jrjhealey · June 19, 2017 08:16 · luisafmc · Mar 2, 2020 · mashu94 · Mar 3, 2020
diff --git a/Shannon.py b/Shannon.py
 # This script will calculate Shannon entropy from a MSA.

 # Dependencies:

 # Biopython, Matplotlib [optionally], Math

 """
 Shannon's entropy equation (latex format):
    H=-\sum_{i=1}^{M} P_i\,log_2\,P_i
    Entropy is a measure of the uncertainty of a probability distribution (p1, ..... , pM)
    https://stepic.org/lesson/Scoring-Motifs-157/step/7?course=Bioinformatics-Algorithms&unit=436
    Where, Pi is the fraction of nuleotide bases of nuleotide base type i,
    and M is the number of nuleotide base types (A, T, G or C)
    H ranges from 0 (only one base/residue in present at that position) to 4.322 (all 20 residues are equally
    represented in that position).
    Typically, positions with H >2.0 are considerered variable, whereas those with H < 2 are consider conserved.
    Highly conserved positions are those with H <1.0 (Litwin and Jores, 1992).
    A minimum number of sequences is however required (~100) for H to describe the diversity of a protein family.
 """
 import os
 import sys
 import warnings
 import traceback

 __author__ = "Joe R. J. Healey"
 __version__ = "1.0.0"
 __title__ = "ShannonMSA"
 __license__ = "GPLv3"
 __author_email__ = "[email protected]"


 def parseArgs():
    """Parse command line arguments"""

    import argparse

    try:
        parser = argparse.ArgumentParser(
            description='Compute per base/residue Shannon entropy of a Multiple Sequence Alignment.')

        parser.add_argument('-a',
                            '--alignment',
                            action='store',
                            required=True,
                            help='The multiple sequence alignment (MSA) in any of the formats supported by Biopython\'s AlignIO.')
        parser.add_argument('-f',
                            '--alnformat',
                            action='store',
                            default='fasta',
                            help='Specify the format of the input MSA to be passed in to AlignIO.')
        parser.add_argument('-v',
                            '--verbose',
                            action='count',
                            default=0,
                            help='Verbose behaviour, printing parameters of the script.')
        parser.add_argument('-m',
                            '--runningmean',
                            action='store',
                            type=int,
                            default=0,
                            help='Return the running mean (a.k.a moving average) of the MSAs Shannon Entropy. Makes for slightly smoother plots. Providing the number of points to average over switches this on.')
        parser.add_argument('--makeplot',
                            action='store_true',
                            help='Plot the results via Matplotlib.')
    except:
        print "An exception occurred with argument parsing. Check your provided options."
        traceback.print_exc()

    return parser.parse_args()



 def parseMSA(msa, alnformat, verbose):
    """Parse in the MSA file using Biopython's AlignIO"""

    from Bio import AlignIO
    alignment = AlignIO.read(msa, alnformat)

    # Do a little sanity checking:
    seq_lengths_list = []
    for record in alignment:
       seq_lengths_list.append(len(record))

    seq_lengths = set(seq_lengths_list)

    if verbose > 0: print("Alignment length is:" + str(list(seq_lengths)))

    if len(seq_lengths) != 1:
        sys.stderr.write("Your alignment lengths aren't equal. Check your alignment file.")
        sys.exit(1)

    index = range(1, list(seq_lengths)[0]+1)

    return alignment, list(seq_lengths), index

 ##################################################################
 # Function to calcuate the Shannon's entropy per alignment column
 # H=-\sum_{i=1}^{M} P_i\,log_2\,P_i (http://imed.med.ucm.es/Tools/svs_help.html)
 # Gaps and N's are included in the calculation
 ##################################################################

 def shannon_entropy(list_input):
    """Calculate Shannon's Entropy per column of the alignment (H=-\sum_{i=1}^{M} P_i\,log_2\,P_i)"""

