get('occaxis', 'row')
if occaxis == 'both':
suffix = ['_row', '_col']
occupancy.append(calcMSAOccupancy(msa, occ='row'))
occupancy.append(calcMSAOccupancy(msa, occ='col'))
else:
suffix = '_' + occaxis
occupancy.append(calcMSAOccupancy(msa, occ=occaxis))
for i, occ in enumerate(occupancy):
writeArray((prefix + suffix[i] + '.txt'), occ, format=numformat)
for i, occ in enumerate(occupancy):
if kwargs.get('figocc'):
try:
import matplotlib.pyplot as plt
except ImportError:
LOGGER.warn('Matplotlib could not be imported, '
'figures are not saved.')
else:
prody.SETTINGS['auto_show'] = False
width = kwargs.get('figwidth', 8)
height = kwargs.get('figheight', 6)
xlabel = kwargs.get('xlabel')
title = kwargs.get('title')
figure = plt.figure(figsize=(width, height))
label = kwargs.get('label')
show = showMSAOccupancy(msa=msa,
occ=occ,
label=label,
xlabel=xlabel,
title=title)
format = kwargs.get('figformat', 'pdf')
figure.savefig(prefix + suffix[i] + '.' + format,
format=format,
dpi=kwargs.get('figdpi', 300))
def evol_occupancy(msa, **kwargs):
from numpy import arange
import prody
from prody import parseMSA, calcMSAOccupancy, showMSAOccupancy, writeArray
from os.path import splitext
prefix = kwargs.get('prefix')
if prefix is None:
prefix, _ = splitext(msa)
if _.lower() == '.gz':
prefix, _ = splitext(prefix)
prefix += '_occupancy'
msa = parseMSA(msa)
numformat = kwargs.get('numformat', '%12g')
occupancy , suffix = [], []
occaxis = kwargs.get('occaxis', 'row')
if occaxis == 'both':
suffix = ['_row', '_col']
occupancy.append(calcMSAOccupancy(msa, occ='row'))
occupancy.append(calcMSAOccupancy(msa, occ='col'))
else:
suffix = '_' + occaxis
occupancy.append(calcMSAOccupancy(msa, occ=occaxis))
for i, occ in enumerate(occupancy):
writeArray((prefix + suffix[i] + '.txt'), occ, format=numformat)
for i, occ in enumerate(occupancy):
if kwargs.get('figocc'):
try:
import matplotlib.pyplot as plt
except ImportError:
LOGGER.warn('Matplotlib could not be imported, '
'figures are not saved.')
else:
prody.SETTINGS['auto_show'] = False
width = kwargs.get('figwidth', 8)
height = kwargs.get('figheight', 6)
xlabel = kwargs.get('xlabel')
title = kwargs.get('title')
figure = plt.figure(figsize=(width, height))
label = kwargs.get('label')
show = showMSAOccupancy(msa=msa, occ=occ, label=label,
xlabel=xlabel, title=title)
format = kwargs.get('figformat', 'pdf')
figure.savefig(prefix + suffix[i] + '.' + format, format=format,
dpi=kwargs.get('figdpi', 300))
def evol_refine(msa, **kwargs):
from prody import parseMSA, refineMSA, writeMSA, LOGGER
from os.path import splitext
outname = kwargs.get('outname')
if outname is None:
outname, ext = splitext(msa)
if ext.lower() == '.gz':
outname, _ = splitext(msa)
outname += '_refined' + ext
writeMSA(outname, refineMSA(parseMSA(msa), **kwargs), **kwargs)
LOGGER.info('Refined MSA is written in file: ' + outname)
def evol_refine(msa, **kwargs):
import prody
from prody import parseMSA, refineMSA, writeMSA, LOGGER
from os.path import splitext
outname = kwargs.get('outname')
if outname is None:
outname, ext = splitext(msa)
if ext.lower() == '.gz':
outname, _ = splitext(msa)
outname += '_refined' + ext
writeMSA(outname, refineMSA(parseMSA(msa), **kwargs), **kwargs)
LOGGER.info('Refined MSA is written in file: ' + outname)
def evol_refine(msa, **kwargs):
import prody
from prody import parseMSA, refineMSA, writeMSA, LOGGER
from os.path import splitext
outname = kwargs.get("outname")
