def testAmbiguous(self):
msa = array([list("bjzxBJZX"), list("bjzxBJZX")], dtype="|S1")
expect = -log(1.0 / array([2, 2, 2, 20] * 2))
result = calcShannonEntropy(msa)
assert_array_almost_equal(expect, result)
def testTwenty(self):
msa = array([[char] for char in "ACDEFGHIKLMNPQRSTVWY"], dtype="|S1")
expect = -log(1.0 / 20)
result = calcShannonEntropy(msa)
assert_array_almost_equal(expect, result)
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 testTwenty(self):
msa = array([[char] for char in 'ACDEFGHIKLMNPQRSTVWY'], dtype='|S1')
expect = -log(1. / 20)
result = calcShannonEntropy(msa)
assert_array_almost_equal(expect, result)
def testSmallProbability(self):
msa = zeros((1000000,1), '|S1')
msa[0] = 'A'
msa[1:] = 'C'
expect = array([1., 999999.]) / 1000000
expect = - (expect * log(expect)).sum()
result = calcShannonEntropy(msa)
assert_array_almost_equal(expect, result)
def testSmallProbability(self):
msa = zeros((1000000, 1), "|S1")
msa[0] = "A"
msa[1:] = "C"
expect = array([1.0, 999999.0]) / 1000000
expect = -(expect * log(expect)).sum()
result = calcShannonEntropy(msa)
assert_array_almost_equal(expect, result)
def testSmallProbability(self):
msa = zeros((1000000, 1), '|S1')
msa[0] = 'A'
msa[1:] = 'C'
expect = array([1., 999999.]) / 1000000
expect = -(expect * log(expect)).sum()
result = calcShannonEntropy(msa)
assert_array_almost_equal(expect, result)
def testAmbiguous(self):
msa = array([
list('bjzxBJZX'),
list('bjzxBJZX'),
], dtype='|S1')
expect = -log(1. / array([2, 2, 2, 20] * 2))
result = calcShannonEntropy(msa)
assert_array_almost_equal(expect, result)
def testGapDividend(self):
msa = array(
[list("AAAA"), list("AAAC"), list("AACD"), list("ACCE"), list("ACDF"), list("ACDG"), list("----")],
dtype="|S1",
)
expect = -log(1.0 / array([1, 2, 3, 6]))
result = calcShannonEntropy(msa, omitgaps=True)
assert_array_almost_equal(expect, result)
def testSixSequences(self):
msa = array([list('AAAAaaaaAAAAaaaa'),
list('AAACaaacAAACaaac'),
list('AACDaacdAACDaacd'),
list('ACCEacceacceACCE'),
list('ACDFacdfacdfACDF'),
list('ACDGacdgacdgACDG')], dtype='|S1')
expect = -log(1. / array([1, 2, 3, 6] * 4))
result = calcShannonEntropy(msa)
assert_array_almost_equal(expect, result)
def testGapDividend(self):
msa = array([list('AAAA'),
list('AAAC'),
list('AACD'),
list('ACCE'),
list('ACDF'),
list('ACDG'),
list('----')], dtype='|S1')
expect = -log(1. / array([1, 2, 3, 6]))
result = calcShannonEntropy(msa, omitgaps=True)
assert_array_almost_equal(expect, result)
def testSixSequences(self):
msa = array([
list('AAAAaaaaAAAAaaaa'),
list('AAACaaacAAACaaac'),
list('AACDaacdAACDaacd'),
list('ACCEacceacceACCE'),
list('ACDFacdfacdfACDF'),
list('ACDGacdgacdgACDG')
],
dtype='|S1')
expect = -log(1. / array([1, 2, 3, 6] * 4))
result = calcShannonEntropy(msa)
assert_array_almost_equal(expect, result)
def testGapDividend(self):
msa = array([
list('AAAA'),
list('AAAC'),
list('AACD'),
list('ACCE'),
list('ACDF'),
list('ACDG'),
list('----')
],
dtype='|S1')
expect = -log(1. / array([1, 2, 3, 6]))
result = calcShannonEntropy(msa, omitgaps=True)
assert_array_almost_equal(expect, result)
def testSixSequences(self):
msa = array(
[
list("AAAAaaaaAAAAaaaa"),
list("AAACaaacAAACaaac"),
list("AACDaacdAACDaacd"),
list("ACCEacceacceACCE"),
list("ACDFacdfacdfACDF"),
list("ACDGacdgacdgACDG"),
],
dtype="|S1",
)
expect = -log(1.0 / array([1, 2, 3, 6] * 4))
result = calcShannonEntropy(msa)
assert_array_almost_equal(expect, result)
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 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}
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))