    import math
    unique_base = set(list_input)
    M   =  len(list_input)
    entropy_list = []
    # Number of residues in column
    for base in unique_base:
        n_i = list_input.count(base) # Number of residues of type i
        P_i = n_i/float(M) # n_i(Number of residues of type i) / M(Number of residues in column)
        entropy_i = P_i*(math.log(P_i,2))
        entropy_list.append(entropy_i)

    sh_entropy = -(sum(entropy_list))

    return sh_entropy


 def shannon_entropy_list_msa(alignment):
    """Calculate Shannon Entropy across the whole MSA"""

    shannon_entropy_list = []
    for col_no in xrange(len(list(alignment[0]))):
        list_input = list(alignment[:, col_no])
        shannon_entropy_list.append(shannon_entropy(list_input))

    return shannon_entropy_list


 def plot(index, sel, verbose):
    """"Create a quick plot via matplotlib to visualise"""
    import matplotlib.pyplot as plt

    if verbose > 0: print("Plotting data...")

    plt.plot(index, sel)
    plt.xlabel('MSA Position Index', fontsize=16)
    plt.ylabel('Shannon Entropy', fontsize=16)

    plt.show()


 def running_mean(l, N):
    sum = 0
    result = list(0 for x in l)

    for i in range( 0, N ):
        sum = sum + l[i]
        result[i] = sum / (i+1)

    for i in range( N, len(l) ):
        sum = sum - l[i-N] + l[i]
        result[i] = sum / N

    return result

 def main():
    """Compute Shannon Entropy from a provided MSA."""

    # Parse arguments
    args = parseArgs()

    # Convert object elements to standard variables for functions
    msa = args.alignment
    alnformat = args.alnformat
    verbose = args.verbose
    makeplot = args.makeplot
    runningmean = args.runningmean

 # Start calling functions to do the heavy lifting

    alignment, seq_lengths, index = parseMSA(msa, alnformat, verbose)
    sel = shannon_entropy_list_msa(alignment)

    if runningmean > 0:
        sel = running_mean(sel, runningmean)

    if makeplot is True:
        plot(index, sel, verbose)

    if verbose > 0: print("Index" + '\t' + "Entropy")
    for c1, c2 in zip(index, sel):

        print(str(c1) + '\t' + str(c2))



 if __name__ == '__main__':
    main()
	# This script will calculate Shannon entropy from a MSA.

	# Dependencies:

	# Biopython, Matplotlib [optionally], Math

	"""
	Shannon's entropy equation (latex format):
	H=-\sum_{i=1}^{M} P_i\,log_2\,P_i
	Entropy is a measure of the uncertainty of a probability distribution (p1, ..... , pM)
	https://stepic.org/lesson/Scoring-Motifs-157/step/7?course=Bioinformatics-Algorithms&unit=436
	Where, Pi is the fraction of nuleotide bases of nuleotide base type i,
	and M is the number of nuleotide base types (A, T, G or C)
	H ranges from 0 (only one base/residue in present at that position) to 4.322 (all 20 residues are equally
	represented in that position).
	Typically, positions with H >2.0 are considerered variable, whereas those with H < 2 are consider conserved.
	Highly conserved positions are those with H <1.0 (Litwin and Jores, 1992).
	A minimum number of sequences is however required (~100) for H to describe the diversity of a protein family.
	"""
	import os
	import sys
	import warnings
	import traceback

	__author__ = "Joe R. J. Healey"
	__version__ = "1.0.0"
	__title__ = "ShannonMSA"
	__license__ = "GPLv3"
	__author_email__ = "[email protected]"


	def parseArgs():
	"""Parse command line arguments"""

	import argparse

	try:
	parser = argparse.ArgumentParser(
	description='Compute per base/residue Shannon entropy of a Multiple Sequence Alignment.')