if outname is None:
outname, ext = splitext(msa)
if ext.lower() == ".gz":
outname, _ = splitext(msa)
outname += "_refined" + ext
writeMSA(outname, refineMSA(parseMSA(msa), **kwargs), **kwargs)
LOGGER.info("Refined MSA is written in file: " + outname)
def evol_merge(*msa, **kwargs):
import prody
from prody import parseMSA, mergeMSA, LOGGER, writeMSA, MSAFile
from prody.sequence.msafile import MSAEXTMAP
from os.path import splitext
if len(msa) < 2:
raise ValueError('multiple msa filenames must be specified')
msaobj = []
try:
msaobj = [parseMSA(fn) for fn in msa]
except:
raise IOError('failed to parse {0}'.format(fn))
msafile = MSAFile(msa[0])
format = kwargs.get('format') or msafile.format
outname = kwargs.get('outname') or (msafile.getTitle() + '_merged' +
MSAEXTMAP[msafile.format])
writeMSA(outname, mergeMSA(*msaobj), **kwargs)
LOGGER.info('Merged MSA is saved as: {0}'.format(outname))
def evol_conserv(msa, **kwargs):
import prody
from prody import parseMSA, calcShannonEntropy, showShannonEntropy
from prody import writeArray
from os.path import splitext
prefix = kwargs.get('prefix')
if prefix is None:
prefix, _ = splitext(msa)
if _.lower() == '.gz':
prefix, _ = splitext(prefix)
prefix += '_conserv'
msa = parseMSA(msa)
entropy = calcShannonEntropy(msa, **kwargs)
writeArray(prefix + '.txt',
entropy,
format=kwargs.get('numformat', '%12g'))
if kwargs.get('figent'):
try:
import matplotlib.pyplot as plt
except ImportError:
LOGGER.warn('Matplotlib could not be imported, '
'figures are not saved.')
else:
prody.SETTINGS['auto_show'] = False
width = kwargs.get('figwidth', 8)
height = kwargs.get('figheight', 6)
figargs = kwargs.get('figargs', ())
figure = plt.figure(figsize=(width, height))
show = showShannonEntropy(entropy, msa=msa, *figargs)
format = kwargs.get('figformat', 'pdf')
figure.savefig(prefix + '.' + format,
format=format,
dpi=kwargs.get('figdpi', 300))
def evol_coevol(msa, **kwargs):
from numpy import arange
import prody
from prody import parseMSA, buildMutinfoMatrix, showMutinfoMatrix
from prody import applyMutinfoCorr, calcShannonEntropy
from prody import writeArray, LOGGER, applyMutinfoNorm, writeHeatmap
from os.path import splitext
prefix = kwargs.get('prefix')
if prefix is None:
prefix, _ = splitext(msa)
if _.lower() == '.gz':
prefix, _ = splitext(prefix)
prefix += '_mutinfo'
msa = parseMSA(msa)
mutinfo = buildMutinfoMatrix(msa, **kwargs)
numformat = kwargs.get('numformat', '%12g')
heatmap = kwargs.get('heatmap', False)
#writeArray(prefix + '.txt', mutinfo, format=numformat)
if heatmap:
hmargs = {
'xlabel': 'Residue', 'ylabel': 'Residue',
'xorigin': 1, 'xstep': 1,
'residue': arange(msa.numResidues())}
todo = [(None, None)]
norm = kwargs.get('normalization', [])
corr = kwargs.get('correction', [])
if norm is not None:
if 'joint' in norm:
todo.append(('norm', 'joint'))
for which in norm:
if which == 'join': continue
todo.append(('norm', which))
if corr is not None:
for which in corr:
todo.append(('corr', which))
entropy = None
for what, which in todo:
if what is None:
matrix = mutinfo
suffix = ''
tuffix = ' Mutual Information'
elif which == 'joint':
LOGGER.info('Applying {0} normalization.'.format(repr(which)))
matrix = buildMutinfoMatrix(msa, norm=True, **kwargs)
suffix = '_norm_joint'
tuffix = ' MI - Normalization: ' + which
elif what == 'norm':
LOGGER.info('Applying {0} normalization.'.format(repr(which)))
if entropy is None:
entropy = calcShannonEntropy(msa, **kwargs)
