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blast_conservation_plot.py
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blast_conservation_plot.py
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#!/usr/bin/env python
"""Examine conservation of a protein by comparison to BLAST hits.
Given a UniProt protein ID or accession number as input (really anything that
can be queried in NCBI), this performs a BLAST search against the
non-redundant protein database and parses the results. Using them, a plot is
generated of average conservation across the protein. This provides a quick
evaluation of conserved and fluctuating regions.
Usage:
blast_conservation_plot.py <accession>
"""
from __future__ import with_statement
import sys
import os
from Bio import Entrez
from Bio.Blast import NCBIWWW
from Bio.Blast import NCBIXML
from Bio.SubsMat import MatrixInfo
import pylab
import numpy
def main(accession):
window_size = 29
cache_dir = os.path.join(os.getcwd(), "cache")
ncbi_manager = NCBIManager(cache_dir)
protein_gi = ncbi_manager.search_for_gi(accession, "protein")
blast_rec = ncbi_manager.remote_blast(protein_gi, "blastp")
cons_caculator = BlastConservationCalculator()
data_smoother = SavitzkyGolayDataSmoother(window_size)
cons_dict = cons_caculator.conservation_dict(blast_rec)
indexes = cons_dict.keys()
indexes.sort()
pos_data = []
cons_data = []
for pos in indexes:
pos_data.append(pos + 1)
if len(cons_dict[pos]) > 0:
cons_data.append(numpy.median(cons_dict[pos]))
else:
cons_dict.append(0)
smooth_data = data_smoother.smooth_values(cons_data)
smooth_pos_data = pos_data[data_smoother.half_window():
len(pos_data) - data_smoother.half_window()]
pylab.plot(smooth_pos_data, smooth_data)
pylab.axis(xmin=min(pos_data), xmax=max(pos_data))
pylab.xlabel("Amino acid position")
pylab.ylabel("Conservation")
pylab.savefig('%s_conservation.png' % accession.replace(".", "_"))
class SavitzkyGolayDataSmoother:
"""Smooth data using the Savitzky-Golay technique from:
http://www.dalkescientific.com/writings/NBN/plotting.html
"""
def __init__(self, window_size):
self._window_size = window_size
if self._window_size%2 != 1:
raise TypeError("smoothing requires an odd number of weights")
def half_window(self):
return (self._window_size-1)/2
def smooth_values(self, values):
half_window = (self._window_size-1)/2
weights = self.savitzky_golay_weights(self._window_size)
weights = [w*100.0 for w in weights]
# Precompute the offset values for better performance.
offsets = range(-half_window, half_window+1)
offset_data = zip(offsets, weights)
# normalize the weights in case the sum != 1
total_weight = sum(weights)
weighted_values = []
for i in range(half_window, len(values)-half_window):
weighted_value = 0.0
for offset, weight in offset_data:
weighted_value += weight*values[i+offset]
weighted_values.append(weighted_value / total_weight)
return weighted_values
def savitzky_golay(self, window_size=None, order=2):
if window_size is None:
window_size = order + 2
if window_size % 2 != 1 or window_size < 1:
raise TypeError("window size must be a positive odd number")
if window_size < order + 2:
raise TypeError("window size is too small for the polynomial")
# A second order polynomial has 3 coefficients
order_range = range(order+1)
half_window = (window_size-1)//2
B = numpy.array(
[ [k**i for i in order_range] for k in range(-half_window, half_window+1)] )
# -1
# [ T ] T
# [ B * B ] * B
M = numpy.dot(
numpy.linalg.inv(numpy.dot(numpy.transpose(B), B)),
numpy.transpose(B)
)
return M
def savitzky_golay_weights(self, window_size=None, order=2, derivative=0):
# The weights are in the first row
# The weights for the 1st derivatives are in the second, etc.
return self.savitzky_golay(window_size, order)[derivative]
class BlastConservationCalculator:
"""Calculate conservation across a protein from a BLAST record.
"""
def __init__(self, matrix_name="blosum62"):
"""Initialize with the name of a substitution matrix for comparisons.
"""
self._subs_mat = getattr(MatrixInfo, matrix_name)
self._no_use_thresh = 0.95
def conservation_dict(self, blast_rec):
"""Get dictionary containing substitution scores based on BLAST HSPs.
"""
cons_dict = {}
rec_size = int(blast_rec.query_letters)
for base_index in range(rec_size):
cons_dict[base_index] = []
for align in blast_rec.alignments:
for hsp in align.hsps:
if (float(hsp.identities) / float(rec_size) <=
self._no_use_thresh):
cons_dict = self._add_hsp_conservation(hsp, cons_dict)
return cons_dict
def _add_hsp_conservation(self, hsp, cons_dict):
"""Add conservation information from an HSP BLAST alignment.
"""
start_index = int(hsp.query_start) - 1
hsp_index = 0
for q_index in range(len(hsp.query)):
if (hsp.query[q_index] != '-'):
if (hsp.sbjct[q_index] != '-'):
try:
sub_val = self._subs_mat[(hsp.query[q_index],
hsp.sbjct[q_index])]
except KeyError:
sub_val = self._subs_mat[(hsp.sbjct[q_index],
hsp.query[q_index])]
cons_dict[start_index + hsp_index].append(sub_val)
hsp_index += 1
return cons_dict
class NCBIManager:
"""Manage interactions with NCBI through Biopython
"""
def __init__(self, cache_dir):
self._cache_dir = cache_dir
if not(os.path.exists(cache_dir)):
os.makedirs(cache_dir)
def search_for_gi(self, uniprot_id, db_name):
"""Find the NCBI GI number corresponding to the given input ID.
"""
handle = Entrez.esearch(db=db_name, term=uniprot_id)
record = Entrez.read(handle)
ids = record["IdList"]
if len(ids) == 0:
raise ValueError("Not found in NCBI: %s" % ids)
return ids[0]
def remote_blast(self, search_gi, blast_method):
"""Perform a BLAST against the NCBI server, returning the record.
"""
out_file = os.path.join(self._cache_dir, "%s_%s_blo.xml" % (blast_method,
search_gi))
if not os.path.exists(out_file):
blast_handle = NCBIWWW.qblast(blast_method, "nr", search_gi)
with open(out_file, 'w') as out_handle:
for line in blast_handle:
out_handle.write(line)
blast_handle.close()
with open(out_file) as in_handle:
rec_it = NCBIXML.parse(in_handle)
return rec_it.next()
if __name__ == "__main__":
if len(sys.argv) != 2:
print "Incorrect arguments"
print __doc__
sys.exit()
main(sys.argv[1])