	parser.add_argument('-a',
	'--alignment',
	action='store',
	required=True,
	help='The multiple sequence alignment (MSA) in any of the formats supported by Biopython\'s AlignIO.')
	parser.add_argument('-f',
	'--alnformat',
	action='store',
	default='fasta',
	help='Specify the format of the input MSA to be passed in to AlignIO.')
	parser.add_argument('-v',
	'--verbose',
	action='count',
	default=0,
	help='Verbose behaviour, printing parameters of the script.')
	parser.add_argument('-m',
	'--runningmean',
	action='store',
	type=int,
	default=0,
	help='Return the running mean (a.k.a moving average) of the MSAs Shannon Entropy. Makes for slightly smoother plots. Providing the number of points to average over switches this on.')
	parser.add_argument('--makeplot',
	action='store_true',
	help='Plot the results via Matplotlib.')
	except:
	print "An exception occurred with argument parsing. Check your provided options."
	traceback.print_exc()

	return parser.parse_args()



	def parseMSA(msa, alnformat, verbose):
	"""Parse in the MSA file using Biopython's AlignIO"""

	from Bio import AlignIO
	alignment = AlignIO.read(msa, alnformat)

	# Do a little sanity checking:
	seq_lengths_list = []
	for record in alignment:
	seq_lengths_list.append(len(record))

	seq_lengths = set(seq_lengths_list)

	if verbose > 0: print("Alignment length is:" + str(list(seq_lengths)))

	if len(seq_lengths) != 1:
	sys.stderr.write("Your alignment lengths aren't equal. Check your alignment file.")
	sys.exit(1)

	index = range(1, list(seq_lengths)[0]+1)

	return alignment, list(seq_lengths), index

	##################################################################
	# Function to calcuate the Shannon's entropy per alignment column
	# H=-\sum_{i=1}^{M} P_i\,log_2\,P_i (http://imed.med.ucm.es/Tools/svs_help.html)
	# Gaps and N's are included in the calculation
	##################################################################

	def shannon_entropy(list_input):
	"""Calculate Shannon's Entropy per column of the alignment (H=-\sum_{i=1}^{M} P_i\,log_2\,P_i)"""

	import math
	unique_base = set(list_input)
	M = len(list_input)
	entropy_list = []
	# Number of residues in column
	for base in unique_base:
	n_i = list_input.count(base) # Number of residues of type i
	P_i = n_i/float(M) # n_i(Number of residues of type i) / M(Number of residues in column)
	entropy_i = P_i*(math.log(P_i,2))
	entropy_list.append(entropy_i)

	sh_entropy = -(sum(entropy_list))

	return sh_entropy


	def shannon_entropy_list_msa(alignment):
	"""Calculate Shannon Entropy across the whole MSA"""

	shannon_entropy_list = []
	for col_no in xrange(len(list(alignment[0]))):
	list_input = list(alignment[:, col_no])
	shannon_entropy_list.append(shannon_entropy(list_input))

	return shannon_entropy_list


	def plot(index, sel, verbose):
	""""Create a quick plot via matplotlib to visualise"""
	import matplotlib.pyplot as plt

	if verbose > 0: print("Plotting data...")

	plt.plot(index, sel)
	plt.xlabel('MSA Position Index', fontsize=16)
	plt.ylabel('Shannon Entropy', fontsize=16)

	plt.show()


	def running_mean(l, N):
	sum = 0
	result = list(0 for x in l)

	for i in range( 0, N ):
	sum = sum + l[i]
	result[i] = sum / (i+1)

	for i in range( N, len(l) ):
	sum = sum - l[i-N] + l[i]
	result[i] = sum / N

	return result

	def main():
	"""Compute Shannon Entropy from a provided MSA."""

	# Parse arguments
	args = parseArgs()

	# Convert object elements to standard variables for functions
	msa = args.alignment
	alnformat = args.alnformat
	verbose = args.verbose
	makeplot = args.makeplot
	runningmean = args.runningmean

	# Start calling functions to do the heavy lifting

	alignment, seq_lengths, index = parseMSA(msa, alnformat, verbose)
	sel = shannon_entropy_list_msa(alignment)

	if runningmean > 0:
	sel = running_mean(sel, runningmean)

	if makeplot is True:
	plot(index, sel, verbose)

	if verbose > 0: print("Index" + '\t' + "Entropy")
	for c1, c2 in zip(index, sel):

	print(str(c1) + '\t' + str(c2))



	if __name__ == '__main__':
	main()