matrix = applyMutinfoNorm(mutinfo, entropy, norm=which)
suffix = '_norm_' + which
tuffix = ' MI - Normalization: ' + which
else:
LOGGER.info('Applying {0} correction.'.format(repr(which)))
matrix = applyMutinfoCorr(mutinfo, which)
suffix = '_corr_' + which
tuffix = ' MI - Correction: ' + which
writeArray(prefix + suffix + '.txt',
matrix, format=kwargs.get('numformat', '%12g'))
if heatmap:
writeHeatmap(prefix + suffix + '.hm', matrix,
title = msa.getTitle() + tuffix, **hmargs)
if kwargs.get('figcoevol'):
try:
import matplotlib.pyplot as plt
except ImportError:
LOGGER.warn('Matplotlib could not be imported, '
'figures are not saved.')
else:
cmin = kwargs.get('cmin', matrix.min())
cmax = kwargs.get('cmax', matrix.max())
prody.SETTINGS['auto_show'] = False
width = kwargs.get('figwidth', 8)
height = kwargs.get('figheight', 6)
xlabel = kwargs.get('xlabel')
title = kwargs.get('title')
figure = plt.figure(figsize=(width, height))
show = showMutinfoMatrix(matrix, msa=msa, clim=(cmin, cmax),
xlabel=xlabel, title=title)
format = kwargs.get('figformat', 'pdf')
figure.savefig(prefix + suffix + '.' + format, format=format,
dpi=kwargs.get('figdpi', 300))
def evol_rankorder(mutinfo, **kwargs):
from prody import parseMSA, LOGGER, parsePDB, calcMSAOccupancy
from prody.utilities import openFile
from os.path import splitext
delimiter = kwargs.get('delimiter')
mi = np.loadtxt(str(mutinfo), delimiter=delimiter)
ndim, shape = mi.ndim, mi.shape
if ndim != 2 or shape[0] != shape[1]:
raise ValueError('mutinfo must contain a square matrix')
msa, label = kwargs.get('msa'), kwargs.get('label')
pdb, pdbflag = kwargs.get('pdb'), False
resnum = None
if pdb is not None:
from prody import parsePDB
try:
pdb = parsePDB(pdb)
except:
LOGGER.info('Could not parse PDB, ignoring PDB input')
else:
chains = list(pdb.iterChains())
for chain in chains:
sel = chain.select('protein and name CA')
if sel.numAtoms() == shape[0]:
resnum = sel.getResnums()
coordset = sel.getCoordsets()
distance = calcAllDist(coordset)
pdbflag = True
label = pdb.getTitle()
LOGGER.info('Residue numbers will be based on pdb: '
'{0}'.format(pdb.getTitle()))
break
else:
LOGGER.info('Number of residues in PDB does not match '
'mutinfo matrix, ignoring PDB input')
if not pdbflag:
if msa is not None:
msa = parseMSA(msa)
if msa.numResidues() != shape[0]:
LOGGER.info('Input MSA and mutinfo do not have similar no '
'of residues, ignoring MSA')
else:
index = msa.getIndex(label)
if index is None:
if label is not None:
LOGGER.info('Could not find given label in MSA, '
'using complete sequence from MSA')
occ = calcMSAOccupancy(msa._msa, 'row')
index = np.where(occ == occ.max())[0][0]
label, seq, start, end = msa[index]
else:
label, seq, start, end = msa[index]
if (start and end is not None) and (start < end):
resnum = np.arange(start, end+1)
if len(resnum) != shape[0]:
LOGGER.info('Label: {0}/{1}-{2} and mutinfo do '
'not have similar no of residues, using '
'serial indexing'.format(label, start, end))
label = 'Serial Index'
resnum = np.arange(1, shape[0]+1)
else:
LOGGER.info('Residue numbers will be based on label: '
'{0}'.format(label))
else:
LOGGER.info('Could not identify residue indexes from MSA'
' using serial indexing')
label = 'Serial Index'
resnum = np.arange(1, shape[0]+1)
else:
LOGGER.info('MSA or PDB not given or does not match mutinfo, '
'using serial indexing')
resnum = np.arange(1, shape[0]+1)
LOGGER.info('Residue numbers start and end with {0}-{1}'.
format(str(resnum[0]), str(resnum[-1])))
outname = kwargs.get('outname')
if outname is None:
outname, ext = splitext(str(mutinfo))
if ext.lower() == '.gz':
outname, _ = splitext(str(mutinfo))
else:
outname, ext = splitext(str(outname))
if ext is None:
ext = '.txt'
outname += '_rankorder' + ext
zscore = kwargs.get('zscore')
if zscore:
LOGGER.info('zscore normalization applied such that each column '
'has 0 mean and standard deviation 1')
header = 'Serial\tRow\tColumn\tZscore'
mi = (mi - mi.mean(0)) / mi.std(0)
else:
header = 'Serial\tRow\tColumn\tMI'
mi_ind_start, mi_ind_end = np.tril_indices(shape[0], k=-1)
mi_matrix = mi[mi_ind_start, mi_ind_end]
sorted_index = mi_matrix.argsort(axis=None)[::-1]
row = mi_ind_start[sorted_index]
column = mi_ind_end[sorted_index]
count = 1
i = 0
f = openFile(outname, 'wb')
if label is None:
label = 'Serial Index'
numpairs = kwargs.get('numpairs')
size = len(row)
seqsep = kwargs.get('seqsep')
if not kwargs.get('usedist') or not pdbflag:
if kwargs.get('usedist'):
LOGGER.info('use-struct-sep set to true, but PDB not given or '
'incorrect residue number. Using sequence separation')
else:
if pdbflag:
LOGGER.info('use-dist not set, using sequence separation'
' to report coevolving pairs')
f.write(('Label: '+ label + '\t' + 'Residue Numbers: ' +
str(resnum[0]) + '-' + str(resnum[-1]) + '\tSequence Separation:' +
str(seqsep) + '\n'))
if pdbflag:
f.write((header + '\tDistance\n'))
while count <=numpairs and i < size:
if row[i] > (column[i] + seqsep):
f.write('{0}\t{1}\t{2}\t{3:.3f}\t{4:.2f}\n'.
format(count, resnum[row[i]], resnum[column[i]],
mi[row[i], column[i]],
distance[row[i], column[i]]))
count += 1
i += 1
else:
f.write((header + '\n'))
while count <=numpairs and i < size:
if row[i] > (column[i] + seqsep):
f.write('{0}\t{1}\t{2}\t{3:.3f}\n'.
format(count, resnum[row[i]], resnum[column[i]],
mi[row[i], column[i]]))
count += 1
i += 1
else:
structsep = kwargs.get('dist')
f.write(('Label: '+ label + '\t' + 'Residue Numbers: ' +
str(resnum[0]) + '-' + str(resnum[-1]) + 'Distance Cutoff:' +
str(structsep) + '\n'))
f.write((header + '\tDistance\n'))
while count <=numpairs and i < size:
if distance[row[i], column[i]] > structsep:
f.write('{0}\t{1}\t{2}\t{3:.3f}\t{4:.2f}\n'.
format(count, resnum[row[i]], resnum[column[i]],
mi[row[i], column[i]],
distance[row[i], column[i]]))
count += 1
i += 1
f.close()
__author__ = 'Ahmet Bakan'
__copyright__ = 'Copyright (C) 2010-2012 Ahmet Bakan'
from prody.tests import TestCase
from numpy import array, log, zeros, char, ones
from numpy.testing import assert_array_equal, assert_array_almost_equal
from prody.tests.test_datafiles import *
from prody import LOGGER, calcShannonEntropy, buildMutinfoMatrix, parseMSA
from prody import calcMSAOccupancy, buildSeqidMatrix, uniqueSequences
LOGGER.verbosity = None
FASTA = parseMSA(pathDatafile('msa_Cys_knot.fasta'))
FASTA_ALPHA = char.isalpha(FASTA._msa)
FASTA_UPPER = char.upper(FASTA._msa)
FASTA_NUMBER, FASTA_LENGTH = FASTA_ALPHA.shape
FASTA_EYE = zeros((FASTA_NUMBER, FASTA_NUMBER))
for i in range(FASTA_NUMBER):
FASTA_EYE[i, i] = 1
for j in range(i + 1, FASTA_NUMBER):
score = 0.0
ncols = 0
for k in range(FASTA_LENGTH):
if FASTA_ALPHA[i, k] or FASTA_ALPHA[j, k]:
if FASTA_UPPER[i, k] == FASTA_UPPER[j, k]:
score += 1
ncols += 1
__author__ = 'Ahmet Bakan'
__copyright__ = 'Copyright (C) 2010-2012 Ahmet Bakan'
from prody.tests import TestCase
from numpy import array, log, zeros, char
from numpy.testing import assert_array_equal, assert_array_almost_equal
from prody.tests.test_datafiles import *
from prody import LOGGER, refineMSA, parseMSA, calcMSAOccupancy, mergeMSA
from prody import uniqueSequences
LOGGER.verbosity = None
FASTA = parseMSA(pathDatafile('msa_Cys_knot.fasta'))
FASTA_ALPHA = char.isalpha(FASTA._msa)
NUMSEQ = FASTA.numSequences() * 1.
class TestRefinement(TestCase):
def testLabel(self):
label = 'FSHB_BOVIN'
index = FASTA.getIndex(label)
refined = refineMSA(FASTA, label=label)._getArray()
expected = FASTA._getArray().take(FASTA_ALPHA[index].nonzero()[0], 1)
assert_array_equal(refined, expected)
def testRowocc(self):
from os.path import join
try:
from io import StringIO
except ImportError:
from io import StringIO
from numpy import array, log, zeros, char
from numpy.testing import assert_array_equal, dec
from prody.tests.datafiles import *
from prody.tests import TEMPDIR
from prody import MSA, MSAFile, parseMSA, LOGGER, writeMSA
LOGGER.verbosity = None
FASTA = parseMSA(pathDatafile('msa_Cys_knot.fasta'))
SELEX = parseMSA(pathDatafile('msa_Cys_knot.slx'))
STOCK = parseMSA(pathDatafile('msa_Cys_knot.sth'))
FASTA_LIST = list(MSAFile(pathDatafile('msa_Cys_knot.fasta')))
SELEX_LIST = list(MSAFile(pathDatafile('msa_Cys_knot.sth')))
STOCK_LIST = list(MSAFile(pathDatafile('msa_Cys_knot.sth')))
class TestMSAFile(TestCase):
def testMSAFile(self):
self.assertListEqual(FASTA_LIST, SELEX_LIST)
self.assertListEqual(FASTA_LIST, STOCK_LIST)
def testWriteFasta(self):
def evol_rankorder(mutinfo, **kwargs):
from prody import parseMSA, LOGGER, parsePDB, calcMSAOccupancy
from prody.utilities import openFile
from os.path import splitext
delimiter = kwargs.get('delimiter')
mi = np.loadtxt(str(mutinfo), delimiter=delimiter)
ndim, shape = mi.ndim, mi.shape
if ndim != 2 or shape[0] != shape[1]:
raise ValueError('mutinfo must contain a square matrix')
msa, label = kwargs.get('msa'), kwargs.get('label')
pdb, pdbflag = kwargs.get('pdb'), False
resnum = None
if pdb is not None:
from prody import parsePDB
try:
pdb = parsePDB(pdb)
except:
LOGGER.info('Could not parse PDB, ignoring PDB input')
else:
chains = list(pdb.iterChains())
for chain in chains:
sel = chain.select('protein and name CA')
if sel.numAtoms() == shape[0]:
resnum = sel.getResnums()
coordset = sel.getCoordsets()
distance = calcAllDist(coordset)
pdbflag = True
label = pdb.getTitle()
LOGGER.info('Residue numbers will be based on pdb: '
'{0}'.format(pdb.getTitle()))
break
else:
LOGGER.info('Number of residues in PDB does not match '
'mutinfo matrix, ignoring PDB input')
if not pdbflag:
if msa is not None:
msa = parseMSA(msa)
if msa.numResidues() != shape[0]:
LOGGER.info('Input MSA and mutinfo do not have similar no '
'of residues, ignoring MSA')
else:
index = msa.getIndex(label)
if index is None:
if label is not None:
LOGGER.info('Could not find given label in MSA, '
'using complete sequence from MSA')
occ = calcMSAOccupancy(msa._msa, 'row')
index = np.where(occ == occ.max())[0][0]
label, seq, start, end = msa[index]
else:
label, seq, start, end = msa[index]
if (start and end is not None) and (start < end):
resnum = np.arange(start, end + 1)
if len(resnum) != shape[0]:
LOGGER.info('Label: {0}/{1}-{2} and mutinfo do '
'not have similar no of residues, using '
'serial indexing'.format(
label, start, end))
label = 'Serial Index'
resnum = np.arange(1, shape[0] + 1)
else:
LOGGER.info('Residue numbers will be based on label: '
'{0}'.format(label))
else:
LOGGER.info('Could not identify residue indexes from MSA'
' using serial indexing')
label = 'Serial Index'
resnum = np.arange(1, shape[0] + 1)
else:
LOGGER.info('MSA or PDB not given or does not match mutinfo, '
'using serial indexing')
resnum = np.arange(1, shape[0] + 1)
LOGGER.info('Residue numbers start and end with {0}-{1}'.format(
str(resnum[0]), str(resnum[-1])))
outname = kwargs.get('outname')
if outname is None:
outname, ext = splitext(str(mutinfo))
if ext.lower() == '.gz':
outname, _ = splitext(str(mutinfo))
else:
outname, ext = splitext(str(outname))
if ext is None:
ext = '.txt'
outname += '_rankorder' + ext
zscore = kwargs.get('zscore')
if zscore:
LOGGER.info('zscore normalization applied such that each column '
'has 0 mean and standard deviation 1')
header = 'Serial\tRow\tColumn\tZscore'
mi = (mi - mi.mean(0)) / mi.std(0)
else:
header = 'Serial\tRow\tColumn\tMI'
mi_ind_start, mi_ind_end = np.tril_indices(shape[0], k=-1)
mi_matrix = mi[mi_ind_start, mi_ind_end]
sorted_index = mi_matrix.argsort(axis=None)[::-1]
row = mi_ind_start[sorted_index]
column = mi_ind_end[sorted_index]
count = 1
i = 0
f = openFile(outname, 'wb')
if label is None:
label = 'Serial Index'
numpairs = kwargs.get('numpairs')
size = len(row)
seqsep = kwargs.get('seqsep')
if not kwargs.get('usedist') or not pdbflag:
if kwargs.get('usedist'):
LOGGER.info('use-struct-sep set to true, but PDB not given or '
'incorrect residue number. Using sequence separation')
else:
if pdbflag:
LOGGER.info('use-dist not set, using sequence separation'
' to report coevolving pairs')
f.write(('Label: ' + label + '\t' + 'Residue Numbers: ' +
str(resnum[0]) + '-' + str(resnum[-1]) +
'\tSequence Separation:' + str(seqsep) + '\n'))
if pdbflag:
f.write((header + '\tDistance\n'))
while count <= numpairs and i < size:
if row[i] > (column[i] + seqsep):
f.write('{0}\t{1}\t{2}\t{3:.3f}\t{4:.2f}\n'.format(
count, resnum[row[i]], resnum[column[i]],
mi[row[i], column[i]], distance[row[i], column[i]]))
count += 1
i += 1
else:
f.write((header + '\n'))
while count <= numpairs and i < size:
if row[i] > (column[i] + seqsep):
f.write('{0}\t{1}\t{2}\t{3:.3f}\n'.format(
count, resnum[row[i]], resnum[column[i]],
mi[row[i], column[i]]))
count += 1
i += 1
else:
structsep = kwargs.get('dist')
f.write(('Label: ' + label + '\t' + 'Residue Numbers: ' +
str(resnum[0]) + '-' + str(resnum[-1]) + 'Distance Cutoff:' +
str(structsep) + '\n'))
f.write((header + '\tDistance\n'))
while count <= numpairs and i < size:
if distance[row[i], column[i]] > structsep:
f.write('{0}\t{1}\t{2}\t{3:.3f}\t{4:.2f}\n'.format(
count, resnum[row[i]], resnum[column[i]],
mi[row[i], column[i]], distance[row[i], column[i]]))
count += 1
i += 1
f.close()
import prody.sequence as sequence
import prody
import matplotlib.pyplot as plt
alignment = prody.MSAFile("pkinase.fasta")
#get positions -> by hand for now
positions = [72, 83, 117, 119, 194, 251, 354, 355, 357, 429, 432]
#user alignSequenceToMSA instead to derive positions automatically
#set up webservice to get correspondance between MSA position and a particular PDB structure
alignment.setSlice(positions)
prody.writeMSA("test.fasta", alignment)
pa = prody.parseMSA("pocket_type1.fasta")
labs = pa.getLabels()
seqidmatrix = prody.buildSeqidMatrix(pa)
scamatrix = prody.buildSCAMatrix(pa)
tree = prody.calcTree(names=labs, distance_matrix=seqidmatrix)
plt.figure()
show = prody.showTree(tree, format='plt')
from prody.tests import TestCase
import os
from os.path import join
from numpy import array, log, zeros, char
from numpy.testing import assert_array_equal, dec
from prody.tests.datafiles import *
from prody.tests import TEMPDIR
from prody import MSA, MSAFile, parseMSA, LOGGER, writeMSA
from prody.utilities import createStringIO
LOGGER.verbosity = None
FASTA = parseMSA(pathDatafile('msa_Cys_knot.fasta'))
SELEX = parseMSA(pathDatafile('msa_Cys_knot.slx'))
STOCK = parseMSA(pathDatafile('msa_Cys_knot.sth'))
FASTA_LIST = list(MSAFile(pathDatafile('msa_Cys_knot.fasta')))
SELEX_LIST = list(MSAFile(pathDatafile('msa_Cys_knot.sth')))
STOCK_LIST = list(MSAFile(pathDatafile('msa_Cys_knot.sth')))
class TestMSAFile(TestCase):
def testMSAFile(self):
self.assertListEqual(FASTA_LIST, SELEX_LIST)
self.assertListEqual(FASTA_LIST, STOCK_LIST)
def testWriteFasta(self):
def testSelex(self):
filename = writeMSA(join(TEMPDIR, 'test.slx'), SELEX)
selex = parseMSA(pathDatafile(filename))
self.assertListEqual(list(SELEX), list(selex))
if os.path.isfile(filename):
os.remove(filename)
def testSelex(self):
filename = writeMSA(join(TEMPDIR, 'test.slx'), SELEX)
selex = parseMSA(pathDatafile(filename))
self.assertListEqual(list(SELEX), list(selex))
if os.path.isfile(filename):
os.remove(filename)
def calcEvolProperties(self,
resid='all',
refresh=False,
folder=None,
max_cols=None,
max_seqs=25000,
**kwargs):
''' Computes Evol properties, i.e. Shannon entropy, Mutual
Information and Direct Information, from Pfam Multiple
Sequence Alignments, for a given residue.
'''
assert type(refresh) is bool
# recover Pfam mapping (if not found already)
self._searchPfam(refresh=refresh)
if resid == 'all':
PF_list = self.Pfam.keys()
else:
# get list of Pfam domains containing resid
PF_list = [
k for k in self.Pfam if any([
resid >= int(segment['start'])
and resid <= int(segment['end'])
for segment in self.Pfam[k]['locations']
])
]
if len(PF_list) == 0:
raise RuntimeError(
'No Pfam domain for resid {}.'.format(resid))
if len(PF_list) > 1:
LOGGER.warn('Residue {} is found in multiple '.format(resid) + \
'({}) Pfam domains.'.format(len(PF_list)))
if folder is None:
folder = SETTINGS.get('rhapsody_local_folder', './')
# iterate over Pfam families
for PF in PF_list:
d = self.Pfam[PF]
# skip if properties are pre-computed
if not refresh and d.get('mapping') is not None:
continue
d['mapping'] = None
d['ref_MSA'] = None
d['entropy'] = np.nan
d['MutInfo'] = np.nan
d['DirInfo'] = np.nan
try:
LOGGER.info('Processing {}...'.format(PF))
# fetch & parse MSA
# fname = PF + '_full.sth'
# fullname = os.path.join(folder, fname)
# if not os.path.isfile(fullname):
# f = fetchPfamMSA(PF)
# os.rename(f, fullname)
# msa = parseMSA(fullname, **kwargs)
# fetch & parse MSA without saving downloaded MSA
f = fetchPfamMSA(PF)
msa = parseMSA(f, **kwargs)
os.remove(f)
# slice MSA to match all segments of the Uniprot sequence
sliced_msa, indexes = self._sliceMSA(msa)
# if max_cols is not None and sliced_msa.numResidues() > max_cols:
# raise Exception('Unable to compute DI: MSA has ' +\
# 'too many columns (max: {}).'.format(max_cols))
# get mapping between Uniprot sequence and Pfam domain
d['mapping'] = self._mapUniprot2Pfam(PF, sliced_msa, indexes)
except Exception as e:
LOGGER.warn('{}: {}'.format(PF, e))
d['mapping'] = str(e)
continue
try:
# refine MSA ('seqid' param. is set as in PolyPhen-2)
rowocc = 0.6
while True:
sliced_msa = refineMSA(sliced_msa, rowocc=rowocc)
rowocc += 0.02
if sliced_msa.numSequences() <= max_seqs or rowocc >= 1:
break
ref_msa = refineMSA(sliced_msa, seqid=0.94, **kwargs)
d['ref_MSA'] = ref_msa
# compute evolutionary properties
d['entropy'] = calcShannonEntropy(ref_msa)
d['MutInfo'] = buildMutinfoMatrix(ref_msa)
# d['DirInfo'] = buildDirectInfoMatrix(ref_msa)
except Exception as e:
LOGGER.warn('{}: {}'.format(PF, e))
return {k: self.Pfam[k] for k in PF_list}
__copyright__ = "Copyright (C) 2010-2012 Ahmet Bakan"
from prody.tests import TestCase
from numpy import array, log, zeros, char, ones, fromfile
from numpy.testing import assert_array_equal, assert_array_almost_equal
from prody.tests.test_datafiles import *
from prody import LOGGER, calcShannonEntropy, buildMutinfoMatrix, parseMSA
from prody import calcMSAOccupancy, buildSeqidMatrix, uniqueSequences
from prody import buildOMESMatrix, buildSCAMatrix
LOGGER.verbosity = None
FASTA = parseMSA(pathDatafile("msa_Cys_knot.fasta"))
FASTA_ALPHA = char.isalpha(FASTA._msa)
FASTA_UPPER = char.upper(FASTA._msa)
FASTA_NUMBER, FASTA_LENGTH = FASTA_ALPHA.shape
FASTA_EYE = zeros((FASTA_NUMBER, FASTA_NUMBER))
for i in range(FASTA_NUMBER):
FASTA_EYE[i, i] = 1
for j in range(i + 1, FASTA_NUMBER):
score = 0.0
ncols = 0
for k in range(FASTA_LENGTH):
if FASTA_ALPHA[i, k] or FASTA_ALPHA[j, k]:
if FASTA_UPPER[i, k] == FASTA_UPPER[j, k]:
score += 1
ncols += 1
